- awesome so we are now recording um so hello everyone thank you so much
- um so hello everyone thank you so much for coming out tonight um
- for coming out tonight um this is a introductory journalism
- this is a introductory journalism workshop my name is claire mallon and
- workshop my name is claire mallon and i'm the president of de paul's chapter
- i'm the president of de paul's chapter of the society of professional
- of the society of professional journalists and the engagement editor
- journalists and the engagement editor for 14 east magazine helping facilitate
- for 14 east magazine helping facilitate this workshop tonight is grace del
- this workshop tonight is grace del vecchio fort's niece magazine's
- vecchio fort's niece magazine's editor-in-chief and cam rodriguez sports
- editor-in-chief and cam rodriguez sports managing editor
- managing editor and with us today and leading our
- and with us today and leading our introductory data journalism workshop is
- introductory data journalism workshop is ben welsh ben is a los angeles times
- ben welsh ben is a los angeles times data journalist who currently serves as
- data journalist who currently serves as the visiting la times journalist at
- the visiting la times journalist at stanford university where he is
- stanford university where he is expanding where he's leading an
- expanding where he's leading an expansion of the big local news
- expansion of the big local news initiative
- initiative um ben stick with us while i just run
- um ben stick with us while i just run through all your amazing credits he
- through all your amazing credits he co-founded the times first digitally
- co-founded the times first digitally focused projects team and went on to
- focused projects team and went on to lead the modernization of the
- lead the modernization of the newspaper's graphics department his work
- newspaper's graphics department his work includes coverage of live election
- includes coverage of live election results across four presidential cycles
- results across four presidential cycles a mapping platform that's a new standard
- a mapping platform that's a new standard for defining la neighborhoods custom
- for defining la neighborhoods custom designs for dozens of flagship projects
- designs for dozens of flagship projects and the most complete resource on the
- and the most complete resource on the spread of cobit 19 in california
- spread of cobit 19 in california his data-driven reporting has led to
- his data-driven reporting has led to reforms in the la
- reforms in the la fire department's 9-1-1 system a revamp
- fire department's 9-1-1 system a revamp of the broken building inspection
- of the broken building inspection program the replacement of the los
- program the replacement of the los angeles police department's public crime
- angeles police department's public crime map as well as an increased fines
- map as well as an increased fines against exploitive landlords he has won
- against exploitive landlords he has won numerous journalism awards including a
- numerous journalism awards including a pulitzer prize for breaking news
- pulitzer prize for breaking news reporting as part of the la times team
- reporting as part of the la times team that covered the mass shooting in san
- that covered the mass shooting in san bernardino he's a proud depaul alum and
- bernardino he's a proud depaul alum and serves as a fellow for depaul's very own
- serves as a fellow for depaul's very own center for journalism integrity and
- center for journalism integrity and excellence he's a big believer in making
- excellence he's a big believer in making data openly accessible and frequently
- data openly accessible and frequently teaches data journalism and computer
- teaches data journalism and computer programming skills to students and
- programming skills to students and professionals like us tonight
- professionals like us tonight ben we are so lucky to have you here
- ben we are so lucky to have you here today
- today well thank you so much for having me
- well thank you so much for having me yeah of course we are very excited um
- yeah of course we are very excited um i'm going to go ahead and spotlight you
- i'm going to go ahead and spotlight you for everyone
- for everyone you're now in the spotlight um so ben is
- you're now in the spotlight um so ben is going to lead a introductory data
- going to lead a introductory data journalism web workshop specifically on
- journalism web workshop specifically on spreadsheets um
- spreadsheets um ben i'll pass it off to you you can go
- ben i'll pass it off to you you can go ahead and get us started okay great
- ahead and get us started okay great thank you for the introduction claire
- thank you for the introduction claire that was really generous um you know as
- that was really generous um you know as she said i'm a depaul graduate i'm from
- she said i'm a depaul graduate i'm from the class of 2004
- the class of 2004 which is a frightening amount of time
- which is a frightening amount of time has passed since then now that i'm
- has passed since then now that i'm forced to reflect on it but it's really
- forced to reflect on it but it's really great to be back at depaul and teaching
- great to be back at depaul and teaching and helping again if even remotely
- and helping again if even remotely because um
- because um you know without being too corny about
- you know without being too corny about it you know depaul really was a major
- it you know depaul really was a major factor in my career and my life i grew
- factor in my career and my life i grew up in eastern iowa
- up in eastern iowa near cedar rapids and iowa city and
- near cedar rapids and iowa city and moving to chicago at age 17 was it might
- moving to chicago at age 17 was it might as it was like paris france to me and it
- as it was like paris france to me and it was the biggest deal in my life and uh
- was the biggest deal in my life and uh you know other than meeting my wife and
- you know other than meeting my wife and getting married probably the best
- getting married probably the best decision i ever made so being involved
- decision i ever made so being involved with paul continuing involved with it is
- with paul continuing involved with it is really important to me and i'd be happy
- really important to me and i'd be happy to talk about that more but
- to talk about that more but i won't get too sappy on you um it was
- i won't get too sappy on you um it was after paul that i began my journalism
- after paul that i began my journalism experience um working with carol moraine
- experience um working with carol moraine and don mosley when they first took up
- and don mosley when they first took up their residence and it was with them
- their residence and it was with them that
- that i built my first spreadsheet so there
- i built my first spreadsheet so there was a story about a a suburb of chicago
- was a story about a a suburb of chicago that people were telling us maybe
- that people were telling us maybe had some crooked stuff going on with how
- had some crooked stuff going on with how legal bills were being charged against
- legal bills were being charged against the city and uh and don help me file my
- the city and uh and don help me file my first public records request to the city
- first public records request to the city just asking for all the legal fees
- just asking for all the legal fees printed out
- printed out it came back as one of those old school
- it came back as one of those old school printouts which maybe you guys have
- printouts which maybe you guys have never seen where the printer would have
- never seen where the printer would have the little tear strips along the side
- the little tear strips along the side and all the pages would be folded
- and all the pages would be folded together like an accordion
- together like an accordion and it was probably 20 or 30 feet long
- and it was probably 20 or 30 feet long and from this like 1980s printer and it
- and from this like 1980s printer and it had all the legal fees in there but they
- had all the legal fees in there but they were you know hard to read and spread
- were you know hard to read and spread out across this long scroll we kind of
- out across this long scroll we kind of thought well how are we going to get
- thought well how are we going to get from this to our to our story
- from this to our to our story and the answer was my first spreadsheet
- and the answer was my first spreadsheet which was to really just sit there and
- which was to really just sit there and go through it page by page and type the
- go through it page by page and type the numbers in
- numbers in and then add it up so that we could say
- and then add it up so that we could say this is how much money got spent over
- this is how much money got spent over what time and and that became the basis
- what time and and that became the basis for an investigative story that ran on
- for an investigative story that ran on wmaq
- wmaq and uh carol even climbed the ladder and
- and uh carol even climbed the ladder and then let the paper drop like live on
- then let the paper drop like live on camera to show how silly it was and and
- camera to show how silly it was and and that for me was kind of my first hit of
- that for me was kind of my first hit of it and what got me into data journalism
- it and what got me into data journalism was really that by learning some nerdy
- was really that by learning some nerdy stuff one i could kind of find my niche
- stuff one i could kind of find my niche like how i could contribute as someone
- like how i could contribute as someone who wasn't as experienced in other ways
- who wasn't as experienced in other ways and two it was just kind of like a
- and two it was just kind of like a shortcut to doing like really cool
- shortcut to doing like really cool investigative work you know because if
- investigative work you know because if you can learn these data skills
- you can learn these data skills even if it's just like an extra kind of
- even if it's just like an extra kind of power you have and that your main focus
- power you have and that your main focus as a specialist
- as a specialist it's something you can use to to just
- it's something you can use to to just get stories nobody else is getting and
- get stories nobody else is getting and to do stuff that really makes an impact
- to do stuff that really makes an impact that people notice and so um you know i
- that people notice and so um you know i ended up going down this whole road
- ended up going down this whole road where i kind of became a specialist and
- where i kind of became a specialist and so you know gradually becoming a
- so you know gradually becoming a computer programmer and really just
- computer programmer and really just focusing on data data data all day and
- focusing on data data data all day and you could go down that road if you're
- you could go down that road if you're interested and i'd be happy to talk to
- interested and i'd be happy to talk to you about it but really a lot of the
- you about it but really a lot of the most effective data journalists really
- most effective data journalists really just have it as like an extra tool in
- just have it as like an extra tool in their toolkit and they you know see
- their toolkit and they you know see themselves more as traditional
- themselves more as traditional journalists but they know enough about
- journalists but they know enough about data to pull off a story now and then
- data to pull off a story now and then and those are people often have some of
- and those are people often have some of the biggest impact and so i think
- the biggest impact and so i think you don't have to look at these skills
- you don't have to look at these skills as something that have to consume your
- as something that have to consume your whole career or life unless you really
- whole career or life unless you really want them to
- want them to so
- so um so the training i'm going to give
- um so the training i'm going to give tonight is is really just like
- tonight is is really just like spreadsheets 101 it's a really basic
- spreadsheets 101 it's a really basic introduction to like what is a
- introduction to like what is a spreadsheet and how do you use it and
- spreadsheet and how do you use it and then how to use it to kind of analyze
- then how to use it to kind of analyze data to ask and answer questions to
- data to ask and answer questions to interview it and so we're going to start
- interview it and so we're going to start with sort of some phony data that we
- with sort of some phony data that we make up just to like keep things simple
- make up just to like keep things simple and we're going to cover some of the
- and we're going to cover some of the fundamentals of working with a
- fundamentals of working with a spreadsheet and then after we get some
- spreadsheet and then after we get some practice we're going to introduce a real
- practice we're going to introduce a real data set from the actual news in chicago
- data set from the actual news in chicago and and try to ask it some questions try
- and and try to ask it some questions try to interview it like we would another
- to interview it like we would another source and that data set is the
- source and that data set is the complaints against chicago police which
- complaints against chicago police which is a database that was sort of
- is a database that was sort of crowbarred out of the government a few
- crowbarred out of the government a few years ago by the invisible institute i
- years ago by the invisible institute i bet you guys have seen it in the news
- bet you guys have seen it in the news from time to time
- from time to time okay so that's kind of the plan
- okay so that's kind of the plan all right that's that's the pre-spiel
- all right that's that's the pre-spiel for the actual spiel
- for the actual spiel um before we begin does anybody have any
- um before we begin does anybody have any questions
- questions or things they
- or things they want to ask i'm happy to answer anything
- and if no one has any questions now if you have questions throughout the
- you have questions throughout the workshop uh cam grace and i will be
- workshop uh cam grace and i will be answering questions in the chat so you
- answering questions in the chat so you can just shoot them there and we'll try
- can just shoot them there and we'll try and help you troubleshoot and if not
- and help you troubleshoot and if not we'll bring them to ben's attention
- we'll bring them to ben's attention looks like ava has a question oh what's
- looks like ava has a question oh what's your question
- your question yeah um i was just wondering i know
- yeah um i was just wondering i know excel is like the most common um
- excel is like the most common um platform for working with spreadsheets
- platform for working with spreadsheets but are there any other any other
- but are there any other any other types of programs or software that data
- types of programs or software that data journalists typically use
- journalists typically use yeah sure so when a you know spreadsheet
- yeah sure so when a you know spreadsheet is really kind of a generic term you
- is really kind of a generic term you know it's like soda
- know it's like soda and excel is like coca-cola right you
- and excel is like coca-cola right you know what i mean it's kind of the brand
- know what i mean it's kind of the brand name of the biggest product but there's
- name of the biggest product but there's other sodas on the market so to speak
- other sodas on the market so to speak when it comes to spreadsheets in fact
- when it comes to spreadsheets in fact the original spreadsheet was called
- the original spreadsheet was called fizzy calc if you've heard of that from
- fizzy calc if you've heard of that from the 1980s
- the 1980s um
- um and you know there's one that comes on
- and you know there's one that comes on apple computers called numbers
- apple computers called numbers um you know i'm a super nerd so i use
- um you know i'm a super nerd so i use linux and there's like an open source
- linux and there's like an open source one that's free
- one that's free and then we're actually in this class
- and then we're actually in this class going to use google sheets which is sort
- going to use google sheets which is sort of like google's competitor to excel
- of like google's competitor to excel it's available in a web browser and it's
- it's available in a web browser and it's free which is why we're teaching it in
- free which is why we're teaching it in this class
- this class i think probably
- i think probably if you were to compare them all excel
- if you were to compare them all excel probably is the best because
- probably is the best because it just can handle data sets that are a
- it just can handle data sets that are a little larger
- little larger and it has a lot of features and it's
- and it has a lot of features and it's really well tested but when it comes to
- really well tested but when it comes to like the basic features that we're going
- like the basic features that we're going to use for this uh class and which most
- to use for this uh class and which most journalists use they all have the basic
- journalists use they all have the basic features and you can really use all of
- features and you can really use all of them to do it and the stuff we cover in
- them to do it and the stuff we cover in google sheets everything we cover is in
- google sheets everything we cover is in excel it's just like the button is going
- excel it's just like the button is going to be in a slightly different place or
- to be in a slightly different place or they might have like a different nerdy
- they might have like a different nerdy name for some feature or something right
- name for some feature or something right so spreadsheets it's like you know we're
- so spreadsheets it's like you know we're going to do use basketball as our
- going to do use basketball as our example and so to me spreadsheets and
- example and so to me spreadsheets and data journalism is like tripling you
- data journalism is like tripling you know it's just like the fundamental
- know it's just like the fundamental skill of what is data how do you
- skill of what is data how do you understand it how do you how do you
- understand it how do you how do you structure it manipulate it and analyze
- structure it manipulate it and analyze it so that's where it all begins and so
- it so that's where it all begins and so i really think that it's the fundamental
- i really think that it's the fundamental tool however as you start working with
- tool however as you start working with larger data sets that have more and more
- larger data sets that have more and more records or you have a database that has
- records or you have a database that has many tables that need to be joined
- many tables that need to be joined together kind of in a complex system
- together kind of in a complex system or if you're going to begin to like um
- or if you're going to begin to like um automate things to routinely gather data
- automate things to routinely gather data from the internet or other things like
- from the internet or other things like that that's where you begin to introduce
- that that's where you begin to introduce tools that go beyond the spreadsheet you
- tools that go beyond the spreadsheet you know and so those include like big
- know and so those include like big relational database software they
- relational database software they include computer programming languages
- include computer programming languages that are built for statistical analysis
- that are built for statistical analysis you know maybe you've heard of r or
- you know maybe you've heard of r or python right and you know a lot of
- python right and you know a lot of people sometimes start learning those
- people sometimes start learning those skills i think that that's fine it's
- skills i think that that's fine it's fine to start with those but i really do
- fine to start with those but i really do think that the spreadsheet is the is the
- think that the spreadsheet is the is the basic building block and i think quite a
- basic building block and i think quite a lot of data journalism can and should be
- lot of data journalism can and should be done in the spreadsheet and doesn't
- done in the spreadsheet and doesn't require the bigger tools
- require the bigger tools did that answer your question
- did that answer your question yes it did thank you
- yes it did thank you [Music]
- excuse me i have a little bit of a cough i have
- i have flu like two weeks ago not covered but i
- flu like two weeks ago not covered but i am still coughing and so that's going to
- am still coughing and so that's going to happen tonight and i just apologize in
- happen tonight and i just apologize in advance it's just a sign of what a
- advance it's just a sign of what a weekly i am okay that's still carrying
- weekly i am okay that's still carrying this around
- this around all right so if there's no other
- all right so if there's no other questions i'm just going to dive in
- questions i'm just going to dive in which is going to start with sharing my
- which is going to start with sharing my screen so bear with me here
- all right can you guys see my screen yes
- yes okay so this is a blank google sheet
- okay so this is a blank google sheet this is like the very beginning of a
- this is like the very beginning of a spreadsheet within google sheets but let
- spreadsheet within google sheets but let me show you how to get there if you
- me show you how to get there if you haven't been there before
- haven't been there before the starting place i'm going to go back
- the starting place i'm going to go back to a tab and it's just in google drive
- to a tab and it's just in google drive so that's
- so that's drive.google.com
- drive.google.com right which is
- right which is this is kind of like the microsoft
- this is kind of like the microsoft office of google i mean you guys are
- office of google i mean you guys are college students i bet you've all seen
- college students i bet you've all seen this before right so this will require
- this before right so this will require that you have a google account i think
- that you have a google account i think and that you log into google so i would
- and that you log into google so i would just ask everybody to start off
- just ask everybody to start off by going to drive.google.com
- by going to drive.google.com and kind of getting to this landing page
- and kind of getting to this landing page which is all your like folders right
- which is all your like folders right and you can see i've got some stuff in
- and you can see i've got some stuff in my life and then some things i've been
- my life and then some things i've been monkeying today
- monkeying today okay
- okay i'm going to give everybody you know a
- i'm going to give everybody you know a couple seconds just to get there because
- couple seconds just to get there because you got to log into your google account
- you got to log into your google account etc
- etc excuse me
- all right if you're having trouble again just feel free to raise the hand button
- just feel free to raise the hand button and one of the other hosts can help you
- and one of the other hosts can help you along and if i'm going too fast or too
- along and if i'm going too fast or too slow please speak up but i'm going to
- slow please speak up but i'm going to begin
- begin the first step is just to create a new
- the first step is just to create a new blank spreadsheet you know a canvas for
- blank spreadsheet you know a canvas for you to begin to create and you do that
- you to begin to create and you do that by clicking the new button in the upper
- by clicking the new button in the upper left hand corner
- left hand corner and then there's this pull down which
- and then there's this pull down which has the different types of things that
- has the different types of things that google drive can do google docs that
- google drive can do google docs that you've all seen they have a powerpoint
- you've all seen they have a powerpoint thing
- thing a little form doodly deal right but
- a little form doodly deal right but right there in the middle is google
- right there in the middle is google sheets sheets being short for
- sheets sheets being short for spreadsheets so i click that
- spreadsheets so i click that and boom
- and boom i've got my new blank spreadsheet which
- i've got my new blank spreadsheet which is oftentimes called a workbook in the
- is oftentimes called a workbook in the language of spreadsheet nerds right
- language of spreadsheet nerds right and it starts off blank we're just going
- and it starts off blank we're just going to start from scratch so we can cover
- to start from scratch so we can cover some of the fundamentals with some phony
- some of the fundamentals with some phony data so the first thing i'm going to do
- data so the first thing i'm going to do is i'm going to go and i'm going to name
- is i'm going to go and i'm going to name my spreadsheet so i'm going to click in
- my spreadsheet so i'm going to click in the upper left hand corner on that
- the upper left hand corner on that untitled spreadsheet i'm going to give
- untitled spreadsheet i'm going to give it a name
- it a name and uh you know forgive me for the
- and uh you know forgive me for the sports stuff but we're going to call
- sports stuff but we're going to call this uh you know our dream team because
- this uh you know our dream team because our what we're going to do in our first
- our what we're going to do in our first spreadsheet is we're going to create our
- spreadsheet is we're going to create our dream team of basketball players our
- dream team of basketball players our favorite basketball players that we all
- favorite basketball players that we all love
- love and then you know start a starting
- and then you know start a starting basketball team has five people in it
- basketball team has five people in it right
- right and so in a spreadsheet you know each
- and so in a spreadsheet you know each row tends to be a record so one two
- row tends to be a record so one two three four five so we're gonna fill in
- three four five so we're gonna fill in five rows one for each player
- five rows one for each player and each column or field
- and each column or field tends to be
- tends to be uh an attribute or a piece of metadata
- uh an attribute or a piece of metadata about the person it's their name it's
- about the person it's their name it's their birthday it's their salary these
- their birthday it's their salary these are the things we'll fill in and that
- are the things we'll fill in and that sort of grid or structure is what
- sort of grid or structure is what makes it a spreadsheet
- makes it a spreadsheet and so
- and so the first thing that really comes with
- the first thing that really comes with any spreadsheet is defining what your
- any spreadsheet is defining what your columns are going to be what's the data
- columns are going to be what's the data that you're going to track within each
- that you're going to track within each row
- row and so my goal is to make my basketball
- and so my goal is to make my basketball dream team and so i'm going to the first
- dream team and so i'm going to the first column is going to be the name of the
- column is going to be the name of the player right so i'm just going to click
- player right so i'm just going to click in right there
- in right there a1 see it's a is the column one is the
- a1 see it's a is the column one is the row it's just like battleship
- row it's just like battleship right
- right a
- a one that's my first column in my first
- one that's my first column in my first row and i'm gonna say name this is these
- row and i'm gonna say name this is these are work this is what's called the
- are work this is what's called the header
- header right it's the kind of first row the
- right it's the kind of first row the front of the sheet
- front of the sheet and then each player has a position that
- and then each player has a position that they play right like in basketball where
- they play right like in basketball where do they play on the court so my next
- do they play on the court so my next column on the type position you guys do
- column on the type position you guys do this too play along
- this too play along okay
- okay name position
- name position and then since we want to do a little
- and then since we want to do a little math as part of this example we want to
- math as part of this example we want to get into
- get into how we can calculate stuff once we have
- how we can calculate stuff once we have data put together we're going to have a
- data put together we're going to have a column that is
- column that is current salary is what i'm going to call
- current salary is what i'm going to call so this is what they get paid right now
- so this is what they get paid right now and then we're going to make a kind of
- and then we're going to make a kind of fun fantasy column which is what we're
- fun fantasy column which is what we're going to pay them from like our big
- going to pay them from like our big budget right and so we're going to say
- budget right and so we're going to say like new salary
- like new salary right so just by clicking in
- right so just by clicking in typing hitting enter i've been able to
- typing hitting enter i've been able to put in these header fields
- put in these header fields into the first row and then each each
- into the first row and then each each row beneath it will have the values for
- row beneath it will have the values for that record
- i jumped in a little bit ahead of here there's some other stuff around that
- there's some other stuff around that might be worth going over you see that
- might be worth going over you see that just like in microsoft word or any uh
- just like in microsoft word or any uh document editing thing you've used
- document editing thing you've used there's kind of formatting ribbon of
- there's kind of formatting ribbon of options up here which allow you to sort
- options up here which allow you to sort of like style and muck around with the
- of like style and muck around with the stuff that you put in so i would just
- stuff that you put in so i would just say let's everybody just click on a1
- say let's everybody just click on a1 hold down and drag to the right you see
- hold down and drag to the right you see how i did that and i've now selected
- how i did that and i've now selected four cells
- four cells i'm gonna do that then i'm just gonna
- i'm gonna do that then i'm just gonna hit that b
- hit that b right there on the ribbon and just like
- right there on the ribbon and just like in microsoft word or google docs it's
- in microsoft word or google docs it's gonna bold it right and so now we've
- gonna bold it right and so now we've bolded the first row of our data to make
- bolded the first row of our data to make it clear these are the headers
- it clear these are the headers right and there's lots of other options
- right and there's lots of other options here which allow you to do background
- here which allow you to do background colors italics and change the font
- colors italics and change the font when it comes to numbers there's some
- when it comes to numbers there's some important ways to format that we'll get
- important ways to format that we'll get into and just a lot of stuff so if you
- into and just a lot of stuff so if you look around up here there's a lot of
- look around up here there's a lot of different options ultimately you
- different options ultimately you probably only need four or five of them
- probably only need four or five of them to kind of do what we need to do but
- to kind of do what we need to do but there's a bunch of stuff
- some of those things will of course be different in excel versus in google
- different in excel versus in google sheets um on mac versus pc and so sort
- sheets um on mac versus pc and so sort of if you switch from one spreadsheet
- of if you switch from one spreadsheet program to another all the same buttons
- program to another all the same buttons are kind of up there they're just going
- are kind of up there they're just going to look a little different and be in
- to look a little different and be in different places
- different places and kind of just being patient about it
- and kind of just being patient about it is and finding what you're after is kind
- is and finding what you're after is kind of what's necessary
- of what's necessary there's some so that's stuff that you
- there's some so that's stuff that you see in documents but there's other
- see in documents but there's other formatting things that are specific to
- formatting things that are specific to spreadsheets so for instance our columns
- spreadsheets so for instance our columns our columns and rows can be resized
- our columns and rows can be resized right so you see here they each have the
- right so you see here they each have the standard size but if i hover
- standard size but if i hover over column d i can make it wider i can
- over column d i can make it wider i can make it narrower
- make it narrower you can do that for the rows make them
- you can do that for the rows make them taller or shorter
- taller or shorter right there's even a trick where if you
- right there's even a trick where if you click in the upper left and that blank
- click in the upper left and that blank box it selects the whole spreadsheet
- box it selects the whole spreadsheet we'll use that later like that's a
- we'll use that later like that's a little trick
- little trick if you do that and then
- if you do that and then double click on the headers it auto
- double click on the headers it auto sizes them to fit the data did you see
- sizes them to fit the data did you see how that happened
- how that happened there's a million little tricks like
- there's a million little tricks like that you don't need to know them all but
- that you don't need to know them all but there's a lot underneath the hood here
- there's a lot underneath the hood here that's really powerful that you'll start
- that's really powerful that you'll start to learn more and more of as you get
- to learn more and more of as you get more practice
- more practice all right
- um one little trick is that you can make the header row kind of special by
- the header row kind of special by grabbing the bottom of that upper
- grabbing the bottom of that upper upper left corner box which doesn't have
- upper left corner box which doesn't have a name as far as i know the magic box
- a name as far as i know the magic box i'll call it
- i'll call it i'm going to grab that top one and pull
- i'm going to grab that top one and pull it down and you can see that now it's
- it down and you can see that now it's kind of put my header
- kind of put my header right with a little line below it to
- right with a little line below it to break it out and after i scroll now you
- break it out and after i scroll now you can see how there's row two it sort of
- can see how there's row two it sort of disappears and then comes back that's
- disappears and then comes back that's gonna freeze that first row so if you
- gonna freeze that first row so if you add more data and scroll it'll stay at
- add more data and scroll it'll stay at the top that's just one nice little
- the top that's just one nice little trick not essential but nice to know
- trick not essential but nice to know okay everybody get that far
- okay great so now we're going to fill this in we need five basketball players
- this in we need five basketball players i need people to shout it out from the
- i need people to shout it out from the audience who do you like who do you want
- audience who do you like who do you want on your dream team
- steph curry steph curry
- steph curry he's healthy again not a bad pick
- he's healthy again not a bad pick okay he plays
- what he's a guard it's okay it's okay
- it's okay it's okay he's a little fast guy
- he's a little fast guy all right somebody else who's on your
- all right somebody else who's on your dream team
- dream team we can throw in lebron to make it
- we can throw in lebron to make it interesting lebron james this is how
- interesting lebron james this is how they do the nba all-star game you know
- i'd call him a guard but that's debatable
- debatable jason tatum
- jason tatum who's that defense
- who's that defense jason tatum is that's what the y
- jason tatum is that's what the y yes okay we're going to say he's playing
- yes okay we're going to say he's playing forward i don't know if that's true yes
- forward i don't know if that's true yes he is a forward
- he is a forward okay
- okay celtic's defense last night was looking
- celtic's defense last night was looking good in the second half
- good in the second half matisse diable
- matisse diable how do you spell that
- how do you spell that t
- t i
- i s s e
- s s e and then i think his last name is
- and then i think his last name is t h y
- t h y b
- b u l l e he's a guard forward for the six
- u l l e he's a guard forward for the six series okay and then i'm picking last
- series okay and then i'm picking last because this is my spreadsheet my center
- because this is my spreadsheet my center is britney grinder guys come on
- is britney grinder guys come on [Laughter]
- [Laughter] we're bringing her home
- we're bringing her home okay
- okay so we got our starting five
- so we got our starting five and you can see here it's one row per
- and you can see here it's one row per person right and whenever you get a
- person right and whenever you get a spreadsheet from somebody else you make
- spreadsheet from somebody else you make one it's always good to think about what
- one it's always good to think about what is a row like is a row a person is it a
- is a row like is a row a person is it a transaction from like your credit card
- transaction from like your credit card statement
- statement is it um an incident that's been
- is it um an incident that's been recorded by a government agency is it a
- recorded by a government agency is it a thing in the world just like what is the
- thing in the world just like what is the row and we'll see this later with the
- row and we'll see this later with the police data because there can be sort of
- police data because there can be sort of interesting nuances to how you interpret
- interesting nuances to how you interpret the data based on what the row kind of
- the data based on what the row kind of is right and so in our case
- is right and so in our case the row is a person or a player right
- the row is a person or a player right okay and so we've got five rows but it's
- okay and so we've got five rows but it's actually six rows right because it's a
- actually six rows right because it's a header but there's five records
- header but there's five records and then we've got two columns we filled
- and then we've got two columns we filled out name
- out name and position
- and position right
- right and then the one i'm going to get is
- and then the one i'm going to get is their current salary so to do this i'm
- their current salary so to do this i'm going to go to basketballreference.com
- going to go to basketballreference.com which is just like a nerdy website that
- which is just like a nerdy website that has data about players and i'm going to
- has data about players and i'm going to get their salary so steph curry's most
- get their salary so steph curry's most recent salary guys buckle up is 43
- recent salary guys buckle up is 43 million dollars
- million dollars geez
- geez right so i'm gonna punch that in go
- right so i'm gonna punch that in go ahead and punch it in yourself you don't
- ahead and punch it in yourself you don't the numbers we put in here don't really
- the numbers we put in here don't really matter you can kind of fudge them you
- matter you can kind of fudge them you know let me make this a little bigger
- know let me make this a little bigger too while we're doing it but i'm going
- too while we're doing it but i'm going to punch it in there and you notice that
- to punch it in there and you notice that i don't put in any commas or the dollar
- i don't put in any commas or the dollar signs because that stuff's for humans
- signs because that stuff's for humans that stuff's not for computers the
- that stuff's not for computers the computer just wants the number right we
- computer just wants the number right we can format the number to look however we
- can format the number to look however we want later and i'll show you that in a
- want later and i'll show you that in a minute but oftentimes when you're
- minute but oftentimes when you're punching in numbers into the into the
- punching in numbers into the into the computer especially when you're copying
- computer especially when you're copying pasting like i did if you include things
- pasting like i did if you include things like dollar signs or commas the computer
- like dollar signs or commas the computer can get confused and think it's a word
- can get confused and think it's a word and not a number right because the
- and not a number right because the computer always has to guess is this a
- computer always has to guess is this a number or a word or
- number or a word or um
- um or what and that affects how it gets
- or what and that affects how it gets interpreted when you begin doing
- interpreted when you begin doing mathematical formulas and trying to uh
- mathematical formulas and trying to uh analyze it that's called the data type
- analyze it that's called the data type and so what type of information is in
- and so what type of information is in the cell and you know characters versus
- the cell and you know characters versus numbers and then dates those three are
- numbers and then dates those three are kind of the most common data types and
- kind of the most common data types and making sure that the spreadsheet is kind
- making sure that the spreadsheet is kind of kosher and happy with them is part of
- of kosher and happy with them is part of you know preparing your data for
- you know preparing your data for analysis so i put the number in there
- analysis so i put the number in there with no commas or dollar signs for now
- with no commas or dollar signs for now all right we're gonna go look up lebron
- all right we're gonna go look up lebron next
- salary a little less making a little less than
- a little less making a little less than step
- step 39 million
- 39 million i'm again just going to pull out those
- i'm again just going to pull out those other things oops i put it in the see
- other things oops i put it in the see what i did
- what i did i made a mistake i put it over steps
- i made a mistake i put it over steps number
- number so what i can do is i'm going to copy it
- so what i can do is i'm going to copy it to make sure i got it but then if i do
- to make sure i got it but then if i do edit undo i get steps numbered back
- edit undo i get steps numbered back right and the reason it went into that
- right and the reason it went into that other one is because the blue selector
- other one is because the blue selector box was around c2 i need to click and
- box was around c2 i need to click and put it on c3 and then paste it in and
- put it on c3 and then paste it in and hit enter which moves me down a row
- hit enter which moves me down a row and that's how i get lebron's number
- and that's how i get lebron's number incorrectly all right
- incorrectly all right let's do jason tatum i like jason tatum
- let's do jason tatum i like jason tatum my favorite celtic is marcus smart
- my favorite celtic is marcus smart though
- though he's wild
- he's wild crazy
- crazy all right we're going to go get the
- all right we're going to go get the salary
- salary is it in here
- is it in here there it is only 9 million
- i said that right i'm just going to search for him copy and paste it oh
- search for him copy and paste it oh that bit my
- that bit my my like mouse wheel went crazy
- my like mouse wheel went crazy okay there we go
- paste that in and then i checked this earlier spoiler
- and then i checked this earlier spoiler they don't have women's basketball
- they don't have women's basketball salaries on the site so i'm just going
- salaries on the site so i'm just going to google it real quick
- to google it real quick and you maybe want to vet this a little
- and you maybe want to vet this a little more but i'm just going to take this
- more but i'm just going to take this first site here and go with it and
- first site here and go with it and brittany griner what a rip off only
- brittany griner what a rip off only making two hundred thousand dollars
- making two hundred thousand dollars so we're going to put her in there last
- so we're going to put her in there last and we've got those numbers in so i'd
- and we've got those numbers in so i'd encourage everybody to just punch in
- encourage everybody to just punch in these numbers or really any numbers you
- these numbers or really any numbers you want because we're just going to teach
- want because we're just going to teach some principles of how to do math it
- some principles of how to do math it doesn't really matter what the numbers
- doesn't really matter what the numbers are for what we're about to learn
- are for what we're about to learn you can see that kind of gathering that
- you can see that kind of gathering that data was a key to begin creating
- data was a key to begin creating something we can analyze and oftentimes
- something we can analyze and oftentimes a lot of the best data journalism
- a lot of the best data journalism stories come from data that you create
- stories come from data that you create that there's kind of unstructured
- that there's kind of unstructured information in the world or sprinkled
- information in the world or sprinkled around the internet or hidden in the
- around the internet or hidden in the government office and you plug it into a
- government office and you plug it into a spreadsheet so you can get it to a place
- spreadsheet so you can get it to a place where you can analyze it
- where you can analyze it and that's where you're often doing work
- and that's where you're often doing work that no one else has done before or done
- that no one else has done before or done quite that way that's how i got that
- quite that way that's how i got that story way back when
- story way back when um everybody good any questions
- i think we're good all right so there's one column we're missing which is the
- one column we're missing which is the new salary so which is what we're gonna
- new salary so which is what we're gonna pay him all right so i'm just gonna
- pay him all right so i'm just gonna throw it out to the crowd what should we
- throw it out to the crowd what should we pay steph curry somebody shout out a
- pay steph curry somebody shout out a number
- 30 million 30 million he makes it though
- okay lebron what's he getting
- 25 million 25 million okay
- 25 million okay all right steph's still top dog all
- all right steph's still top dog all right jason tatum
- 100 million 100 million
- 20 million 20 million
- you got to get britney at least that much yeah i don't know give her give her
- much yeah i don't know give her give her 50 mil give her give her 50 billion give
- 50 mil give her give her 50 billion give her
- her i mean when she gets home she deserves
- i mean when she gets home she deserves something i'll tell you that yeah
- okay so we've just got to punch some numbers
- so we've just got to punch some numbers in you can punch in different ones you
- in you can punch in different ones you want again it's not a big deal but now
- want again it's not a big deal but now we've kind of got a complete data set
- we've kind of got a complete data set that's ready for a little bit of
- that's ready for a little bit of analysis and we can cover some fun
- analysis and we can cover some fun spreadsheet tricks um one thing here's
- spreadsheet tricks um one thing here's something i teased earlier is you know i
- something i teased earlier is you know i told you to punch in these numbers
- told you to punch in these numbers without commas or dollar signs so the
- without commas or dollar signs so the computer would be happy but that makes
- computer would be happy but that makes them kind of hard to read so what if you
- them kind of hard to read so what if you want to make it easy for you to read but
- want to make it easy for you to read but still be able to analyze it right the
- still be able to analyze it right the way you do that is you say select the
- way you do that is you say select the column so if i click on the c up at the
- column so if i click on the c up at the top that selects all of column c so do
- top that selects all of column c so do that
- that and then there's a format menu here at
- and then there's a format menu here at the top
- the top you go and click on format and then
- you go and click on format and then there's a
- there's a option and you can see here a lot of
- option and you can see here a lot of different preset ways that google sheets
- different preset ways that google sheets is able to format a number depending on
- is able to format a number depending on what kind of thing it is right should it
- what kind of thing it is right should it have a decimal place is it a percent
- have a decimal place is it a percent that should be taken by a hundred maybe
- that should be taken by a hundred maybe you want scientific notation i sure as
- you want scientific notation i sure as hell don't but maybe you do
- hell don't but maybe you do um
- um do you want it rounded or not you know
- do you want it rounded or not you know is this a date at not a number like all
- is this a date at not a number like all these different things can get sorted
- these different things can get sorted out with the format menu so in our case
- out with the format menu so in our case i think we want currency rounded right
- i think we want currency rounded right we wanted to have a dollar we want the
- we wanted to have a dollar we want the commas after every three
- commas after every three values and we don't really need the
- values and we don't really need the decimal places right
- decimal places right so i'm going to select currency rounded
- so i'm going to select currency rounded you can pick whatever you'd like it's up
- you can pick whatever you'd like it's up to you but you see i that that magically
- to you but you see i that that magically put in
- put in the commas and the dollar sign on the
- the commas and the dollar sign on the front then but if i click on one of the
- front then but if i click on one of the values and i look into the
- values and i look into the editor here at the top you see that
- editor here at the top you see that there aren't actually any commas in it
- there aren't actually any commas in it right it's because what's stored in the
- right it's because what's stored in the data behind the scenes with sheets is
- data behind the scenes with sheets is just the number but then the formatting
- just the number but then the formatting for us to look at is sort of like icing
- for us to look at is sort of like icing on the cake right
- on the cake right and i'm going to do the same thing for
- and i'm going to do the same thing for salary i'm going to click on d
- salary i'm going to click on d and there's even a little shortcut do
- and there's even a little shortcut do you see this little dollar sign here
- you see this little dollar sign here in the uh ribbon
- in the uh ribbon that will do it for you but you see that
- that will do it for you but you see that actually puts those decimal places in
- actually puts those decimal places in there which i don't want but there's
- there which i don't want but there's another shortcut for that right here
- another shortcut for that right here this button will decrease the decimal
- this button will decrease the decimal places
- places so i hit that twice and boom i've got
- so i hit that twice and boom i've got some nice numbers
- okay now if you were in a different country
- now if you were in a different country sheets would be smart enough to maybe
- sheets would be smart enough to maybe put a different currency value in front
- put a different currency value in front or use the commas in a different way
- or use the commas in a different way because these are
- because these are as i suspect you guys know but just say
- as i suspect you guys know but just say these are you know american or english
- these are you know american or english conventions that aren't shared by
- conventions that aren't shared by everyone in the world and so computer
- everyone in the world and so computer software often has to internationalize
- software often has to internationalize itself to the current locales
- itself to the current locales okay
- okay everybody get that in
- okay so now we've got our starting data set it's what we're here but we want to
- set it's what we're here but we want to analyze it we want to ask some questions
- analyze it we want to ask some questions and answer them
- and answer them numerically
- numerically um using the spreadsheet it'll work for
- um using the spreadsheet it'll work for our five records but also what's great
- our five records but also what's great about spreadsheets is it would work for
- about spreadsheets is it would work for 500 million records if you had enough
- 500 million records if you had enough these tricks that we learn on this
- these tricks that we learn on this little small data set can be easily
- little small data set can be easily applied to lots and lots of data once we
- applied to lots and lots of data once we learn how to do them right so i'm going
- learn how to do them right so i'm going to add a new column which is going to be
- to add a new column which is going to be my new calculated thing
- my new calculated thing that i'm going to pull out of thin air
- that i'm going to pull out of thin air using excel and it's going to be change
- using excel and it's going to be change because i want to know the change in
- because i want to know the change in salary for each of the five players from
- salary for each of the five players from their current salary to what we're going
- their current salary to what we're going to pay them right so i just type in
- to pay them right so i just type in change i hit b
- change i hit b to control b to bold it you know hit
- to control b to bold it you know hit that b right there
- that b right there and then i click down to e2 so
- and then i click down to e2 so here's a really tough math question guys
- here's a really tough math question guys how would you calculate the difference
- how would you calculate the difference between someone's new salary and their
- between someone's new salary and their current salary
- subtraction subtraction yeah right and so really you take the new
- right and so really you take the new salary you then subtract from it the
- salary you then subtract from it the current salary and the difference would
- current salary and the difference would be the change right
- be the change right and so that could be 30 million
- and so that could be 30 million uh minus 43 million in the case of steph
- uh minus 43 million in the case of steph curry right and in the case of lebron
- curry right and in the case of lebron james it's 25 million minus 39
- james it's 25 million minus 39 right so you could just type in these
- right so you could just type in these numbers into a calculator and then type
- numbers into a calculator and then type the results into the spreadsheet but
- the results into the spreadsheet but that would be a lot of work we can have
- that would be a lot of work we can have the spreadsheet automatically calculated
- the spreadsheet automatically calculated by substituting the battleship positions
- by substituting the battleship positions of the cells in the spreadsheet
- of the cells in the spreadsheet into a mathematical formula and you do
- into a mathematical formula and you do that
- that by clicking into the cell where you want
- by clicking into the cell where you want to work so i'm going to click on e2 and
- to work so i'm going to click on e2 and then you start off your cell with an
- then you start off your cell with an equal sign if you just type equal you
- equal sign if you just type equal you can then write a mathematical equation
- can then write a mathematical equation and the spreadsheet will magically do it
- and the spreadsheet will magically do it for you you can see here that google's
- for you you can see here that google's even suggesting one for me d2 minus c2
- even suggesting one for me d2 minus c2 right it read my mind guys the ai is
- right it read my mind guys the ai is here
- here and like that is exactly what we want to
- and like that is exactly what we want to do we want to take the cell
- do we want to take the cell d2 which is steph curry's 30 million
- d2 which is steph curry's 30 million dollar salary and we want to subtract
- dollar salary and we want to subtract from it cell c2 which is steph curry's
- from it cell c2 which is steph curry's current salary so you just do equal d 2
- current salary so you just do equal d 2 minus c
- minus c 2 right
- 2 right hit enter
- hit enter boom
- boom the result steph cam steph curry is
- the result steph cam steph curry is getting his pay cut by 13 million bucks
- getting his pay cut by 13 million bucks maybe he did it voluntarily he wants to
- maybe he did it voluntarily he wants to play with these guys you know
- play with these guys you know um
- um and you can see that that's
- and you can see that that's automatically been done and then whoa
- automatically been done and then whoa check out this like pop-up on my screen
- check out this like pop-up on my screen autofill
- autofill right google sheets is already saying
- right google sheets is already saying well you've done this uh formula for the
- well you've done this uh formula for the first cell you want to roll it down
- first cell you want to roll it down across the subsequent cells and i'm like
- across the subsequent cells and i'm like oh hell yes and so i click that little
- oh hell yes and so i click that little plus button there to auto fill and boom
- plus button there to auto fill and boom you can see that the change in salaries
- you can see that the change in salaries has been automatically calculated for
- has been automatically calculated for each row in the spreadsheet here it did
- each row in the spreadsheet here it did it for five but if we had everybody in
- it for five but if we had everybody in the mba it would do it for ever for
- the mba it would do it for ever for every row in the data set all the way to
- every row in the data set all the way to the bottom right so
- the bottom right so and you can see if i click into that
- and you can see if i click into that second cell for lebron instead of d2 to
- second cell for lebron instead of d2 to c2 it's now d3 to c3 if i click on the
- c2 it's now d3 to c3 if i click on the next one it's d4 to c4 and you can see
- next one it's d4 to c4 and you can see that the spreadsheet has just sort of
- that the spreadsheet has just sort of upped the number right for each
- upped the number right for each subsequent row as it applied the formula
- subsequent row as it applied the formula down anybody have problems with that
- no okay so then we're going to make another column we're going to get
- another column we're going to get statistical i got to stretch for this
- statistical i got to stretch for this we're going to do percent change
- we're going to do percent change right so in f1 i'm going to type percent
- right so in f1 i'm going to type percent change i'm going to bold it and so we
- change i'm going to bold it and so we want to know on a percentage basis
- want to know on a percentage basis what's the change for each person right
- what's the change for each person right so who here is such a mathematical
- so who here is such a mathematical wizard that they can share with the
- wizard that they can share with the group
- group the formula for percent change somebody
- the formula for percent change somebody here knows it
- math math math i think you got the wrong crowd i'm not
- i think you got the wrong crowd i'm not gonna lie i don't know
- all right so maybe not you can be you can get pretty far in
- you can be you can get pretty far in journalism with just one or two
- journalism with just one or two mathematical tricks but every report i
- mathematical tricks but every report i was gonna say
- was gonna say you know you should know how to do a
- you know you should know how to do a percent change right which is you know
- percent change right which is you know like if if there's 10 of something and
- like if if there's 10 of something and it goes up to 20 it's doubled or that's
- it goes up to 20 it's doubled or that's a hundred percent change right an
- a hundred percent change right an increase or decrease and that's a way of
- increase or decrease and that's a way of coming up with changes that you can
- coming up with changes that you can compare for different groups obviously
- compare for different groups obviously there's pitfalls to it really small
- there's pitfalls to it really small numbers can have really large percent
- numbers can have really large percent changes that can be misleading right
- changes that can be misleading right there can be volatility in the data from
- there can be volatility in the data from data point to data point that can lead
- data point to data point that can lead to noise which is why everybody does
- to noise which is why everybody does seven day averages with coven if you've
- seven day averages with coven if you've noticed right
- noticed right um but it's a really good formula to
- um but it's a really good formula to learn and um the way i keep it memorized
- learn and um the way i keep it memorized in my head because i'm not a
- in my head because i'm not a mathematical
- mathematical with either is with this little say
- with either is with this little say it's new minus old divided by old right
- it's new minus old divided by old right so you take the new value which is what
- so you take the new value which is what we're going to pay somebody you subtract
- we're going to pay somebody you subtract from it
- from it the old value hey we already did that in
- the old value hey we already did that in column e right that's new minus old
- column e right that's new minus old right there and then you divide that
- right there and then you divide that against the old value or the original
- against the old value or the original value
- value and that will return the percentage
- and that will return the percentage change
- change and so it's this minus this divided by
- and so it's this minus this divided by this
- this right is what's going to get us there
- right is what's going to get us there and so you can
- in the spreadsheet really write formulas of pretty great complexity following the
- of pretty great complexity following the same principles that we just did before
- same principles that we just did before so i want to do new minus old what do i
- so i want to do new minus old what do i type just to do that right here guys
- type just to do that right here guys what should i type in somebody
- d3 or sorry
- or sorry wait yeah d2 it's all right here we got
- wait yeah d2 it's all right here we got it
- it minus c2 minus c2 and then i want to
- minus c2 minus c2 and then i want to divide it against c2 so that's divided
- divide it against c2 so that's divided by c2 right all right so that's that's
- by c2 right all right so that's that's my first hunch does anybody see what's
- my first hunch does anybody see what's wrong with this anybody remember from
- wrong with this anybody remember from high school or
- high school or elementary school
- the order of operations order of operations right so in math it'll do the
- operations right so in math it'll do the division before it does the subtraction
- division before it does the subtraction right which would screw up the math and
- right which would screw up the math and so just like in like pre-algebra class
- so just like in like pre-algebra class you can put a little percentage sign
- you can put a little percentage sign around the subtraction d2 minus c2
- around the subtraction d2 minus c2 inside
- inside parentheses which will which will ensure
- parentheses which will which will ensure that that runs first right and then the
- that that runs first right and then the result of that is then divided into c2
- result of that is then divided into c2 and so if i do that and hit enter
- and so if i do that and hit enter right you can see that it's negative 0.3
- right you can see that it's negative 0.3 well is that a percent what's wrong
- you need to format it as a percent right because remember percentages are
- right because remember percentages are often just you know fractions between 0
- often just you know fractions between 0 and 1 or 1 and negative 1.
- and 1 or 1 and negative 1. and when we read them we often take them
- and when we read them we often take them times a hundred and like round them off
- times a hundred and like round them off right
- right and so you could you could put times 100
- and so you could you could put times 100 in your formula and that would work and
- in your formula and that would work and there'd be nothing wrong with it right
- there'd be nothing wrong with it right but like another trick is just to run it
- but like another trick is just to run it this way and then hit this little
- this way and then hit this little percentage formatting button right there
- percentage formatting button right there right and boom that'll do the math for
- right and boom that'll do the math for you and that tells us steph curry's
- you and that tells us steph curry's taking a 30
- taking a 30 pay cut
- pay cut right
- right okay so there was that auto fill trick
- okay so there was that auto fill trick before which is great but there's
- before which is great but there's another way to roll down your formulas i
- another way to roll down your formulas i want to make sure everybody knows
- want to make sure everybody knows clicking here on f2 you see the blue box
- clicking here on f2 you see the blue box around it and then in the lower right
- around it and then in the lower right hand corner there's kind of this like
- hand corner there's kind of this like little fatty box there
- little fatty box there and that also doesn't have a name as far
- and that also doesn't have a name as far as i know i think some people call it
- as i know i think some people call it the magic corner
- the magic corner or whatever but it's really just kind of
- or whatever but it's really just kind of this little handlebar and you'll notice
- this little handlebar and you'll notice when you hover over it the cursor
- when you hover over it the cursor changes into this crosshairs again i
- changes into this crosshairs again i don't know why a crosshairs just nerds
- don't know why a crosshairs just nerds did it that way but what you're going to
- did it that way but what you're going to do is you're going to hover till you get
- do is you're going to hover till you get that crosshairs you're going to click
- that crosshairs you're going to click and hold it down and then if you drag
- and hold it down and then if you drag that
- that down to f6 you can see that it applies
- down to f6 you can see that it applies the formula across all those cells
- the formula across all those cells that's another way to do the autofill
- that's another way to do the autofill is to just grab that corner and drag it
- is to just grab that corner and drag it down another way is just to double click
- down another way is just to double click it
- it and boom it'll fill all the way to the
- and boom it'll fill all the way to the bottom for you
- bottom for you and so we could see that we gave some
- and so we could see that we gave some pretty big raises at the bottom of our
- pretty big raises at the bottom of our sheet and a little at the top we took
- sheet and a little at the top we took from the one percent guys
- from the one percent guys we're we're the robin hood owners here
- we're we're the robin hood owners here okay
- okay that work for everybody any questions
- there's actually a really bad joke about new minus old divided by old which is uh
- new minus old divided by old which is uh you know it's there's like it's got it's
- you know it's there's like it's got it's even like structured like a joke hey did
- even like structured like a joke hey did anybody here get into journalism to make
- anybody here get into journalism to make a lot of money
- a lot of money no
- no get it
- i don't know if i like that but that is a joke that people tell
- but that is a joke that people tell it is a joke that people tell that will
- it is a joke that people tell that will be my halloween joke next year
- be my halloween joke next year yeah
- um okay so we've we've
- okay so we've we've computed some new columns or calculated
- computed some new columns or calculated some new columns by tacking them on and
- some new columns by tacking them on and writing formulas right and so that's
- writing formulas right and so that's called computing or calculating but like
- called computing or calculating but like another common mathematical thing is to
- another common mathematical thing is to aggregate it right is to add it all up i
- aggregate it right is to add it all up i don't want to annotate a new column or a
- don't want to annotate a new column or a new field on each row i want to look
- new field on each row i want to look across all the fields and come up with
- across all the fields and come up with some high level statistics right like
- some high level statistics right like what is the total amount of money that
- what is the total amount of money that we're spending as a team
- we're spending as a team right what's the average salary on the
- right what's the average salary on the team or the median right and those
- team or the median right and those aggregated statistics can also be
- aggregated statistics can also be calculated really easily
- calculated really easily in a spreadsheet tool
- in a spreadsheet tool using different formulas using functions
- using different formulas using functions that are pre-written to do aggregations
- that are pre-written to do aggregations that work a little differently okay and
- that work a little differently okay and to do that we're going to go down um
- to do that we're going to go down um beneath our data set here we want to
- beneath our data set here we want to kind of keep this as its own thing we
- kind of keep this as its own thing we don't want what we're about to do to get
- don't want what we're about to do to get kind of confused with it as we work it
- kind of confused with it as we work it later but um
- later but um so we're going to go down below we're
- so we're going to go down below we're going to create a little island a little
- going to create a little island a little separate data set down below so just
- separate data set down below so just click like three or four rows
- click like three or four rows down below the total maybe click on like
- down below the total maybe click on like c9
- c9 and the question that i want to answer
- and the question that i want to answer here
- here is what was the total amount of money
- is what was the total amount of money that all these players last year
- that all these players last year right and the mathematical term for
- right and the mathematical term for totaling something is summing it right
- totaling something is summing it right the sum total and so if you hit equal
- the sum total and so if you hit equal again and then just type the word sum
- again and then just type the word sum see this like magic pull down whether
- see this like magic pull down whether you know it or not available to you
- you know it or not available to you within your spreadsheet is literally
- within your spreadsheet is literally hundreds of magic functions that can
- hundreds of magic functions that can take in cells or lists of cells and
- take in cells or lists of cells and return some computed or aggregated
- return some computed or aggregated result right and that includes all the
- result right and that includes all the mathematical operations that the
- mathematical operations that the calculator can do right like the sum and
- calculator can do right like the sum and so by typing equal sum and then a
- so by typing equal sum and then a parenthesis it's expecting you after the
- parenthesis it's expecting you after the parenthesis to tell it what you want it
- parenthesis to tell it what you want it to sum and i wanted to sum these five
- to sum and i wanted to sum these five rows right in column c and so i'm gonna
- rows right in column c and so i'm gonna while that little open parenthesis is
- while that little open parenthesis is hanging there i'm just gonna go click
- hanging there i'm just gonna go click and drag down across those five rows and
- and drag down across those five rows and you can see it now says c2 colon c6 so
- you can see it now says c2 colon c6 so that just means the series of rows that
- that just means the series of rows that starts here and ends there right that
- starts here and ends there right that list of them is going to be fed into the
- list of them is going to be fed into the sum function
- sum function i then close the parenthesis and we have
- i then close the parenthesis and we have a complete function
- a complete function and then hit enter
- and then hit enter boom the sum total right and we can see
- boom the sum total right and we can see that the current salaries of all of our
- that the current salaries of all of our players together
- players together is 95 million dollars and just here on
- is 95 million dollars and just here on the left i'm going to type sum
- the left i'm going to type sum just so that's kind of labeled right
- just so that's kind of labeled right now
- now if i want to
- if i want to do that same thing for our new salary
- do that same thing for our new salary what's our payroll this year on our
- what's our payroll this year on our dream team
- dream team using only tricks we've learned this far
- using only tricks we've learned this far can anyone guess how i could with just a
- can anyone guess how i could with just a flick of the wrist quickly calculate
- flick of the wrist quickly calculate everything the same thing for column d
- the magic corner the magic corner so if i click on c2
- i click on c2 i go to the magic corner hold and drag
- i go to the magic corner hold and drag to the right
- to the right boom we can see when i click on it that
- boom we can see when i click on it that c2 to c6 is now d2 to d6 right it's just
- c2 to c6 is now d2 to d6 right it's just kind of magically carried over to the
- kind of magically carried over to the next column and we can see that our
- next column and we can see that our payroll has more than doubled
- payroll has more than doubled right
- right and you know i might see this as like
- and you know i might see this as like the head of basketball operations on our
- the head of basketball operations on our dream team and say we really gotta bring
- dream team and say we really gotta bring down how much jason tatum's making like
- down how much jason tatum's making like he's good but i'm gonna cut that in half
- he's good but i'm gonna cut that in half and so if i just go up above and i
- and so if i just go up above and i instead put in 50 million cut his pay in
- instead put in 50 million cut his pay in half see how everything instantly
- half see how everything instantly updated right so our sum total below
- updated right so our sum total below went down 50 million and his his change
- went down 50 million and his his change values instantly updated right if i put
- values instantly updated right if i put one dollar in there you would see that
- one dollar in there you would see that they changed to reflect that if i undo
- they changed to reflect that if i undo and go back to where i was before it
- and go back to where i was before it changes and this is part of the magic of
- changes and this is part of the magic of the spreadsheet right is that once the
- the spreadsheet right is that once the formula is in place if the numbers
- formula is in place if the numbers change everything automatically updates
- okay so sums one good statistic another one is the average right which is just
- one is the average right which is just what's the sort of take all of them
- what's the sort of take all of them together and divide it by the number of
- together and divide it by the number of members
- members what do we get so the average can be
- what do we get so the average can be calculated by an equal
- calculated by an equal uh mean
- uh mean no average sometimes it's called the
- no average sometimes it's called the mean sometimes it's called the average
- mean sometimes it's called the average i often struggle is it average or is it
- i often struggle is it average or is it mean guys
- mean guys i think it's is it mean so let's just
- i think it's is it mean so let's just try it
- try it no so like the way i could do this like
- no so like the way i could do this like let's say you're ben and you can't
- let's say you're ben and you can't remember you just go to your buddy
- remember you just go to your buddy google and you do like google
- google and you do like google sheets
- sheets average formula yeah what is that again
- average formula yeah what is that again here's google has a website and they
- here's google has a website and they have a page for every one of these
- have a page for every one of these formulas that like tells you what the
- formulas that like tells you what the name of it is defines it tell you how to
- name of it is defines it tell you how to do it you can see here on the right
- do it you can see here on the right there's literally hundreds of these like
- there's literally hundreds of these like crazy math functions
- crazy math functions and you can see that no band in this
- and you can see that no band in this case it's not called the mean it's
- case it's not called the mean it's called the average so i do equal average
- called the average so i do equal average open parenthesis drag my c cells
- open parenthesis drag my c cells hit enter
- hit enter boom
- boom right we can see that
- right we can see that the average salary
- the average salary is 19 000.
- is 19 000. i then can just grab the corner drag to
- i then can just grab the corner drag to the right we can see that's gone up to
- the right we can see that's gone up to 35 million right
- 35 million right that's because we really raised up
- that's because we really raised up people on the low end right because as
- people on the low end right because as we know in averages things that are
- we know in averages things that are really high or really low can kind of
- really high or really low can kind of skew the numbers right so when it comes
- skew the numbers right so when it comes to something like home sales or income
- to something like home sales or income as is very well publicized there is a
- as is very well publicized there is a small number of people that have really
- small number of people that have really expensive homes and make lots and lots
- expensive homes and make lots and lots of money and so they can kind of skew
- of money and so they can kind of skew the results of an average in case it's
- the results of an average in case it's not in a way that's saying wrong but
- not in a way that's saying wrong but they can be a little misleading about
- they can be a little misleading about what the typical sort of situation is
- what the typical sort of situation is right does anybody know what the
- right does anybody know what the statistical solution
- statistical solution or alternative to an average is in a
- or alternative to an average is in a case where you have skewed data
- so the standard deviation that's part of it but that's not the
- that's part of it but that's not the number that's not exactly it
- number that's not exactly it the standard deviation is a little more
- the standard deviation is a little more sophisticated
- looking at the median the median who said that
- too i don't know if i i don't know i feel like claire and i said at the same
- feel like claire and i said at the same time
- time okay i didn't say anything no you got it
- okay i didn't say anything no you got it you didn't say it
- you didn't say it no do you want what's the median explain
- no do you want what's the median explain it the median is the one that's in the
- it the median is the one that's in the dead middle and so it's not affected by
- dead middle and so it's not affected by you know the really high
- you know the really high the highest number or the lowest number
- the highest number or the lowest number it's just kind of like what's right in
- it's just kind of like what's right in the middle of the set of numbers yeah
- the middle of the set of numbers yeah you just sort everybody from highest to
- you just sort everybody from highest to lowest like say there's 100 people you
- lowest like say there's 100 people you sort them and after you sort them by the
- sort them and after you sort them by the value whoever is the 50th one is the
- value whoever is the 50th one is the media right it's also you know it's also
- media right it's also you know it's also the 50th percentile is like another way
- the 50th percentile is like another way of thinking about it if you're familiar
- of thinking about it if you're familiar with percentiles
- with percentiles and you know you'll notice this in the
- and you know you'll notice this in the news when people talk about home sales
- news when people talk about home sales this is really common right it's always
- this is really common right it's always the median sales price of homes in the
- the median sales price of homes in the news right and that's the reason is
- news right and that's the reason is because there's really a small number of
- because there's really a small number of high ones that skew the number and there
- high ones that skew the number and there could be a difference and so really
- could be a difference and so really whenever you're doing a story if you
- whenever you're doing a story if you calculate an average it's kind of good
- calculate an average it's kind of good practice to look at the median just to
- practice to look at the median just to kind of see if there's much difference
- kind of see if there's much difference between the two the average while
- between the two the average while statistically not as good in some ways
- statistically not as good in some ways is is sometimes better to use because
- is is sometimes better to use because it's just more commonplace you know when
- it's just more commonplace you know when you're writing a news story people know
- you're writing a news story people know what that is it's easy to get across
- what that is it's easy to get across right
- right and so um
- and so um i'll sometimes use it if they're really
- i'll sometimes use it if they're really close just because it's easier but the
- close just because it's easier but the key thing to really contemplate is is my
- key thing to really contemplate is is my data set skewed you know is there a few
- data set skewed you know is there a few records that are really far out
- records that are really far out different in the case of brittany griner
- different in the case of brittany griner i think we kind of have a little bit of
- i think we kind of have a little bit of skewing there right
- skewing there right um so to calculate the median i'm going
- um so to calculate the median i'm going to make another row to put median and
- to make another row to put median and like you literally just do equal median
- like you literally just do equal median right it's that easy
- right it's that easy open parenthesis
- open parenthesis drag
- drag close parenthesis
- close parenthesis boom and we can see there is quite a
- boom and we can see there is quite a difference there right
- difference there right uh now in the case of the median it's
- uh now in the case of the median it's jason tatum who is the medium value
- jason tatum who is the medium value right of the five he ranks in the middle
- right of the five he ranks in the middle so he becomes the median if i drag it to
- so he becomes the median if i drag it to the right and look at the median our
- the right and look at the median our median becomes 30 million steph curry
- any questions about the average the median
- there's many other things minimum maximum
- maximum standard deviation is one which helps
- standard deviation is one which helps you understand the distribution
- you understand the distribution the curve
- okay then we're going to cover we're going to move on to our next basic
- we're going to move on to our next basic skill which is sorting and filtering
- skill which is sorting and filtering right so let's say you have a data set
- right so let's say you have a data set but you want to resort it to say what
- but you want to resort it to say what comes out on top what comes out on the
- comes out on top what comes out on the bottom you want to focus in just on one
- bottom you want to focus in just on one subset of the records this is where
- subset of the records this is where sorting and filtering comes in handy and
- sorting and filtering comes in handy and you can do that really easily in any
- you can do that really easily in any spreadsheet tool by putting sort of a
- spreadsheet tool by putting sort of a magic filter at the top
- magic filter at the top you can do so what we want to do is want
- you can do so what we want to do is want to select all of our data so i just
- to select all of our data so i just clicked on f6 and i dragged and i
- clicked on f6 and i dragged and i selected all the data that's outside of
- selected all the data that's outside of my little island my statistical island
- my little island my statistical island at the bottom there
- at the bottom there and then
- and then [Music]
- [Music] i'm going to click on the data pull down
- i'm going to click on the data pull down menu at the top and you see that there's
- menu at the top and you see that there's this option called create a filter
- this option called create a filter i want everybody to click on that
- i want everybody to click on that and you'll see that not a lot of changes
- and you'll see that not a lot of changes but there's these kind of magic green
- but there's these kind of magic green arrows on each of our header rows right
- arrows on each of our header rows right and excel has something just like this
- and excel has something just like this it's just in a slightly different place
- it's just in a slightly different place and these magic arrows now have like
- and these magic arrows now have like options for us that allow us to
- options for us that allow us to manipulate
- manipulate our data set right so if i wanted to
- our data set right so if i wanted to sort people by their current salary from
- sort people by their current salary from highest to lowest so maybe i could see
- highest to lowest so maybe i could see the median
- the median i click the little green guy
- i click the little green guy and you can see that there's two sorting
- and you can see that there's two sorting options a to z sorting in ascending
- options a to z sorting in ascending order or z to a sorting in descending
- order or z to a sorting in descending order and so since i want the top salary
- order and so since i want the top salary at the top
- at the top i'm going to sort z to a
- i'm going to sort z to a and we can see that that shifted things
- and we can see that that shifted things with jason tatum at the bottom i think
- with jason tatum at the bottom i think it was like that to start with i'm going
- it was like that to start with i'm going to do it on d and do z to a and you can
- to do it on d and do z to a and you can see that they resort right we now have
- see that they resort right we now have tatum and grinder at the top and matisse
- tatum and grinder at the top and matisse thigh bowl at the bottom if we wanted to
- thigh bowl at the bottom if we wanted to sort by percentage change who had the
- sort by percentage change who had the biggest percentage change i could do a
- biggest percentage change i could do a sort there as well
- and if you're looking at a column like name it'll just sort alphabetically
- name it'll just sort alphabetically right as opposed to sorting numerically
- right as opposed to sorting numerically and that's where the data type comes in
- and that's where the data type comes in because depending on whether your column
- because depending on whether your column is words or numbers or dates how the
- is words or numbers or dates how the computer sorts it differs right and so
- computer sorts it differs right and so that's why it's important to kind of get
- that's why it's important to kind of get that right
- that right the other thing is a filter let's say we
- the other thing is a filter let's say we wanted to look just at our guards for
- wanted to look just at our guards for instance i click on the position column
- instance i click on the position column you can see that it gives you the three
- you can see that it gives you the three unique values there's center forwards
- unique values there's center forwards and guards if i just uncheck
- and guards if i just uncheck center and forward see i just clicked on
- center and forward see i just clicked on there to uncheck it and hit ok
- there to uncheck it and hit ok boom the data set is filtered down to
- boom the data set is filtered down to just the guards and you can see that
- just the guards and you can see that there's now a little funnel icon
- there's now a little funnel icon to indicate we've done a filter
- to indicate we've done a filter if i click again and i switch it to
- if i click again and i switch it to forward
- forward it's filtered to our one or two forwards
- it's filtered to our one or two forwards right if i click it again and i click on
- right if i click it again and i click on select all
- select all we have everybody
- we have everybody that was a character filter based on a
- that was a character filter based on a categorical value but you can also do
- categorical value but you can also do filters that are numerical let's say we
- filters that are numerical let's say we want to see everyone who makes more than
- want to see everyone who makes more than 10 million dollars right you can do
- 10 million dollars right you can do what's called filtering by condition
- what's called filtering by condition and then you can say greater than less
- and then you can say greater than less than equal to et cetera and because this
- than equal to et cetera and because this is a numerical field you can do your
- is a numerical field you can do your filters with a numerical expression so
- filters with a numerical expression so if i say i want to see everyone who made
- if i say i want to see everyone who made more than 25 million
- more than 25 million i just would select greater than the
- i just would select greater than the condition i'd type in to great 25
- condition i'd type in to great 25 million
- million and you can see that there's only two
- and you can see that there's only two players who then qualify right and this
- players who then qualify right and this these sorting and filtering can be a
- these sorting and filtering can be a good way to ask and answer some basic
- good way to ask and answer some basic questions that you have about your data
- questions that you have about your data set as you interview it as you try to
- set as you interview it as you try to find a story in it right
- okay any questions about sorting and
- any questions about sorting and filtering
- okay so i'm going to do one pop quiz question so if i wanted to get the grand
- question so if i wanted to get the grand total of money spent across both years
- total of money spent across both years of all salaries
- of all salaries how would i do that what would i do in
- how would i do that what would i do in the spreadsheet somebody pipe up and
- the spreadsheet somebody pipe up and tell me what to type
- i think you would do equal sum and then take your
- take your magic little dragger and go across
- magic little dragger and go across current salary and new salary
- current salary and new salary just like that right selecting both
- just like that right selecting both that's definitely a way to do it
- that's definitely a way to do it we get 270 million another way would be
- we get 270 million another way would be to sum the computed columns i could just
- to sum the computed columns i could just click on the two totals for each year
- click on the two totals for each year there
- there and do that and that would work as well
- and do that and that would work as well it comes out the same because you can do
- it comes out the same because you can do formulas of formulas right and it all
- formulas of formulas right and it all adds up together
- adds up together i might even say this is current this is
- i might even say this is current this is new and this is total okay if i wanted
- new and this is total okay if i wanted to get the average salary across both
- to get the average salary across both years how would i do that
- it would just be equals average and then you would do
- you would do parentheses current salary and then
- parentheses current salary and then through the new salary right
- through the new salary right yep equal average and drag them across
- yep equal average and drag them across there so it's the same thing
- there so it's the same thing that claire did for some it's just we
- that claire did for some it's just we typed average instead of sum as our
- typed average instead of sum as our formula right because it's the same
- formula right because it's the same range of cells it's called a range the
- range of cells it's called a range the sort of list of cells but it's just
- sort of list of cells but it's just going into a different function
- going into a different function right and i could do the same thing with
- right and i could do the same thing with median as well
- and if i wanted to do the change in my salary from year to year i could just
- salary from year to year i could just simply do equals
- simply do equals this
- this minus this right and we've calculated
- minus this right and we've calculated that i can autofill and we can see the
- that i can autofill and we can see the change in each of those values from year
- change in each of those values from year to year as a grand total too
- to year as a grand total too and because we've kept this separately
- and because we've kept this separately as an island the sort and filter doesn't
- as an island the sort and filter doesn't really affect this that's why it's nice
- really affect this that's why it's nice to leave that little gap in there
- that's covered sorting and filtering i have one more basic skill thing we're
- have one more basic skill thing we're going to learn which is sort of the
- going to learn which is sort of the crowning skill of spreadsheets before we
- crowning skill of spreadsheets before we get into our real data set but before i
- get into our real data set but before i do that i just want to give a second in
- do that i just want to give a second in case there's any questions
- okay great so the next thing we're going to cover is how to group and aggregate
- to cover is how to group and aggregate for groups within the data set right we
- for groups within the data set right we were able to compute a value for each
- were able to compute a value for each row we were able to aggregate values
- row we were able to aggregate values across the entire data set but there's
- across the entire data set but there's another thing that's a really common
- another thing that's a really common kind of deal
- kind of deal which is to look at to do aggregate
- which is to look at to do aggregate values for groups of rows as opposed for
- values for groups of rows as opposed for all the rows so for instance if you
- all the rows so for instance if you wanted to ask the question what's the
- wanted to ask the question what's the total amount of money we're paying our
- total amount of money we're paying our guards versus our forwards right or if
- guards versus our forwards right or if we added a column that was gender let's
- we added a column that was gender let's do it let's add a column this gender
- do it let's add a column this gender right oh see how i did that i clicked on
- right oh see how i did that i clicked on b
- b i right clicked it says insert one
- i right clicked it says insert one column left boom i get a new column so
- column left boom i get a new column so if i do gender and i just do you know f
- if i do gender and i just do you know f m
- m m right for the two the genders in this
- m right for the two the genders in this case or the sexes i should say um
- case or the sexes i should say um let's re-label that right
- let's re-label that right because i think brittany griner is
- because i think brittany griner is non-binary is that correct i can't be
- non-binary is that correct i can't be corrected on this book yeah i think they
- corrected on this book yeah i think they are
- are yeah and um
- yeah and um and so you know forgive the vulgarity of
- and so you know forgive the vulgarity of this classification just to make the
- this classification just to make the point
- point um
- um uh
- uh you know by putting in these these
- you know by putting in these these categorical values we've created uh
- categorical values we've created uh records that can effectively be grouped
- records that can effectively be grouped together for analysis and so you can use
- together for analysis and so you can use this position or this sex column to look
- this position or this sex column to look at what's the average salary for men
- at what's the average salary for men versus women right to maybe do a gender
- versus women right to maybe do a gender pay gap analysis or to look at the
- pay gap analysis or to look at the different positions to see how the team
- different positions to see how the team is investing in one thing versus another
- is investing in one thing versus another or look at it by age or other kind of
- or look at it by age or other kind of common attributes eye color if you
- common attributes eye color if you wanted to right and to do those kind of
- wanted to right and to do those kind of group end counts or group and sums which
- group end counts or group and sums which really are pretty common
- really are pretty common there's a technique that every
- there's a technique that every spreadsheet can do and for some weird
- spreadsheet can do and for some weird nerdy reason is almost always called a
- nerdy reason is almost always called a pivot table have you guys heard this
- pivot table have you guys heard this before
- so a pivot table is just a really dumb piece of jargon from like the 1980s like
- piece of jargon from like the 1980s like clippy the paperclip or whatever which
- clippy the paperclip or whatever which really is just a sort of brand name in
- really is just a sort of brand name in the spreadsheet software for grouping
- the spreadsheet software for grouping and counting or grouping and summing or
- and counting or grouping and summing or grouping and averaging using one of your
- grouping and averaging using one of your columns and this is a really powerful
- columns and this is a really powerful tool that can you know win you a
- tool that can you know win you a pulitzer prize or whatever and
- pulitzer prize or whatever and oftentimes result in good stories
- oftentimes result in good stories because with just a couple clicks of the
- because with just a couple clicks of the mouse right and so
- mouse right and so let's do it let's make a pivot table so
- let's do it let's make a pivot table so we want to select the data that we want
- we want to select the data that we want to group and count so again i'm just
- to group and count so again i'm just going to drag my mouse over kind of
- going to drag my mouse over kind of everything from a1 to g6 right
- and so that now is selected in blue kind of my data set and the and i want to ask
- of my data set and the and i want to ask questions that have to do with position
- questions that have to do with position and have to do with sex right and so i
- and have to do with sex right and so i selected the data that i'd like to pivot
- selected the data that i'd like to pivot and then again this button will be in a
- and then again this button will be in a different place in depending on which
- different place in depending on which program you're using but it's almost
- program you're using but it's almost always called a pivot table
- always called a pivot table and in google sheets you click on insert
- and in google sheets you click on insert and then you see that right there pivot
- and then you see that right there pivot table it can also put in charts that's a
- table it can also put in charts that's a whole other world we won't get to
- whole other world we won't get to but
- but insert pivot table if you do that it's
- insert pivot table if you do that it's going to say well here what data do you
- going to say well here what data do you want me to use and this is the data
- want me to use and this is the data range a1 to g6 that's good that's what i
- range a1 to g6 that's good that's what i want
- want and then it's like okay where do you
- and then it's like okay where do you want to put the pivot table and the
- want to put the pivot table and the right answer almost always is new sheet
- right answer almost always is new sheet which is good so just assuming you see
- which is good so just assuming you see something like that just hit create
- something like that just hit create and that's going to take you to a new
- and that's going to take you to a new spreadsheet
- spreadsheet and you might be like holy cow where'd
- and you might be like holy cow where'd my data go right and this is where it's
- my data go right and this is where it's important to kind of stop and make sure
- important to kind of stop and make sure you get one of the basic user interface
- you get one of the basic user interface things about a spreadsheet that i
- things about a spreadsheet that i haven't covered which is that your
- haven't covered which is that your spreadsheet is a workbook that can have
- spreadsheet is a workbook that can have multiple spreadsheets within it you can
- multiple spreadsheets within it you can have kind of a bundle of spreadsheets in
- have kind of a bundle of spreadsheets in your book so to speak and those are
- your book so to speak and those are controlled in the lower left hand corner
- controlled in the lower left hand corner do you see these little tabs down here
- do you see these little tabs down here so sheet1 if i click there takes me back
- so sheet1 if i click there takes me back to where i started i didn't lose my data
- to where i started i didn't lose my data but then the pivot table was inserted
- but then the pivot table was inserted into sheet two what's called pivot table
- into sheet two what's called pivot table one by default right
- one by default right which is
- which is um where the new calculated values are
- um where the new calculated values are going to be placed you can even name
- going to be placed you can even name your tabs so if i click on the arrow on
- your tabs so if i click on the arrow on sheet one and hit rename i can type
- sheet one and hit rename i can type roster right because that's like my
- roster right because that's like my roster of players
- roster of players but we're going to go back to the pivot
- but we're going to go back to the pivot table now we're going to use this to um
- table now we're going to use this to um to group and count by position right
- to group and count by position right and so you can see here on the left is
- and so you can see here on the left is sort of a blank canvas on which we're
- sort of a blank canvas on which we're going to group and count our data and
- going to group and count our data and then on the right are some like little
- then on the right are some like little knobs and like goofy things which is how
- knobs and like goofy things which is how you manipulate the pivot table right you
- you manipulate the pivot table right you can see it as rows columns and values
- can see it as rows columns and values are the key ones so the row is really
- are the key ones so the row is really the thing you want to group by right so
- the thing you want to group by right so like which of the columns do you want to
- like which of the columns do you want to roll up into like subtotals and for us
- roll up into like subtotals and for us that's position and you can see here on
- that's position and you can see here on the right hand side all of our columns
- the right hand side all of our columns are like available so you just click on
- are like available so you just click on position and you drag it into the rows
- position and you drag it into the rows area of the pivot table and boom you see
- area of the pivot table and boom you see automatically there on the left it's
- automatically there on the left it's created a list of just the unique values
- created a list of just the unique values by position
- by position right center forward and guard and now i
- right center forward and guard and now i want to calculate values for each of
- want to calculate values for each of those so i want to get the total amount
- those so i want to get the total amount of salary we're now spending so i'm
- of salary we're now spending so i'm going to grab our new salary column
- going to grab our new salary column i'm going to drag that to the values
- i'm going to drag that to the values area i'm going to drop it and you can
- area i'm going to drop it and you can see that here it says summarize by so
- see that here it says summarize by so that's the that's the function or
- that's the that's the function or formula to run on that column and if i
- formula to run on that column and if i click on it you can see the sum and
- click on it you can see the sum and count and average and max and min and
- count and average and max and min and median and standard deviation and you
- median and standard deviation and you know they can you could pick but some is
- know they can you could pick but some is what we wanted to start with so i'm good
- what we wanted to start with so i'm good with that we can see that we're spending
- with that we can see that we're spending 50 million on center 70 on forward 55 of
- 50 million on center 70 on forward 55 of card so if i want to get the average
- card so if i want to get the average it's as easy as taking new salary again
- it's as easy as taking new salary again dragging and dropping it into values and
- dragging and dropping it into values and you can see wow another column magically
- you can see wow another column magically appears
- appears i can select average
- i can select average boom that's now got the averages
- boom that's now got the averages automatically calculated right and i can
- automatically calculated right and i can do this for counts too oftentimes the
- do this for counts too oftentimes the most common way to use this is just to
- most common way to use this is just to count how many people
- count how many people are in each category that you're
- are in each category that you're interested in right and so if i just
- interested in right and so if i just take
- take current salary
- current salary drop it a third or you really can do it
- drop it a third or you really can do it for any field but let's do new salary a
- for any field but let's do new salary a third time
- third time if i select count a count a is just like
- if i select count a count a is just like the weird way of saying count the number
- the weird way of saying count the number of records it's just like a weird
- of records it's just like a weird tradition
- tradition and if you do that it quickly tells us
- and if you do that it quickly tells us there's two forwards two guards and one
- there's two forwards two guards and one center right and you guys can see how
- center right and you guys can see how this basic pivot table we just did if
- this basic pivot table we just did if you were to put in like the city of
- you were to put in like the city of chicago's government salaries database
- chicago's government salaries database right you could compare how much the
- right you could compare how much the average firefighter makes to the average
- average firefighter makes to the average police officer you could look at
- police officer you could look at different ranks within
- different ranks within an agency and see how much the
- an agency and see how much the executives get paid versus the lower
- executives get paid versus the lower level workers and this these basic
- level workers and this these basic tricks can be done to do all kinds of
- tricks can be done to do all kinds of stories and analysis and the pivot is
- stories and analysis and the pivot is often your way to get there
- often your way to get there and there's a lot more to it and a lot
- and there's a lot more to it and a lot more can do but that's really the basics
- more can do but that's really the basics so we've grouped here by position just
- so we've grouped here by position just to kind of show how easy it is i'm going
- to kind of show how easy it is i'm going to click the little x there to get rid
- to click the little x there to get rid of position and then i'm just going to
- of position and then i'm just going to drop the sex column into rows now right
- drop the sex column into rows now right and you can see that i've left the
- and you can see that i've left the calculated values there and by switching
- calculated values there and by switching what was in the rows it's now grouping
- what was in the rows it's now grouping by sex as opposed to grouping by
- by sex as opposed to grouping by position and the same analysis is just
- position and the same analysis is just immediately run for all of them pretty
- immediately run for all of them pretty nice right
- nice right and a lot of times for data journals and
- and a lot of times for data journals and stories the numbers that appear in your
- stories the numbers that appear in your pivot table just go right into your
- pivot table just go right into your story you know after you vet the data
- story you know after you vet the data and do all that other stuff of course
- and do all that other stuff of course but this is often where you come up with
- but this is often where you come up with what is going to be reported
- we'll do a more sophisticated pivot table in a minute but that's really the
- table in a minute but that's really the basics of it you just learned
- basics of it you just learned any questions about pivot tables
- no okay well those are the basic skills that i wanted to cover that's kind of
- that i wanted to cover that's kind of part one of this class and so now i
- part one of this class and so now i think we're gonna advance to kind of
- think we're gonna advance to kind of part two which is instead of creating
- part two which is instead of creating sort of a funny data set on our own
- sort of a funny data set on our own we're going to look at a serious data
- we're going to look at a serious data set something that came from the real
- set something that came from the real world and we're going to apply
- world and we're going to apply some of those
- some of those skills we just learned to ask and answer
- skills we just learned to ask and answer questions of this real data set okay and
- questions of this real data set okay and so the data set that we're going to use
- so the data set that we're going to use comes from a group called the invisible
- comes from a group called the invisible institute does anybody here know what
- institute does anybody here know what that is
- the invisible institute is a chicago-based sort of non-profit group
- chicago-based sort of non-profit group that produces investigative journalism
- that produces investigative journalism documentaries even an art exhibit that's
- documentaries even an art exhibit that's currently on display at the ball is
- currently on display at the ball is connected to the invisible institute and
- connected to the invisible institute and it's sort of one
- it's sort of one um permutation of kind of a shifting
- um permutation of kind of a shifting group of chicago people who over the
- group of chicago people who over the last couple decades have put a lot of
- last couple decades have put a lot of effort
- effort into
- into exposing abuses by the chicago police
- exposing abuses by the chicago police department through public records
- department through public records requests lawsuits and other things um
- requests lawsuits and other things um they are connected in a complicated way
- they are connected in a complicated way to the john burge torture story if
- to the john burge torture story if you're familiar with that if you're not
- you're familiar with that if you're not google it um and one of their big data
- google it um and one of their big data projects that they did a few years ago
- projects that they did a few years ago and they were really one of the first
- and they were really one of the first groups in the whole country to do this
- groups in the whole country to do this is they were they um one through a
- is they were they um one through a freedom of information act request not
- freedom of information act request not an email not a memo not the sort of
- an email not a memo not the sort of typical documents you might associate
- typical documents you might associate with foia but a database you know
- with foia but a database you know there's a literally a bureaucratic
- there's a literally a bureaucratic process in the city of chicago where if
- process in the city of chicago where if you believe a police officer has
- you believe a police officer has mistreated you you can file a complaint
- mistreated you you can file a complaint and that complaint depending on how it
- and that complaint depending on how it plays off kicks off a kind of like
- plays off kicks off a kind of like um i guess
- um i guess adjudication process that results in um
- adjudication process that results in um an investigation in some cases and then
- an investigation in some cases and then a decision about whether your complaint
- a decision about whether your complaint is upheld or not and then whether the
- is upheld or not and then whether the officer should be disciplined
- officer should be disciplined and that
- and that government process generates data right
- government process generates data right there is literally like a glorified
- there is literally like a glorified spreadsheet or database system inside
- spreadsheet or database system inside the city of chicago that's tracking all
- the city of chicago that's tracking all those things and those databases are
- those things and those databases are things that you can file public or
- things that you can file public or records requests for and literally get
- records requests for and literally get the raw data to analyze on your own and
- the raw data to analyze on your own and so one whole sort of strain of data
- so one whole sort of strain of data journalism one type of data story is not
- journalism one type of data story is not building your own database but prying
- building your own database but prying loose a database from the government and
- loose a database from the government and then kind of figuring out how it works
- then kind of figuring out how it works and what's really in it and then
- and what's really in it and then analyzing it to do stories or in the
- analyzing it to do stories or in the case of you know now on the web just
- case of you know now on the web just republish the whole darn thing and
- republish the whole darn thing and that's what the invisible institute did
- that's what the invisible institute did i think in 2016 or so i can't remember
- i think in 2016 or so i can't remember exactly that was a pretty controversial
- exactly that was a pretty controversial time is they built the website where
- time is they built the website where they just published um the complaints
- they just published um the complaints against police officers you know one
- against police officers you know one argument for this
- argument for this is transparency just on its own but also
- is transparency just on its own but also that um
- that um that uh when people are defending
- that uh when people are defending themselves in court after a police
- themselves in court after a police officer is involved in testimony or
- officer is involved in testimony or other parts of the investigation they
- other parts of the investigation they you know the one argument is that they
- you know the one argument is that they have a right to be able to you know find
- have a right to be able to you know find out if the officer testifying against
- out if the officer testifying against them has a track record that might not
- them has a track record that might not be great and so um i suspect the defense
- be great and so um i suspect the defense attorneys are probably the top user of
- attorneys are probably the top user of this website you can see here that it
- this website you can see here that it has an analysis of the whole system
- has an analysis of the whole system overall how many people in aggregate
- overall how many people in aggregate have had allegations against them
- have had allegations against them discipline and then they even have a
- discipline and then they even have a page for individual officers and this is
- page for individual officers and this is part of what makes it controversial i
- part of what makes it controversial i suppose you can see here they have a
- suppose you can see here they have a readout on individual officers and so
- readout on individual officers and so this database
- this database fueled a whole lot of stories in chicago
- fueled a whole lot of stories in chicago when it first came out
- when it first came out many people in local news copied it in
- many people in local news copied it in subsequent years and did similar stories
- subsequent years and did similar stories there was a whole reform to the chicago
- there was a whole reform to the chicago system for managing this to introduce
- system for managing this to introduce the civilian review board
- the civilian review board and a lot of it really stems back to the
- and a lot of it really stems back to the work this group did and one thing they
- work this group did and one thing they did that i think is quite admirable is
- did that i think is quite admirable is they just publish the whole database in
- they just publish the whole database in raw spreadsheet format right so they
- raw spreadsheet format right so they made a pretty website they wrote stories
- made a pretty website they wrote stories where they analyze the thing but then
- where they analyze the thing but then they also just put up the data for other
- they also just put up the data for other people to use which means other
- people to use which means other journalists can analyze it academics
- journalists can analyze it academics criminologists get their hands on it and
- criminologists get their hands on it and maybe people who wanted to vet their
- maybe people who wanted to vet their work right who might you know ultimately
- work right who might you know ultimately try to poke a hole in it and i think
- try to poke a hole in it and i think that that is just a really great kind of
- that that is just a really great kind of you know scientific practice that is
- you know scientific practice that is awesome and so kudos to them for that
- awesome and so kudos to them for that and here on their website is this kind
- and here on their website is this kind of odd page download the data
- of odd page download the data and this it links off to this even
- and this it links off to this even stranger nerdy website called github
- stranger nerdy website called github where you can get all their computer
- where you can get all their computer code but
- code but if you were to click into this little
- if you were to click into this little link
- link you would find like literally a dropbox
- you would find like literally a dropbox that has all the data they got and kind
- that has all the data they got and kind of cleaned up from the government
- of cleaned up from the government including the actual four-year request
- including the actual four-year request themselves which is always kind of
- themselves which is always kind of interesting to read you can see here
- interesting to read you can see here that they literally wrote a letter dear
- that they literally wrote a letter dear foia officer
- foia officer under the freedom of information act
- under the freedom of information act request i i request your database and
- request i i request your database and you can see they even included the names
- you can see they even included the names of the fields in the database they
- of the fields in the database they wanted right i was trying to get rajiv
- wanted right i was trying to get rajiv who was involved in this to join us but
- who was involved in this to join us but he couldn't tonight but i suspect that
- he couldn't tonight but i suspect that they did some reporting ahead of time to
- they did some reporting ahead of time to know what was in there so that they
- know what was in there so that they could make sure to request it so that it
- could make sure to request it so that it didn't get left out in the response
- didn't get left out in the response because one issue that could happen is
- because one issue that could happen is is you might request the database but do
- is you might request the database but do they really give you the whole thing
- they really give you the whole thing a lot of time they don't want to right
- a lot of time they don't want to right and so being specific in this case was
- and so being specific in this case was probably part of their strategy to make
- probably part of their strategy to make sure they got it at the end of the day
- sure they got it at the end of the day um
- um and writing that type of foia is a whole
- and writing that type of foia is a whole sort of art that we can talk about if
- sort of art that we can talk about if you want to
- you want to um and but these files are really big
- um and but these files are really big because they have hundreds of thousands
- because they have hundreds of thousands of records and i don't want to like
- of records and i don't want to like strain everybody's computer here in
- strain everybody's computer here in class
- class and they might require some heavier duty
- and they might require some heavier duty programming tools to do like a super
- programming tools to do like a super duper analysis so what i've done is i
- duper analysis so what i've done is i talked with rajiv who's behind it i
- talked with rajiv who's behind it i created like a little
- created like a little extract
- extract i went into the data i merged a couple
- i went into the data i merged a couple tables into a single table and then i
- tables into a single table and then i filtered it down to just one year worth
- filtered it down to just one year worth of complaints this is from the year 2014
- of complaints this is from the year 2014 and so what we have here is sort of a
- and so what we have here is sort of a trimmed down simplified version of the
- trimmed down simplified version of the data set that has i've removed a bunch
- data set that has i've removed a bunch of columns and i've removed a bunch of
- of columns and i've removed a bunch of rows to kind of get to something that is
- rows to kind of get to something that is derived from the original original
- derived from the original original analysis but is all real data
- analysis but is all real data this process of like cleaning the data
- this process of like cleaning the data of merging different tables filtering it
- of merging different tables filtering it down getting rid of stuff you don't want
- down getting rid of stuff you don't want is often like 80 90 of this type of data
- is often like 80 90 of this type of data story because the stuff you get from the
- story because the stuff you get from the government is often like cryptic or
- government is often like cryptic or incomplete or like gnarly and like
- incomplete or like gnarly and like understanding what's there just so you
- understanding what's there just so you can trim it down to something nice and
- can trim it down to something nice and clean that you can analyze is a whole
- clean that you can analyze is a whole reporting effort and often requires
- reporting effort and often requires another set of programming skills we're
- another set of programming skills we're not going to cover that here in class
- not going to cover that here in class okay sorry so what we're going to do is
- okay sorry so what we're going to do is i'm going to share a link to the
- i'm going to share a link to the spreadsheet
- spreadsheet in the chat and i want everybody to open
- in the chat and i want everybody to open it in google sheets and i'm going to
- it in google sheets and i'm going to show you how to make your own copy of it
- show you how to make your own copy of it really quickly okay
- really quickly okay so in google sheets if you haven't done
- so in google sheets if you haven't done it this is part of what makes it great
- it this is part of what makes it great is you can click the share button in the
- is you can click the share button in the upper right you can add people to
- upper right you can add people to privately share it which is often useful
- privately share it which is often useful but you also here can just get a link
- but you also here can just get a link that you can give to the whole world or
- that you can give to the whole world or your co-workers and this is how a lot of
- your co-workers and this is how a lot of data journalists share data within their
- data journalists share data within their newsroom it's how we make charts at the
- newsroom it's how we make charts at the la times is people make a google sheet
- la times is people make a google sheet and we plug it into the chart tool right
- and we plug it into the chart tool right and the way you give you sort of or plug
- and the way you give you sort of or plug it in somewhere give it to someone else
- it in somewhere give it to someone else is you click that share button and then
- is you click that share button and then here there's this get link section
- here there's this get link section i'm going to click you know it's
- i'm going to click you know it's restricted right now it starts as
- restricted right now it starts as private i'm going to open it up so i
- private i'm going to open it up so i click there and i'm going to say anybody
- click there and i'm going to say anybody who has the link
- who has the link can actually not just view but edit the
- can actually not just view but edit the spreadsheet that's a little dangerous
- spreadsheet that's a little dangerous you might not want to do that for stuff
- you might not want to do that for stuff for your story but for this class this
- for your story but for this class this is now like an open door to come into
- is now like an open door to come into this spreadsheet and do whatever you
- this spreadsheet and do whatever you want with it right
- want with it right so i'm going to drop that into the chat
- so i'm going to drop that into the chat here in class let me just find the chat
- here in class let me just find the chat real quick
- okay boom you guys see that link i want you
- boom you guys see that link i want you to jump in
- to jump in we should start seeing like people's
- we should start seeing like people's little like google icons coming there
- little like google icons coming there they are
- they are we got a buffalo and a kiwi a mink a bat
- we got a buffalo and a kiwi a mink a bat an elephant
- an elephant i'm just gonna give folks a second to
- i'm just gonna give folks a second to jump in
- jump in [Music]
- okay now i want you to duplicate it and make your own copy so if you go to the
- make your own copy so if you go to the file menu you see this make a copy
- file menu you see this make a copy option fourth one down once you just
- option fourth one down once you just click that and that should like open up
- click that and that should like open up a new tab on your computer
- a new tab on your computer where you have to give it a name so i'm
- where you have to give it a name so i'm gonna call mine copy
- gonna call mine copy i'm gonna say make a copy and you can
- i'm gonna say make a copy and you can see it's just gonna open a new tab
- see it's just gonna open a new tab and make another copy of the data
- and make another copy of the data excuse me
- excuse me and now i'm going to go back to my
- and now i'm going to go back to my original one because i don't need the
- original one because i don't need the copy but i want everybody to do that so
- copy but i want everybody to do that so just do file
- just do file make a copy
- okay so now we have our data set
- so now we have our data set there's a couple like good practices
- there's a couple like good practices anytime you get like a data set from a
- anytime you get like a data set from a government or a source to help you just
- government or a source to help you just like gradually begin to understand it
- like gradually begin to understand it right and one of those things is what i
- right and one of those things is what i talked about earlier which is like
- talked about earlier which is like figuring out
- figuring out what is a row right like in the
- what is a row right like in the philosophical sense what is defined by
- philosophical sense what is defined by each row in this data set right and if i
- each row in this data set right and if i start looking at this i see okay i've
- start looking at this i see okay i've got my columns there's a log number
- got my columns there's a log number log no
- log no i've seen a lot of data sets so i'm
- i've seen a lot of data sets so i'm guessing that's a log number right
- guessing that's a log number right there's a date a complaint date there's
- there's a date a complaint date there's the officer's first name last name
- the officer's first name last name their employee number maybe that's their
- their employee number maybe that's their badge number i'm not sure i might have
- badge number i'm not sure i might have to ask somebody to find that out
- to ask somebody to find that out we've got the allegation against them
- we've got the allegation against them which has some descriptions
- which has some descriptions and then we've got the finding code
- and then we've got the finding code which like finding is kind of weird
- which like finding is kind of weird maybe that's the ultimate decision or
- maybe that's the ultimate decision or disposition
- disposition of the case those are all things i'm
- of the case those are all things i'm like guessing right from just like what
- like guessing right from just like what is this i'm smelling the data and like i
- is this i'm smelling the data and like i think that's right right and you can
- think that's right right and you can precede your like initial analysis of a
- precede your like initial analysis of a data set just kind of
- data set just kind of guessing what you know it is but
- guessing what you know it is but ultimately you have to test those
- ultimately you have to test those assumptions against the data itself
- assumptions against the data itself and also just by maybe just asking the
- and also just by maybe just asking the source you know like what is this column
- source you know like what is this column is this is this defining or is that
- is this is this defining or is that that's the result or is it not you know
- that's the result or is it not you know and then one thing is i just like look
- and then one thing is i just like look at the first few rows and i kind of
- at the first few rows and i kind of sniff the data right i notice oh hey
- sniff the data right i notice oh hey those log numbers repeat like we have
- those log numbers repeat like we have look at this this log number is the same
- look at this this log number is the same for like the first 23 records
- for like the first 23 records right
- right and hey roberto lopianco is in here
- and hey roberto lopianco is in here twice with the same number that's that's
- twice with the same number that's that's probably the same guy right
- probably the same guy right so
- so each log so each row isn't a single
- each log so each row isn't a single complaint i don't think because it has
- complaint i don't think because it has multiple people or maybe a complaint can
- multiple people or maybe a complaint can have more than one officer
- have more than one officer yeah that's it probably right so
- yeah that's it probably right so the relationship between records or like
- the relationship between records or like the data is often you have to think
- the data is often you have to think about those relationships are often like
- about those relationships are often like one to one or one to many and so we have
- one to one or one to many and so we have here like one log number that has many
- here like one log number that has many officers right and if you think about it
- officers right and if you think about it that probably means when you file a
- that probably means when you file a complaint you can accuse multiple people
- complaint you can accuse multiple people which passes the common sense test and
- which passes the common sense test and kind of smells okay but you might want
- kind of smells okay but you might want to make sure that you might want to look
- to make sure that you might want to look at the form
- at the form right that's used to file a complaint
- right that's used to file a complaint oftentimes if you don't understand your
- oftentimes if you don't understand your data from a government database getting
- data from a government database getting the form that it comes from can answer
- the form that it comes from can answer those questions faster than asking
- those questions faster than asking anybody right because if you just look
- anybody right because if you just look at the blanks you fill in on any
- at the blanks you fill in on any government form that almost always
- government form that almost always corresponds to fields and tables in a
- corresponds to fields and tables in a database right and so if i was to report
- database right and so if i was to report this out that's what i would look for
- this out that's what i would look for and i always look for that sometimes
- and i always look for that sometimes i'll file a public records request for
- i'll file a public records request for the form right because i can't get
- the form right because i can't get anybody to answer my question and i know
- anybody to answer my question and i know if i get the form i got something you
- if i get the form i got something you know that i can go off that i can have
- know that i can go off that i can have some
- some confidence in right
- confidence in right it'll i'll often get request the forms
- it'll i'll often get request the forms before the database so that i know what
- before the database so that i know what fields to ask for right and that's one
- fields to ask for right and that's one way you can kind of know what they got
- way you can kind of know what they got you know you can reverse engineer the
- you know you can reverse engineer the database
- database and then we see here robert lobianco is
- and then we see here robert lobianco is in here twice what's that about
- in here twice what's that about and if i look at this allegation
- and if i look at this allegation category we see there's different
- category we see there's different allegations so that suggests to me the
- allegations so that suggests to me the complaint has a one-to-many relationship
- complaint has a one-to-many relationship to the officers and then the officers
- to the officers and then the officers have a one-to-many relationship to the
- have a one-to-many relationship to the allegations which also makes sense right
- allegations which also makes sense right so it's like i could
- so it's like i could i could make an allegation against
- i could make an allegation against multiple officers and i could say that
- multiple officers and i could say that each one did multiple things wrong right
- each one did multiple things wrong right and so you can see here that just within
- and so you can see here that just within this one complaint there actually is
- this one complaint there actually is like five or six officers and each one
- like five or six officers and each one of them has multiple allegations against
- of them has multiple allegations against them
- them right
- right okay so we're starting to get a sense of
- okay so we're starting to get a sense of like i would say and this is the
- like i would say and this is the question i was almost what's a row
- question i was almost what's a row what's a row how would i define it i
- what's a row how would i define it i would say the row is an allegation
- would say the row is an allegation that's what i think i would call it
- that's what i think i would call it right so each row is an allegation
- right so each row is an allegation against an officer linked to a complaint
- against an officer linked to a complaint i think
- i think and like that definition of my head
- and like that definition of my head often gets revised as i like better
- often gets revised as i like better understand the data like i was working
- understand the data like i was working with claire and her classmates on a
- with claire and her classmates on a story last year and we thought we was
- story last year and we thought we was about trees in the city of chicago and
- about trees in the city of chicago and there was this one column that was just
- there was this one column that was just like a number and it was just and we
- like a number and it was just and we didn't know what it was and we thought
- didn't know what it was and we thought each row is a tree each row is one tree
- each row is a tree each row is one tree like one right but then we asked them
- like one right but then we asked them hey what's this weird number column they
- hey what's this weird number column they were like oh that's the number of trees
- were like oh that's the number of trees we planted
- we planted and so each row was in order to plant
- and so each row was in order to plant trees
- trees and that number told you how many trees
- and that number told you how many trees there were and so the difference between
- there were and so the difference between just counting each record is one and
- just counting each record is one and summing that numerical thing is the
- summing that numerical thing is the difference between your story being
- difference between your story being right your story being wrong right and
- right your story being wrong right and so
- so doing this kind of data smelling
- doing this kind of data smelling figuring it out what each row is
- figuring it out what each row is really pushing yourself to kind of like
- really pushing yourself to kind of like guess what each column is
- guess what each column is and you can often just say these 10
- and you can often just say these 10 columns i don't even think i need to
- columns i don't even think i need to care about right but
- care about right but you got to do that process
- you got to do that process and then so we kind of got we came up
- and then so we kind of got we came up with our working theory of what a row is
- with our working theory of what a row is right and then another good thing is
- right and then another good thing is just to look at like how much data you
- just to look at like how much data you have and whether it's all filled in or
- have and whether it's all filled in or not right because you have this data set
- not right because you have this data set we don't know how many rows there are we
- we don't know how many rows there are we don't know if there's empty values all
- don't know if there's empty values all over the database it could be incomplete
- over the database it could be incomplete and a really great way to do that is
- and a really great way to do that is just to scroll around the four corners
- just to scroll around the four corners of the database right just to like
- of the database right just to like literally scroll to the bottom and see
- literally scroll to the bottom and see what you've got and like see if stuff is
- what you've got and like see if stuff is filled in so i'm just gonna page down
- filled in so i'm just gonna page down really quick
- really quick see i don't see a lot of empty cells
- see i don't see a lot of empty cells there are some i've noticed look there's
- there are some i've noticed look there's some empty cells that's interesting so
- some empty cells that's interesting so sometimes there isn't a finding well
- sometimes there isn't a finding well that's interesting why wouldn't there be
- that's interesting why wouldn't there be a finding sometimes is it maybe still
- a finding sometimes is it maybe still under investigation
- under investigation maybe right is it um a flaw that they
- maybe right is it um a flaw that they failed to put it in i gotta tuck that
- failed to put it in i gotta tuck that that's that's a data smell right i smell
- that's that's a data smell right i smell that and i'm like okay i'm gonna make a
- that and i'm like okay i'm gonna make a little note for myself in my notepad
- little note for myself in my notepad that might or might not be something
- that might or might not be something that matters down the road but i wanna
- that matters down the road but i wanna just like accrue my little list of like
- just like accrue my little list of like you know status smells so i know
- you know status smells so i know what's going on
- what's going on and you got to get to know your data
- and you got to get to know your data like this you don't want to run too fast
- like this you don't want to run too fast and so you get to the bottom and we can
- and so you get to the bottom and we can see that there's 4
- see that there's 4 338 rows subtract our one header row we
- 338 rows subtract our one header row we know there's 4
- know there's 4 337 complaints right
- 337 complaints right in this like set of data that ben gave
- in this like set of data that ben gave there's other integrity checks you could
- there's other integrity checks you could do like for instance i think this is
- do like for instance i think this is everything in 2014 you might want to do
- everything in 2014 you might want to do totals by month just to make sure we're
- totals by month just to make sure we're not missing a couple months right you
- not missing a couple months right you could do these little integrity checks
- could do these little integrity checks on the data that help you like see kind
- on the data that help you like see kind of what's up with it right
- okay so we kind of smelled it we think it's for 2014 this is all the
- it's for 2014 this is all the allegations made against officers in the
- allegations made against officers in the city
- city now i want to use some of the tricks
- now i want to use some of the tricks that we've covered already to ask and
- that we've covered already to ask and answer some questions right
- answer some questions right so one thing i i want to look at is like
- so one thing i i want to look at is like a story that's been done before so here
- a story that's been done before so here was a story that ran in the chicago
- was a story that ran in the chicago reporter where they've done lots and
- reporter where they've done lots and lots of great data journalism over the
- lots of great data journalism over the years they're having some management
- years they're having some management problems right now like many journalism
- problems right now like many journalism outlets are but the chicago reporter has
- outlets are but the chicago reporter has a tremendous track record of great data
- a tremendous track record of great data journalism especially around issues of
- journalism especially around issues of race and policing and one story they did
- race and policing and one story they did it looks like six years ago is
- it looks like six years ago is they looked at a subsequent release they
- they looked at a subsequent release they looked at this very same data set
- looked at this very same data set right
- right and they
- and they looked at they analyzed something called
- looked at they analyzed something called the affidavit requirement
- the affidavit requirement and what they found
- and what they found looking at like literally the same
- looking at like literally the same spreadsheet that we have that's on the
- spreadsheet that we have that's on the website is they found let me see if we
- website is they found let me see if we can find the finding here
- can find the finding here they found there were 17 000 complaints
- they found there were 17 000 complaints in the three years they looked at which
- in the three years they looked at which really would just be
- really would just be scrolling to the bottom of the data set
- scrolling to the bottom of the data set right in the same way we just did they
- right in the same way we just did they found out investigators didn't open
- found out investigators didn't open cases in 58
- cases in 58 because they were marked no affidavit
- because they were marked no affidavit well that looks familiar i see that
- well that looks familiar i see that right here no affidavit right that's in
- right here no affidavit right that's in my finding code
- my finding code and if you read more into it it turns
- and if you read more into it it turns out that
- out that under the the rules of how police
- under the the rules of how police complaints work which were hammered out
- complaints work which were hammered out in a past labor contract negotiation
- in a past labor contract negotiation with the police union um
- with the police union um i'm not a lawyer so i might not get this
- i'm not a lawyer so i might not get this exactly right but my understanding is is
- exactly right but my understanding is is you can't just file a complaint you also
- you can't just file a complaint you also have to file a legal affidavit where you
- have to file a legal affidavit where you kind of swear that what you're saying is
- kind of swear that what you're saying is what really happened which is a kind of
- what really happened which is a kind of hoop a procedural hoop that each
- hoop a procedural hoop that each complainant has to jump through for
- complainant has to jump through for their complaint to actually be
- their complaint to actually be investigated by the police and it turns
- investigated by the police and it turns out that it's really really common
- out that it's really really common for that to be done according to the
- for that to be done according to the analysis of this group and then this
- analysis of this group and then this piece kind of has a little bit of an
- piece kind of has a little bit of an opinion angle to it where it's saying if
- opinion angle to it where it's saying if they were to get rid of that requirement
- they were to get rid of that requirement it would lead to a lot more
- it would lead to a lot more investigations and a lot more discipline
- investigations and a lot more discipline against police is kind of potential
- against police is kind of potential right is what the story says
- right is what the story says so
- so if i wanted to do a similar analysis for
- if i wanted to do a similar analysis for this data set i want to figure out
- this data set i want to figure out of the what was the number again of the
- ah here's a fun trick if you hit control
- here's a fun trick if you hit control down control and hit up it jumps to the
- down control and hit up it jumps to the last record of our 4
- last record of our 4 37 complaints what percentage were no
- 37 complaints what percentage were no affidavit right like that's what i want
- affidavit right like that's what i want to figure out and let's say you filed a
- to figure out and let's say you filed a foia and got last year's complaints and
- foia and got last year's complaints and you were the first reporter to get it
- you were the first reporter to get it you could run this analysis to like
- you could run this analysis to like update this old story right so if i
- update this old story right so if i wanted to do that i want to know what's
- wanted to do that i want to know what's the number
- the number of
- of allegations
- allegations that that were
- that that were um dropped because they were no
- um dropped because they were no affidavit
- affidavit no affidavit was filed and i want to
- no affidavit was filed and i want to know that as a percentage of the total
- know that as a percentage of the total what trick that we've used so far would
- what trick that we've used so far would help us get there who can think of
- help us get there who can think of something
- it's the pivot table guys right because we've got our categorical data that we
- we've got our categorical data that we want to group and count we want to know
- want to group and count we want to know for each of the findings how many there
- for each of the findings how many there are right
- are right and so i'm going to click in the upper
- and so i'm going to click in the upper left on the selector to select my whole
- left on the selector to select my whole data set
- data set i'm going to do insert
- i'm going to do insert and then my buddy pivot table
- and then my buddy pivot table oh yeah
- selected new sheet
- sheet okay so now i want to group by the
- okay so now i want to group by the finding code column who can tell me how
- finding code column who can tell me how to do that
- finding code and then under row yes we got our columns here i grab drag that
- got our columns here i grab drag that and i drop it into row and so that's one
- and i drop it into row and so that's one per row and so we can now see
- per row and so we can now see that these are all the potential values
- that these are all the potential values there's some empty rows as we saw when
- there's some empty rows as we saw when we were scrolling
- we were scrolling there's some that are under
- there's some that are under additional investigation required
- additional investigation required some exonerated no affidavit there it is
- some exonerated no affidavit there it is not sustained sustained which means they
- not sustained sustained which means they said it was legit the complaint right
- said it was legit the complaint right and then unfounded right and you might
- and then unfounded right and you might need to report out what are all these
- need to report out what are all these values right you know and that's part of
- values right you know and that's part of the process of getting to know your data
- the process of getting to know your data and so i just want to count
- and so i just want to count how many allegations
- how many allegations there are
- there are for each one of these groups so how do i
- for each one of these groups so how do i do that
- do that pull finding code to values and do by
- pull finding code to values and do by count a
- count a yeah that would work so if i take
- yeah that would work so if i take finding code the same column drop it
- finding code the same column drop it there and leave it as count a
- there and leave it as count a we can see that that instantly fills in
- we can see that that instantly fills in right and we can see that there are two
- right and we can see that there are two rows where additional investigation is
- rows where additional investigation is required
- required 297
- 297 right
- right where they're exonerated and we see
- where they're exonerated and we see there's
- there's 1286 that are no affidavit out of our
- 1286 that are no affidavit out of our total of four two two three now wait
- total of four two two three now wait hold on a second four two two three is
- hold on a second four two two three is our total and this is part of the data
- our total and this is part of the data smells it's like didn't we have more
- smells it's like didn't we have more rows than that when i was looking
- rows than that when i was looking right well if i go here and hit control
- right well if i go here and hit control to the bottom
- to the bottom there were
- there were 4
- 4 38 so shouldn't there be a little more
- 38 so shouldn't there be a little more than that does anybody know why that why
- than that does anybody know why that why we have that difference
- we have that difference there's the ones that are potentially
- there's the ones that are potentially still being investigated so the blank
- still being investigated so the blank their left is empty and i think it's i
- their left is empty and i think it's i think i didn't anticipate this but i
- think i didn't anticipate this but i think that's because we use the finding
- think that's because we use the finding code for our value
- code for our value i said and those that's empty in some
- i said and those that's empty in some cases and in the cases where it's empty
- cases and in the cases where it's empty it's not counting it so i bet if we take
- it's not counting it so i bet if we take log no which we know is filled in for
- log no which we know is filled in for every row put that in and do count a
- every row put that in and do count a look at that we get the total right and
- look at that we get the total right and we see now that there's 114 that are
- we see now that there's 114 that are blank you see that at the top there
- blank you see that at the top there and so that was just like a little weird
- and so that was just like a little weird wrinkle of how google sheets works right
- wrinkle of how google sheets works right if we did our value in our account using
- if we did our value in our account using a column that had null or empty cells
- a column that had null or empty cells right it didn't count the null or empty
- right it didn't count the null or empty ones right is that really the end of the
- ones right is that really the end of the world no
- world no in this case right and you might say
- in this case right and you might say well for the purposes of this analysis i
- well for the purposes of this analysis i might want to subtract those right like
- might want to subtract those right like when i calculate the percentage if
- when i calculate the percentage if they're still in progress cases so it
- they're still in progress cases so it might actually be better to exclude them
- might actually be better to exclude them methodologically but
- methodologically but the lesson here i think to take home is
- the lesson here i think to take home is that a little slip of the finger or the
- that a little slip of the finger or the data the software not doing exactly what
- data the software not doing exactly what you expected to do can sometimes change
- you expected to do can sometimes change the analysis in a way that maybe you
- the analysis in a way that maybe you don't even notice right and so that's
- don't even notice right and so that's why when when you work with these pivot
- why when when you work with these pivot tables
- tables and other programming tools it's
- and other programming tools it's important that every step to just stop
- important that every step to just stop and look and be like did these numbers
- and look and be like did these numbers add up
- add up is this what i would expect it to do
- is this what i would expect it to do because you'll often find it's not even
- because you'll often find it's not even your fault like in this case it was just
- your fault like in this case it was just kind of a weird thing and the way i was
- kind of a weird thing and the way i was able to do that here just to recreate it
- able to do that here just to recreate it is i just looked at the total
- is i just looked at the total right
- right and i said well wait wasn't it supposed
- and i said well wait wasn't it supposed to be 4 300 or something and the reason
- to be 4 300 or something and the reason i even had that thought is because i did
- i even had that thought is because i did my four corners check right and i tucked
- my four corners check right and i tucked away in my head
- away in my head that's what i think it should be and
- that's what i think it should be and that kind of back and forth of like
- that kind of back and forth of like fumbling with it is really just part of
- fumbling with it is really just part of the process and i've been doing this
- the process and i've been doing this nearly 20 years and like i do this on
- nearly 20 years and like i do this on everything i'm working on like on that
- everything i'm working on like on that trees thing last year i probably screwed
- trees thing last year i probably screwed up four or five things and we run around
- up four or five things and we run around in circles like do i really know what
- in circles like do i really know what this is and like that struggle is really
- this is and like that struggle is really just part of the process right and the
- just part of the process right and the fact that you're doing that is what's
- fact that you're doing that is what's separating you from the competition
- separating you from the competition right you're actually figuring it out
- right you're actually figuring it out and you're going to get a story somebody
- and you're going to get a story somebody else doesn't right and so when
- else doesn't right and so when it was when i feel that frustration i'm
- it was when i feel that frustration i'm as frustrated as anybody 20 years in i
- as frustrated as anybody 20 years in i still feel the frustration every day but
- still feel the frustration every day but i've got to the point where i realize i
- i've got to the point where i realize i have the zen
- have the zen that like oh that's good right it's
- that like oh that's good right it's better that i had the frustration before
- better that i had the frustration before the story came out than after the story
- the story came out than after the story came out it's
- came out it's right because i want to know it ahead of
- right because i want to know it ahead of time of course so i'm right
- time of course so i'm right and i don't have to write a correction
- and i don't have to write a correction right but also it's usually a sign that
- right but also it's usually a sign that you're kind of you're
- you're kind of you're cutting some new territory right you're
- cutting some new territory right you're there maybe beyond everybody else so
- there maybe beyond everybody else so that's good
- that's good okay so now we still haven't quite
- okay so now we still haven't quite answered the question we know there's
- answered the question we know there's 1286 no affidavits we know that's the
- 1286 no affidavits we know that's the total oh wait let me go back to the log
- total oh wait let me go back to the log number so that uh
- number so that uh we have the full count
- we have the full count okay so now how using only tricks we've
- okay so now how using only tricks we've learned before but using them a little
- learned before but using them a little bit differently how can we calculate the
- bit differently how can we calculate the percentage of cases that are no
- percentage of cases that are no affidavit anybody
- i just want to put it right there in c5 i want to calculate it what do i do
- so how do you start a formula who remembers
- rules equals so you type equals you can really just start riff and math so
- can really just start riff and math so the percentage is just this
- the percentage is just this right
- right divided into this right
- divided into this right boom
- boom 0.29 hit the percentage sign
- 0.29 hit the percentage sign 29
- 29 we're no affidavit
- we're no affidavit right
- right and so that's 29
- and so that's 29 of allegations right now if you look at
- of allegations right now if you look at the story i think they actually don't do
- the story i think they actually don't do percent of allegations they do percent
- percent of allegations they do percent of cases
- of cases right and this is where the relationship
- right and this is where the relationship in your data and how you think about it
- in your data and how you think about it can really vary and a case isn't defined
- can really vary and a case isn't defined by a row in our data set right a case is
- by a row in our data set right a case is defined by the log node or so we think
- defined by the log node or so we think we have to report that out and so really
- we have to report that out and so really we would want to get into like the
- we would want to get into like the unique log nodes or something maybe a
- unique log nodes or something maybe a little more sophisticated and if so if
- little more sophisticated and if so if we say rather than counting all records
- we say rather than counting all records only count the unique log numbers right
- only count the unique log numbers right after you've grouped and then how many
- after you've grouped and then how many unique log numbers are in each group we
- unique log numbers are in each group we can see that while we may have 4 000
- can see that while we may have 4 000 allegations we only have 1200 complaints
- allegations we only have 1200 complaints right and this is again thinking
- right and this is again thinking conceptually about what your data is
- conceptually about what your data is right and you'll notice that in in these
- right and you'll notice that in in these stories as you read them how that's
- stories as you read them how that's managed is really part of kind of
- managed is really part of kind of getting it right or being artful and
- getting it right or being artful and precise with your data and so i might
- precise with your data and so i might say if my initial hunches hold up that
- say if my initial hunches hold up that 34
- 34 of complaints had no affidavit
- of complaints had no affidavit but that
- but that 29
- 29 of allegations
- of allegations right uh had no affidavit and this kind
- right uh had no affidavit and this kind of subtlety of like how you think about
- of subtlety of like how you think about or work it really is important to be
- or work it really is important to be thinking about it every step of your
- thinking about it every step of your analysis a lot of journalists who do
- analysis a lot of journalists who do this keep what they call a data diary
- this keep what they call a data diary which is where they literally write down
- which is where they literally write down kind of the decisions they're making the
- kind of the decisions they're making the questions that are coming up as they go
- questions that are coming up as they go along and then they go back to that
- along and then they go back to that again and again and oftentimes you do a
- again and again and oftentimes you do a lot of sort of doodles and curly cues as
- lot of sort of doodles and curly cues as you mess around with the data and then
- you mess around with the data and then you figure out well this is the number i
- you figure out well this is the number i want to report or i want to build my
- want to report or i want to build my story around and then you return to that
- story around and then you return to that diary to the process all the steps you
- diary to the process all the steps you took and you try to make sure is
- took and you try to make sure is everything i'm doing on each step like
- everything i'm doing on each step like exactly what i need it to be you know
- exactly what i need it to be you know what i mean for this to be right are
- what i mean for this to be right are there any shaky assumptions i'm making
- there any shaky assumptions i'm making is there anything about the structure of
- is there anything about the structure of the data that i'm unsure about or it
- the data that i'm unsure about or it doesn't feel right or seem right
- doesn't feel right or seem right is the language i'm using to describing
- is the language i'm using to describing it correct that's really the kind of
- it correct that's really the kind of bulletproofing process of data
- bulletproofing process of data journalism that is similar to
- journalism that is similar to bulletproofing other stories but just
- bulletproofing other stories but just has a more technical nature and for me
- has a more technical nature and for me personally having done this more and
- personally having done this more and more this is where computer programming
- more this is where computer programming comes into it for me because what i like
- comes into it for me because what i like to do as because i've gotten nerdier is
- to do as because i've gotten nerdier is literally write computer code that
- literally write computer code that executes each step of the data
- executes each step of the data transformation so that i can really
- transformation so that i can really carefully regulate what i do with the
- carefully regulate what i do with the data right
- data right and know each step and then i can go
- and know each step and then i can go back and go over it and go over and go
- back and go over it and go over and go over it until i feel confident that i
- over it until i feel confident that i have a strong and that's harder to do
- have a strong and that's harder to do when you're working in a spreadsheet
- when you're working in a spreadsheet because you really have like the
- because you really have like the spreadsheet's like a machete right it's
- spreadsheet's like a machete right it's not a scalpel you're sort of hacking at
- not a scalpel you're sort of hacking at the data moving it around trying a lot
- the data moving it around trying a lot of different things with pivots and you
- of different things with pivots and you can sometimes get lost in it or make a
- can sometimes get lost in it or make a mistake but there's ways to avoid that
- mistake but there's ways to avoid that too like one really common thing
- too like one really common thing stress is you know you should always
- stress is you know you should always keep a pure and unedited copy of your
- keep a pure and unedited copy of your data
- data that you have not added any columns to
- that you have not added any columns to or much around with so you're always
- or much around with so you're always able to retrace your footsteps back to
- able to retrace your footsteps back to where you began right it could live as
- where you began right it could live as an email attachment from a government
- an email attachment from a government official that's fine you might want to
- official that's fine you might want to save it to your computer but you don't
- save it to your computer but you don't want to end up in a situation where you
- want to end up in a situation where you lose track of where you started because
- lose track of where you started because then it can be really hard to find
- then it can be really hard to find mistakes or make sure verify that what
- mistakes or make sure verify that what you've done is correct
- you've done is correct i think we're getting to the point where
- i think we're getting to the point where i'm supposed to stop spilling and so i
- i'm supposed to stop spilling and so i figured i would stop with like a really
- figured i would stop with like a really um sanctimonious moral lesson like that
- um sanctimonious moral lesson like that was
- was uh which i i appreciate you guys sort of
- uh which i i appreciate you guys sort of sitting you know
- sitting you know enduring but that's kind of the spiel
- enduring but that's kind of the spiel there's a lot of other questions and
- there's a lot of other questions and stuff we could answer in the spreadsheet
- stuff we could answer in the spreadsheet but i don't want to go on too long and i
- but i don't want to go on too long and i want to give us time to just have kind
- want to give us time to just have kind of a
- of a discussion where we can answer
- discussion where we can answer specific technical questions you have or
- specific technical questions you have or just have a broader conversation about
- just have a broader conversation about data journalism chicago depaul
- data journalism chicago depaul the cubs chances this fall
- the cubs chances this fall whatever you want to get into claire
- whatever you want to get into claire what do you think
- what do you think yeah that sounds good i think my video
- yeah that sounds good i think my video might have gone out there for a second
- might have gone out there for a second it was pretty funny but uh yeah i
- it was pretty funny but uh yeah i i like to take these last 15 minutes for
- i like to take these last 15 minutes for us to do a q a
- us to do a q a um as ben said if you guys have any
- um as ben said if you guys have any questions for him or about his work or
- questions for him or about his work or about data driven reporting and data
- about data driven reporting and data journalism so haley it looks like you
- journalism so haley it looks like you have a question go ahead
- have a question go ahead yeah so my first well my question is you
- yeah so my first well my question is you know if you create so if you find this
- know if you create so if you find this number by yourself say so we find like
- number by yourself say so we find like that 29
- that 29 um number
- um number if you don't
- if you don't if you s you know you put this in a
- if you s you know you put this in a piece that you're working on and you
- piece that you're working on and you publish it you say this is the number
- publish it you say this is the number that i came up with
- that i came up with are people going to be critical of that
- are people going to be critical of that if you're not like you know what i mean
- if you're not like you know what i mean if you're not citing somewhere else well
- if you're not citing somewhere else well i mean like you're definitely you're
- i mean like you're definitely you're climbing out on a limb every time you do
- climbing out on a limb every time you do it right and i think a lot of in
- it right and i think a lot of in journalism we're often taking numbers
- journalism we're often taking numbers from other people and kind of passing
- from other people and kind of passing them along but you know
- them along but you know the most i think often impactful and
- the most i think often impactful and original data journalism stories are
- original data journalism stories are calculating their own findings right
- calculating their own findings right yeah but i think but you know you have
- yeah but i think but you know you have with that with that comes a higher
- with that with that comes a higher degree of rigor and responsibility
- degree of rigor and responsibility that's necessary yeah and so for me it's
- that's necessary yeah and so for me it's really like i was saying understanding
- really like i was saying understanding every step you're making in the process
- every step you're making in the process uh making sure you fully grasp and
- uh making sure you fully grasp and understand your data what is each row
- understand your data what is each row what's in the data set what's left out
- what's in the data set what's left out of the data set there's stuff we didn't
- of the data set there's stuff we didn't cover like maybe we need to exclude some
- cover like maybe we need to exclude some records right or maybe you need to like
- records right or maybe you need to like really carefully frame your findings so
- really carefully frame your findings so that you don't overstate it or say it
- that you don't overstate it or say it kind of wrong you know what i mean yeah
- kind of wrong you know what i mean yeah refinement process is hard especially
- refinement process is hard especially when you're starting but once you get
- when you're starting but once you get the hang of it it's not so bad i mean
- the hang of it it's not so bad i mean obviously i think you if you're you know
- obviously i think you if you're you know a lot of data journalism will get into
- a lot of data journalism will get into the investigative space you know and
- the investigative space you know and once you're there
- once you're there um you know i think you really want to
- um you know i think you really want to go above and beyond to give people an
- go above and beyond to give people an opportunity to respond you know i've
- opportunity to respond you know i've done you know not like i'm mr data
- done you know not like i'm mr data journalism or whatever but like i've
- journalism or whatever but like i've done stories that have gotten people
- done stories that have gotten people fired
- fired or
- or put in the newspaper that uh uh
- put in the newspaper that uh uh something is dangerous or you know like
- something is dangerous or you know like i did a story like this is the most
- i did a story like this is the most dangerous helicopter kind of thing
- dangerous helicopter kind of thing and like and in those circumstances
- and like and in those circumstances i always make sure that the subject the
- i always make sure that the subject the people i'm writing about see everything
- people i'm writing about see everything i'm writing about before it's published
- i'm writing about before it's published and they have an opportunity to respond
- and they have an opportunity to respond to
- to um every basically the analysis itself
- um every basically the analysis itself so in the case of the helicopter company
- so in the case of the helicopter company where we're going to publish a story
- where we're going to publish a story that is effectively
- that is effectively ben says this is the most dangerous
- ben says this is the most dangerous helicopter in the world
- helicopter in the world the actual computer code that i wrote
- the actual computer code that i wrote um i gave to the helicopter company
- um i gave to the helicopter company ahead of time here's how i calculated
- ahead of time here's how i calculated this i wrote out effectively a memo that
- this i wrote out effectively a memo that was sent to them an email saying these
- was sent to them an email saying these will be our key claims
- will be our key claims and here's a bullet point summary of how
- and here's a bullet point summary of how i arrived at those claims and you know
- i arrived at those claims and you know in that case what we did is we modeled
- in that case what we did is we modeled our study on one that had been done by
- our study on one that had been done by the faa in the past so you know one way
- the faa in the past so you know one way in investigations to deal with this
- in investigations to deal with this issue is to look for outside standards
- issue is to look for outside standards and outside methods that you're not just
- and outside methods that you're not just like inventing you know so yeah that
- like inventing you know so yeah that makes sense my method of calculating how
- makes sense my method of calculating how the helicopters were dangerous was an
- the helicopters were dangerous was an accident rate that was something that
- accident rate that was something that the faa itself had done like 30 years
- the faa itself had done like 30 years ago and so i recreated effectively
- ago and so i recreated effectively something that had already been done by
- something that had already been done by the agency and then in that process
- the agency and then in that process i went to the agency i had an interview
- i went to the agency i had an interview with them and i said i did this analysis
- with them and i said i did this analysis i was on the phone with their data guy
- i was on the phone with their data guy and i filtered this and i grouped that
- and i filtered this and i grouped that and i joined this
- and i joined this right is there anything i'm getting
- right is there anything i'm getting wrong or missing and you give them an
- wrong or missing and you give them an opportunity to tell you you're wrong so
- opportunity to tell you you're wrong so you did all that stuff behind the scenes
- you did all that stuff behind the scenes before the story came out
- before the story came out and so recreating experiments or things
- and so recreating experiments or things that other people have already done that
- that other people have already done that have sort of like some justification is
- have sort of like some justification is good also using standards that are sort
- good also using standards that are sort of objective or defined by the
- of objective or defined by the um
- um defined by
- defined by the subject themselves right so i've
- the subject themselves right so i've done stories about the 911 system in la
- done stories about the 911 system in la and like you can calculate the stats and
- and like you can calculate the stats and say it's slow but it's like slow
- say it's slow but it's like slow according to who
- according to who yeah according to you yeah no well
- yeah according to you yeah no well actually there's like a group of fire
- actually there's like a group of fire chiefs who meet and set the standards
- chiefs who meet and set the standards and i'm judging you against that
- and i'm judging you against that right and so finding an outside standard
- right and so finding an outside standard is often really key
- is often really key to all investigative stories like did
- to all investigative stories like did someone break the law or not would be a
- someone break the law or not would be a classic example right
- classic example right but can be really helpful when defining
- but can be really helpful when defining your data methodology of kind of what
- your data methodology of kind of what your target is right and i think that
- your target is right and i think that these complaint stories kind of have an
- these complaint stories kind of have an issue there because you can say well
- issue there because you can say well only a small number of complaints
- only a small number of complaints are um upheld right you can put that in
- are um upheld right you can put that in the paper it's x percent but like is
- the paper it's x percent but like is that how do we know that's good or bad
- that how do we know that's good or bad you really don't right and that and that
- you really don't right and that and that is i think
- is i think one reason why the stories stories have
- one reason why the stories stories have great impact they're great stories i'm
- great impact they're great stories i'm jealous of them but they're missing that
- jealous of them but they're missing that standard piece that really helps you hit
- standard piece that really helps you hit in a lot of cases you know
- yeah yeah context context context context so important
- context so important um nadia i think you have a question
- um nadia i think you have a question yeah hi thank you so much for um
- yeah hi thank you so much for um speaking with us today um i did have a
- speaking with us today um i did have a question so
- question so typically i
- typically i don't do math and i don't really do data
- don't do math and i don't really do data um but i'm wondering like kind of what
- um but i'm wondering like kind of what you were saying in that like last part
- you were saying in that like last part of your answer to the question about how
- of your answer to the question about how kind of data journalism and also but
- kind of data journalism and also but like data can kind of like impact and
- like data can kind of like impact and strengthen like a story so how would you
- strengthen like a story so how would you say
- say would be a good way to insert like a
- would be a good way to insert like a little bit of data journalism in a story
- little bit of data journalism in a story to kind of strengthen it without take
- to kind of strengthen it without take without making it like the focus of a
- without making it like the focus of a story yeah i mean i mean to me looking
- story yeah i mean i mean to me looking to it for context and just like a little
- to it for context and just like a little bit of background is where it's most
- bit of background is where it's most commonly used like hey i'm writing the
- commonly used like hey i'm writing the story about the chief of police saying
- story about the chief of police saying homicides are up and it's a big problem
- homicides are up and it's a big problem in chicago and it definitely is and i
- in chicago and it definitely is and i wouldn't want to minimize that but that
- wouldn't want to minimize that but that story would benefit from context of like
- story would benefit from context of like well okay it's up over this year but how
- well okay it's up over this year but how does it compare to 20 years ago or like
- does it compare to 20 years ago or like what's the general trend it may be a
- what's the general trend it may be a chart right and so i think like
- chart right and so i think like background and context is like the most
- background and context is like the most common thing you know so like often the
- common thing you know so like often the thing that's in the news is a really
- thing that's in the news is a really short time frame and just like
- short time frame and just like stretching that out is good
- stretching that out is good um i think you know understanding uh
- um i think you know understanding uh something sense of proportion you know
- something sense of proportion you know what i mean so like oh hospitalizations
- what i mean so like oh hospitalizations are really up for coven among young
- are really up for coven among young people but how many young people are
- people but how many young people are actually being hospitalized like the
- actually being hospitalized like the actual number right and so i think uh
- actual number right and so i think uh context over time and kind of contest of
- context over time and kind of contest of like in in the grand scheme of things
- like in in the grand scheme of things you know what i mean are probably the
- you know what i mean are probably the two most common data things if i had to
- two most common data things if i had to i guess guess right um i also think
- i guess guess right um i also think let's say you cover education or you
- let's say you cover education or you cover the hospital system or you cover
- cover the hospital system or you cover whatever i think you could put on a sort
- whatever i think you could put on a sort of metaphorical set of database glasses
- of metaphorical set of database glasses and kind of look at your beat and say
- and kind of look at your beat and say well what's the data gathered on this
- well what's the data gathered on this beat that i could maybe use to bring
- beat that i could maybe use to bring some accountability or context and
- some accountability or context and perspective to a story education's a
- perspective to a story education's a classic example there's just so much
- classic example there's just so much education data of all different shapes
- education data of all different shapes and sizes that i think probably every
- and sizes that i think probably every education reporter in america does some
- education reporter in america does some data right it's like kind of whether
- data right it's like kind of whether it's test scores or
- it's test scores or dropped enrollment after covid or you
- dropped enrollment after covid or you name it right you know there's just a
- name it right you know there's just a lot of data inequity right is also a
- lot of data inequity right is also a classic data frame
- classic data frame and
- and and i think i think looking at your beat
- and i think i think looking at your beat and saying well what's the data gathered
- and saying well what's the data gathered by this
- by this this thing i'm covering or what are the
- this thing i'm covering or what are the goals and standards that they claim that
- goals and standards that they claim that they're reaching for or upholding and
- they're reaching for or upholding and can i use data to measure whether that's
- can i use data to measure whether that's really the case or not
- anyone else have any questions you can either drop them in the chat or go ahead
- either drop them in the chat or go ahead and
- yeah i have one um so you've been doing this for a while you've been doing it
- this for a while you've been doing it for a minute um
- for a minute um and i mean i don't know what depaul's
- and i mean i don't know what depaul's program journalism program was like 20
- program journalism program was like 20 years ago but i'm guessing it didn't
- years ago but i'm guessing it didn't have a super strong data journalism
- have a super strong data journalism program so i guess how have you learned
- program so i guess how have you learned and picked up all of these different
- and picked up all of these different data skills over the past two decades
- data skills over the past two decades have you like just gotten a thousand
- have you like just gotten a thousand degrees
- degrees from people in newsrooms like what's
- from people in newsrooms like what's what's your secret sure
- what's your secret sure um
- um you're right yeah so when i went to
- you're right yeah so when i went to depaul there was it was just the college
- depaul there was it was just the college of communication i actually answered
- of communication i actually answered phones at the front desk
- phones at the front desk uh that was my gig
- uh that was my gig um
- um there was no data journalism program
- there was no data journalism program no
- no uh yes this was on the fifth floor of
- uh yes this was on the fifth floor of sac i don't think it's still there right
- sac i don't think it's still there right i don't know but that's where that's
- i don't know but that's where that's where the department used to be up at
- where the department used to be up at the top and
- the top and use the whole loop thing now and it's
- use the whole loop thing now and it's you know but um
- you know but um no they're real very few places that any
- no they're real very few places that any data journalism programs then data
- data journalism programs then data journalism in the united states really
- journalism in the united states really was kind of a grassroots movement that
- was kind of a grassroots movement that grew out of the 60s and 70s and really
- grew out of the 60s and 70s and really not unlike
- not unlike hackers who made computers in silicon
- hackers who made computers in silicon valley right there was sort of just like
- valley right there was sort of just like a a niche of people who saw the
- a a niche of people who saw the potential of the computer and began to
- potential of the computer and began to work it into their profession and in
- work it into their profession and in journalism that was really the it was
- journalism that was really the it was really an investigative tradition to
- really an investigative tradition to start with it's like we want to look at
- start with it's like we want to look at the census data using a big crazy
- the census data using a big crazy computer or we want to survey our
- computer or we want to survey our readers about what they think about the
- readers about what they think about the detroit riots in 1968 that was a really
- detroit riots in 1968 that was a really big early data journalism story or we
- big early data journalism story or we want to investigate the county
- want to investigate the county courthouse for racial inequities right
- courthouse for racial inequities right these and and a sort of small tribe of
- these and and a sort of small tribe of investigative journalists began
- investigative journalists began developing those skills in the 60s 70s
- developing those skills in the 60s 70s 80s and then they formed actually a sort
- 80s and then they formed actually a sort of
- of a non-profit group that's mission was to
- a non-profit group that's mission was to train journalists in what was then
- train journalists in what was then called computer assisted reporting
- called computer assisted reporting and that became a group at the
- and that became a group at the university of missouri which is the
- university of missouri which is the national institute for computer assisted
- national institute for computer assisted reporting which is kind of a ridiculous
- reporting which is kind of a ridiculous name today you know it's like
- name today you know it's like everyone is computer assisted in
- everyone is computer assisted in everything they do we don't call it
- everything they do we don't call it computer assisted photography right or
- computer assisted photography right or whatever uh but um so that's data
- whatever uh but um so that's data journalism has kind of replaced that as
- journalism has kind of replaced that as a term but um after i finished it depaul
- a term but um after i finished it depaul i went to the university of missouri to
- i went to the university of missouri to graduate school and i was a graduate
- graduate school and i was a graduate assistant at this place nikar and as a
- assistant at this place nikar and as a graduate assistant working there i was
- graduate assistant working there i was able to work on stories with newsrooms
- able to work on stories with newsrooms that partnered with it and spend a year
- that partnered with it and spend a year and a half at mizzou really focusing and
- and a half at mizzou really focusing and that's really where i began to learn how
- that's really where i began to learn how to code and i really began to learn and
- to code and i really began to learn and i met a lot of people who did this field
- i met a lot of people who did this field now since i graduated it's really become
- now since i graduated it's really become much more institutionalized paul has a
- much more institutionalized paul has a class there's many many graduate
- class there's many many graduate programs that explicitly focus on data
- programs that explicitly focus on data journalism right now that just didn't
- journalism right now that just didn't exist then and i think that's great and
- exist then and i think that's great and i think that people are getting a lot
- i think that people are getting a lot more education on it but i really i was
- more education on it but i really i was lucky to benefit from like the one
- lucky to benefit from like the one program that existed at the time that
- program that existed at the time that kind of got me into it
- kind of got me into it and graduate school was great for me you
- and graduate school was great for me you know um i
- know um i mizzou was great for me um you know it's
- mizzou was great for me um you know it's got a great program in it the tradition
- got a great program in it the tradition is there um you know i would just say
- is there um you know i would just say it's it's compared to other graduate
- it's it's compared to other graduate schools very very inexpensive
- schools very very inexpensive um you can see the chart in the wall
- um you can see the chart in the wall street journal about this mizzou is
- street journal about this mizzou is probably the least expensive journalism
- probably the least expensive journalism graduate school
- graduate school in its class you know what i mean by a
- in its class you know what i mean by a long margin so i was able to go in an
- long margin so i was able to go in an inexpensive way and it really worked out
- inexpensive way and it really worked out for me and then i just kind of got jobs
- for me and then i just kind of got jobs and learned from people i work with i
- and learned from people i work with i was lucky to start off in non-profit
- was lucky to start off in non-profit news at the center for public integrity
- news at the center for public integrity which is something that's also changed
- which is something that's also changed since then but i really do think
- since then but i really do think nonprofit news is a great place to start
- nonprofit news is a great place to start out because um the newsrooms tend to be
- out because um the newsrooms tend to be smaller they tend to be more projects
- smaller they tend to be more projects focused so you can like there's not
- focused so you can like there's not quite so much of a hurry so they're not
- quite so much of a hurry so they're not great places to learn how to like write
- great places to learn how to like write right right you know what i mean because
- right right you know what i mean because they oftentimes don't move as quickly
- they oftentimes don't move as quickly but they are great places to learn how
- but they are great places to learn how to do projects to learn how to do
- to do projects to learn how to do investigations to learn how to like
- investigations to learn how to like um put together something that goes
- um put together something that goes beyond the average story so i would
- beyond the average story so i would really recommend them as a place to look
- really recommend them as a place to look for a first job
- for a first job um or whatever because you can be kind
- um or whatever because you can be kind of shielded from some of the
- of shielded from some of the insanity of a larger newsroom oftentimes
- insanity of a larger newsroom oftentimes but different people want different
- but different people want different things in their career but for a data
- things in their career but for a data person it's a great place to start it
- person it's a great place to start it was for me
- great thank you [Music]
- well i have one um so ben what would you recommend for people today who
- recommend for people today who maybe are just now getting introduced or
- maybe are just now getting introduced or this might be their first introduction
- this might be their first introduction into data journalism and spreadsheets
- into data journalism and spreadsheets where do you think they should go to
- where do you think they should go to continue refining the skills that they
- continue refining the skills that they learned today what would be the next
- learned today what would be the next step i think putting on if you're
- step i think putting on if you're covering something like in 14 east or
- covering something like in 14 east or the depaul or somewhere else i think
- the depaul or somewhere else i think really putting on the database glasses
- really putting on the database glasses and saying well i'm covering this topic
- and saying well i'm covering this topic like can i come up with a story where is
- like can i come up with a story where is there some data on this beat that i can
- there some data on this beat that i can like try to do something with and that
- like try to do something with and that could just be you can look at it as just
- could just be you can look at it as just even a breaking news story like some new
- even a breaking news story like some new data gets released what does it say i
- data gets released what does it say i think covet data right now is a great
- think covet data right now is a great opportunity to do this you know because
- opportunity to do this you know because it's just
- it's just you know obviously not the crazy story
- you know obviously not the crazy story it was two years ago but still a really
- it was two years ago but still a really big story and there's probably an
- big story and there's probably an opportunity to find some angle on it if
- opportunity to find some angle on it if you begin to
- you begin to to look at it it's finding that data on
- to look at it it's finding that data on your bead or something coming and then
- your bead or something coming and then really treating it like a source and
- really treating it like a source and like challenging yourself with to come
- like challenging yourself with to come up with questions to answer that ask the
- up with questions to answer that ask the data and then using some of these basic
- data and then using some of these basic skills we covered to try to answer them
- skills we covered to try to answer them and even if you don't have writing a
- and even if you don't have writing a story just that practice of like finding
- story just that practice of like finding the data on the beat
- the data on the beat asking a question answering it is really
- asking a question answering it is really just how you can start to strengthen
- just how you can start to strengthen your muscles and kind of get more
- your muscles and kind of get more comfortable
- comfortable with this kind of approach
- i actually have one more question and i hope it's not too personal
- hope it's not too personal um
- um where's pale wire from what what was the
- where's pale wire from what what was the inspiration for that
- inspiration for that this is my online handle so like
- this is my online handle so like yeah so like i'm old enough you know i'm
- yeah so like i'm old enough you know i'm gray-haired enough that like i i joined
- gray-haired enough that like i i joined the internet in the early 1990s
- the internet in the early 1990s when it was still
- when it was still it was maybe even considered cool as
- it was maybe even considered cool as weird as that it sound to have like a
- weird as that it sound to have like a handle or a username that you kept
- handle or a username that you kept and i think gradually over time it's
- and i think gradually over time it's probably been a good development that
- probably been a good development that people just use their names you know um
- people just use their names you know um but i've kind of just always been
- but i've kind of just always been attached to mine because i feel like
- attached to mine because i feel like it's kind of a marker of where i came
- it's kind of a marker of where i came from and my whatever and it's also a
- from and my whatever and it's also a little bit of an internet brand but it
- little bit of an internet brand but it actually it's pretty pretentious you
- actually it's pretty pretentious you know it comes from william shakespeare
- know it comes from william shakespeare um so uh timon of athens is a you know
- um so uh timon of athens is a you know shakespeare play that has a soliloquy
- shakespeare play that has a soliloquy about a
- about a the moon and it's uh the moon has a pale
- the moon and it's uh the moon has a pale fire that and um the silicone is called
- fire that and um the silicone is called each thing's a thief you can find it and
- each thing's a thief you can find it and it's sort of commonly played upon by
- it's sort of commonly played upon by vladimir nabokov and others through the
- vladimir nabokov and others through the years for different things they do and
- years for different things they do and and um i always i've always i thought
- and um i always i've always i thought that this locally has a certain
- that this locally has a certain resonance or
- resonance or you could see its connection to
- you could see its connection to journalism um if you read it
- journalism um if you read it and uh i just sounded cool when i was 21
- and uh i just sounded cool when i was 21 or whatever and so you know
- so that's awesome i love that
- that's awesome i love that yeah it's pretty nerdy my my wife's not
- yeah it's pretty nerdy my my wife's not a big fan
- a big fan but i do think you know this and this
- but i do think you know this and this has been this has been a big thing in
- has been this has been a big thing in journalism twitter maybe you guys are
- journalism twitter maybe you guys are getting pulled into that world i'm sorry
- getting pulled into that world i'm sorry about you know personal branding as
- about you know personal branding as people call it you know and whether
- people call it you know and whether that's a good thing or a bad thing and i
- that's a good thing or a bad thing and i don't think there's really a simple
- don't think there's really a simple answer but i do think it's true that you
- answer but i do think it's true that you know if you're not someone who gets
- know if you're not someone who gets lucky to get a big job right away or who
- lucky to get a big job right away or who is like you know rocketed to the top
- is like you know rocketed to the top making it into journalism you kind of
- making it into journalism you kind of have to carve out your niche of like who
- have to carve out your niche of like who you are and i mean part of that is
- you are and i mean part of that is branding you might not want to call it
- branding you might not want to call it branding you might not want to think
- branding you might not want to think about it that way but i i you know who
- about it that way but i i you know who are you as a journalist what do you do
- are you as a journalist what do you do and so for me like that decision kind of
- and so for me like that decision kind of coming out of mizzou and depaul was like
- coming out of mizzou and depaul was like i'm a data guy and i'm an internet guy
- i'm a data guy and i'm an internet guy and that led me to do more web
- and that led me to do more web development and stuff that some of my
- development and stuff that some of my peers didn't want to do but i saw as
- peers didn't want to do but i saw as like a path forward in my career as
- like a path forward in my career as opportunity
- opportunity and then part of that was like keeping
- and then part of that was like keeping my dumb internet handle right because i
- my dumb internet handle right because i wanted to kind of just communicate
- wanted to kind of just communicate it sounds stupid to say today but like
- it sounds stupid to say today but like in 2005 to say like i am an internet
- in 2005 to say like i am an internet journalist
- journalist was like you were a weirdo you know what
- was like you were a weirdo you know what i mean and like
- i mean and like and and so there's part of me that like
- and and so there's part of me that like that worked for me and there's also part
- that worked for me and there's also part of me that feels like that's kind of my
- of me that feels like that's kind of my roots in a way and i don't want to give
- roots in a way and i don't want to give it up even though at this point it's
- it up even though at this point it's pretty uncool
- pretty uncool but i think generally having your like
- but i think generally having your like thing of like this is my thing and your
- thing of like this is my thing and your thing doesn't have to be a dumb internet
- thing doesn't have to be a dumb internet handle it doesn't have to be acting like
- handle it doesn't have to be acting like a fool and showing your ass on
- a fool and showing your ass on twitter you know what i mean
- twitter you know what i mean but
- but it you kind of want to figure out what
- it you kind of want to figure out what you know what is it your thing and you
- you know what is it your thing and you don't have to know right away
- don't have to know right away and like part of that might be redefined
- and like part of that might be redefined over time but kind of saying this is
- over time but kind of saying this is like kind of what i do
- like kind of what i do and making sure you have your little tag
- and making sure you have your little tag line that clearly communicates that and
- line that clearly communicates that and your website that like clearly
- your website that like clearly communicates that
- communicates that and then you kind of beat that drum and
- and then you kind of beat that drum and sadly self-promote because you know
- sorry you know like it's just it's it's part of kind of making it not like i'm
- part of kind of making it not like i'm any huge success story but like
- any huge success story but like it does help people know what i do and
- it does help people know what i do and they're like what's pale wire i'm like
- they're like what's pale wire i'm like oh that's me i'm the nerd you know
- oh that's me i'm the nerd you know and oh yeah the nerd yeah
- well thank you so much for that explanation um if we don't have any
- explanation um if we don't have any other questions we can go ahead and wrap
- other questions we can go ahead and wrap up um perfect right on the dot right at
- up um perfect right on the dot right at nine o'clock uh ben thank you so much
- nine o'clock uh ben thank you so much for being with us tonight i know spj
- for being with us tonight i know spj depaul and fort denise are so grateful
- depaul and fort denise are so grateful for your time
- for your time and one more thing before we go we do
- and one more thing before we go we do want to plug that this workshop was free
- want to plug that this workshop was free and open to the public
- and open to the public but um we are a student journalism
- but um we are a student journalism newsroom and we need funding so
- newsroom and we need funding so if you got anything out of this workshop
- if you got anything out of this workshop tonight and you have the means we'd
- tonight and you have the means we'd really encourage you to donate to our
- really encourage you to donate to our student newsrooms fundraiser um so we
- student newsrooms fundraiser um so we can fund more engagement events and
- can fund more engagement events and workshops like this in the future and
- workshops like this in the future and grace has dropped the link grace is on
- grace has dropped the link grace is on it grace dropped the link to the
- it grace dropped the link to the fundraiser in the chat so
- fundraiser in the chat so anything helps if you want to donate um
- anything helps if you want to donate um but other than that thank you so much
- but other than that thank you so much ben for being with us tonight
- ben for being with us tonight thank you for having me and thank you
- thank you for having me and thank you for staying up late on a school night to
- for staying up late on a school night to get nerdy i mean i really appreciate it
- get nerdy i mean i really appreciate it also i would just add if anybody wants
- also i would just add if anybody wants to talk about a story or has questions
- to talk about a story or has questions or just wants whatever um i mean please
- or just wants whatever um i mean please feel free to reach out i'm going to put
- feel free to reach out i'm going to put my email my personal email into the chat
- my email my personal email into the chat um you know you know i'm not uh
- um you know you know i'm not uh you know i'm happy i've got time i'm
- you know i'm happy i've got time i'm happy to talk just feel free to reach
- happy to talk just feel free to reach out and google me probably find my phone
- out and google me probably find my phone number too if you want it you know it's
- number too if you want it you know it's like so
- like so um
- um thank you again
- thank you again go demons
Data Journalism Workshop
By Ben Welsh • • 14East Magazine in Zoom