Working Effectively with Data | Community Webinar

Key Takeaways

uh so thank you everyone for joining uh my name is nathan faccini i am one of the marketing managers here at data science dojo and today i have ben jones with me he is the co-founder and ceo of data literacy and he also teaches data visualization at the university of washington foster school of business so he's going to be doing a talk on read write think data so ben why don't you go ahead and take it away or maybe right before that i can remind myself to tell everyone that for q a we will do all of uh answer all of your questions at the end today if you're in zoom with us use that q a tab helps us keep everything organized and then if you're on one of our live streams feel free to ask your questions uh we will pull them into zoom for you so um ask your questions uh you know talk to us on chat and ben why don't you now you now the floor is yours all right great thanks nathan yeah and i can see the chat right here too so um feel free to and so it's nice to see all the greetings from people all around the world arizona right here in seattle toronto um yeah new york city it's really neat southeast asia so we have some international represent so i just want to say hello to you all and i hope you're doing well wherever you are listening and also want to say thank you to nathan fatima as well as megan hanno who's on the call who works for uh for my team and just does a fabulous job for us and i'm happy to just spend a minute with you all uh today you know going over the title of the next book that i'm working on is read write think data and this is really a title that came to me because it feels like what's missing is i'll talk about in this session isn't a specific tool training offering i think that there's a lot of that out there but what is not really as pre as prevalent you know is the ability to help someone learn how to think clearly with data critical thinking skills understanding how data fits into a broader decision making problem solving question answering process so that's what i want to talk about today and i know that you're on this call or listening or watching the recording because you know you're interested right in being able to work effectively with data that's something that you've already noticed is important and you've probably also noticed that it's important in a variety of contexts not just not just at work of course you know we can realize that some of the issues we face in the public sector in the world in general even down to our communities can be addressed and hopefully even maybe solved you know with some data inputs and even personally right what sorts of things are you working on improving about yourself so there's intuitive emotional components there's analytical data components and i think the artful approach to living is to find a way to bring those together which is very much a theme you know in our training so a bit about me nathan already did a great job of introducing so i won't really go into much more detail only just to say that i really love writing and teaching i've come to learn a little later in my career that that's really my passion you know i spent a lot of time up front coming out of engineering school going into engineering as well as then continuous improvement and and even in marketing analytics the theme as probably for many of you was always data you know working with data to try to improve the situation to try to grasp opportunities and then i made the jump in 2012 to uh to tableau and that's when i moved from los angeles up to the seattle area where i live now i'm actually in bellevue so across the lake from the city of seattle and um you know while i was at tableau i ran the tableau public platform and loved it you know really met a lot of people who are passionate about data got a chance to travel and teach journalists students teachers at some of the you know largest universities of the world how to work effectively with data and started writing books about what i was learning so that's really what uh i'm into i also so in right now i run this business called data literacy and so i left tableau back in 2018 to help people speak the language of data because i recognized that there was this gap like i said you know people were trying to learn how to use tool x or tool y but they weren't really data literate and so they were running into problems they were using these powerful tools to mislead themselves and others most mostly just mistakenly i don't think that people were doing it on purpose although there is some of that but mostly they were just realizing at some point hopefully that wait a second i'm trying to make sense of data but i have some important building blocks some important foundational pieces that are not in place yet and so it's hard for me to know what to do and i've and maybe even you know i've been making mistakes all along that i don't know about and so what we want to do with this business is really help educate people help build their knowledge and their skills but also address their attitudes and their behaviors how do they feel about data what are the sorts of things they do day in and day out as they contribute you know in the workplace in the communities that they're in and even in their homes okay so that's what data literacy is all about it's a phrase you know what does it even mean well it's widely uh understood something similar to this that it's the ability to read understand create and communicate data as information and so you can see even in those verbs that there's a connection to language there's a connection to you know the way we interact with each other and the way we both learn and communicate and so i i think that that is a helpful way to think about learning data and that's why we build these courses that help people step up and become more and more fluent so when you're learning a foreign language i grew up in southern california and i loved spanish i took five years of spanish i spent time in central america and just really loved it you know and did presentations and all that um and at one point was fairly fluent it's been getting very rusty but i remember you know at the beginning i learned vocabulary words i learned maybe sentences and sentence structure how to conjugate verbs you know very basic stuff and that's like our fundamentals course just what is this language what are the elements of it help me understand it and then of course you kind of graduate a bit you know in second level maybe second semester and you start to read things you start to read sentences that other people have written even maybe eventually listening to audio or watching um telemundo or something like that univision right the news broadcast the tele novellas trying to figure out what is the meaning behind what these noises are that i'm hearing and that's really to us the level one program where you are really kind of coming it from the point of view of consuming what other people are putting out there and making sense of it and then eventually you need to kind of step up you know as you become more and more proficient with data and that involves rolling up your sleeves and actually working with data seeing what's there and learning and gathering insights from data that's our level two program that i'm talking about a bit today and of course then level three is where you get into crafting your own messages becoming not just um able to communicate but even perhaps even eloquent right in the way that you are articulating your thoughts to others and conveying things to them and getting your message across so we're trying to do all of that with data and that's why i think that it's a very important thing that that many others in this data literacy movement are also seeking to do folks like um valerie logan at the data lodge who really helps companies come up with their game plan what's what's your roadmap here for becoming a more data literate organization and many others and so we really focus on the training piece and also the assessment piece so that aside i just wanted to at least kind of help you understand you know who i am and why i'm here and why i i care about the topics i'm going to be talking about but let's just do a quick exercise put in the chat the square root of the sum of these five cards let's see who i'm looking at the chat so i'm gonna know if you put the answer in if you're watching the recording i don't think this part is very useful to you but okay so brian is the winner brian and also betsy jose and mike they're right up there on there on the on the money this is clearly you know not rocket science this is five we can add up three four five six and seven and we get 25 we take the square root of that and we get five so nice job it's good to see everyone's awake and they've got uh you know their um their wits about them and they have the ability to use the chat so that's great uh so let's speaking of observing right that's really what you were doing you're looking at these numbers you're doing some mental math you're doing a little bit of an equation in your head well here's another question i have for you you're looking right now at six world maps let's just see for fun if someone can guess what do they have in common they all have one thing in common i'll give you a clue to help maybe narrow it down but at the beginning i just want to say i just want to know if anybody can come up with a wild guess of what these six world maps all have in common okay so take a look you know use your powers of observation to check it out and see what it might be now oh okay so you got to be kidding me seth completely nailed it right out of the gate they're all missing new zealand how did you i did not i don't seth and i have never met i did not communicate with him ahead of time he's absolutely right these are all missing new zealand it happens to be a thing you know there's even a subreddit maps without new zealand on them there used to be a tumbler but i don't think that's a thing anymore i don't know anyway uh you know it so happens to be the case that uh people when they put world maps out there often drop new zealand entirely from the view now why well of course the common let's say i guess supposedly western way of centering a projection of a globe would include new zealand being in the bottom right corner and just getting cropped out of course right uh and is no it is certainly not far from australia uh mary it certainly is close enough i would say they would argue to be included on the map um but when we talk about new zealand in honor of new zealand and to make up for all of those errors that uh probably i've made to at times let's think about the map of new zealand well there it is right there there's there's new zealand okay well the maori people lived there for centuries before anyone from europe ever knew that there was anything there and so it so happens that the first european to sail upon new zealand was a dutch explorer by the name of abel tasman after whom tasmania is named you guessed it now um so he cruised along there and he uh in his name with his ship was zee hayne he and his crew decided to name this area in the middle of new zealand they called it zee haynes bite zee hain after his ship bite after not a bite out of a sandwich although there is a connection there there's certainly a resemblance a bite with big ht happens to be a crescent-shaped recession in a coastline this is actually where i didn't i didn't know that until i was researching this topic but i happened to have lived right by one here in southern california for much of my life so z haynes bite okay we're going to come back to to uh new zealand and zehane and uh what it is that that we see there but uh for now um again just all i'm really trying to do is make up to new zealanders and so we'll focus on their map but you know we talk about explorers we talk about sailing on something that you are seeing for the very first time and i can really relate to that experience because you know when i moved from los angeles up to to seattle like i mentioned um i uh wanted to learn how to hike i don't know why i just had this like urge to get out there on the trails and experience nature i was never that kind of kid growing up in los angeles i was too busy riding my skateboard or playing soccer or nintendo you know going on a nature walk would have been the most boring thing ever i would have tuned you right out but then for some reason i turned 30 35 and got into my 40s and i just really wanted to be on the trails and so you know i decided i was gonna do that now when i came here i didn't know the first thing about it i mean i didn't i was i didn't know where to go i was new to the area i didn't have any um like equipment or any even good shoes to take on the trails with me but i was determined you know so what did i do well i started researching it i started talking to other dads at the soccer practice and asking them where i should be going they mentioned a few trails i would never go on today because they're completely overrun don't go to rattlesnake ledge if you ever come to seattle but those were places that seemed so exotic to me like wow there's this trail out there called rattlesnake ledge oh my gosh i gotta get out there and try it and then i get out there with my kids and i'd be like well am i gonna get lost or eaten by a bear i mean i was so nervous right and so i had these devices and gears trying to track my gps and it seemed for sure like i was lost many many times and so i was just stepping out you know into this activity that it wanted to get good at and it was pretty intimidating and i had so much to learn but i did it and i uh got to the place where you know we we love this activity we backpacked for multiple nights at a time and um you know still haven't been eaten by a bear still haven't seen a bear but i'm about to knock on wood because that is almost inviting the issue i think but here we are this is me and my family and uh our little dog winston who magically is off a leash and still there so that's like the one time it's happened and he even stayed long enough for the counter on the timer on the tripod to go 10 seconds and take the photo this is a place called spectacle lake but you have to promise me to keep that a secret it's not a great secret it's actually on the the pct the pacific crest trail or very close to it and so some people know about it but it's a beautiful place but my point is this is a the process of adopting something new the process of exploring these are really things we can all relate to you know there was times where you try to pick up something and you started to do that well this is what we all need to do with data today and people are petrified of it and so they feel left out of this data revolution that's just been going by them over their heads and they're trying to play catch up maybe even afraid to say that they're not really that comfortable with the topic at all and so you know we're trying to help them do that when you're on the trails one thing you notice pretty quickly is sign signs everywhere you know signs including those that are warning you not to do this one is actually on a coal creek trail here in bellevue maybe about a mile and a half from where i'm sitting right now and so it says warning keep out and it's interesting on the other side of the sign is this huge bowl in the ground and evidently if you step in it you're going to fall right to the bottom of the center of the earth i think it's some kind of uh what do they call this like um yeah it's remember when we were kids quicksand was the thing we were always afraid of but evidently this is some kind of an area in the hillside that's susceptible to completely falling away thank you mike yes a sinkhole why did the word just completely escape me i think it's because as i said i'm in my 40s but my point is there are these signs okay i will never forget the one that i was on called the timberline trail we go around mount hood down in oregon and there's this pencil scratch sign nailed to a tree it just says don't do it that's it so i look on the other side of this sign and there's this rope you know and we had these big heavy packs on so needless to say we didn't do it but the alternative wasn't great either we had to hike from miles up and over this ridge line and through this scree field and it was it was bad it ended up being that we didn't get off the trail until 11 30 but the point at night but the point is you know there are these signs that tell you what to do what not to do and so my my uh observation is that when it comes to working with data we don't have those signs right now you know people are stumbling forward working with data doing their best with great intentions using very powerful tools and yet completely making mistakes left right and so we need some signs we need some road maps and you know some maps of the terrain we need to understand the sorts of things to look out for and um and avoid just like someone who's uh you know really experienced with hiking would would be able to tell you all about if they were to take you around and and point out some of the things and they probably learned that the hard way you know that's how a lot of us have been learning by making some of those mistakes because we have been adopting technologies this is not really new to us this is our entire era our generation if we've done nothing else it's adopt new technologies one after another after another in the 80s we had to learn you know and this was the pc revolution we had to learn word processing i remember some of my first reports that i was turning in by computer we used to have those little perforated little uh what was it called again someone help me a dot matrix printer and you had to rip off the edges and staple it and turn it into your fifth grade teacher a report on the ocelot i don't know before we just do it by hand i mean i'm old enough to remember that switch over in the 80s to when all these reports had to be done by hand yeah and you can see here how you can learn you can learn how to use word star in just four or five hours comp you lit my kids used to like that and now they just cringe you know it's it's not cool anymore to make a joke about something being lit so i just leave that alone even though i just didn't do that but my point is that this is something we had to adopt and actually we did it really wasn't that hard we got good at it what is left justifying right justifying how do i make it italics all that right we did it and then in the 90s along comes the internet we have to learn how to email we have to learn how to use the internet and browse we start putting things like netscape navigator on our resumes this was back in the day i went to ucla in the 90s in 1996 when i started my freshman year of ucla i had to register for my classes with a phone and a big thick booklet of all book not a booklet a book you could use it as a probably a door stop i don't know and we have to dial up and put our numbers in or the classes we wanted to register by the time i graduated in 2000 it was all online everything you just do online your courses online your assignments online everything and i i would have loved though if i could get a hold of this video professors learn to use internet it would probably be a classic they probably wear really bad sweaters and have funny wavy hair i don't know you know you got to love the 80s uh in the 90s right it's a great time to be alive to be growing up uh but we had to learn how to use the internet and adopt that oh my gosh did we do it yeah i'm not so sure we're that good at it but that's another story we certainly know how to use the browser and click our way around and nobody says they know how to use chrome on their resume anymore i certainly doubt it but that is again something we had to adopt now 2000's come along we all of a sudden have the internet in our pockets and we see the rise of these social media networks the facebook right there welcome to the facebook so we had to learn how to what what do i use facebook for that i don't use twitter for and what about linkedin what am i using that for now we've all kind of figured it out i think you know we kind of know what this platform does and what why i would be here versus there and so these are technologies we've all gone about adopting now when it comes to working with data this digital revolution this third and now even fourth parallel industrial revolutions have totally changed the way we work with data we've been working with data as a species for thousands of years i mean that's not new you can find newspaper articles from the 16 1700s dealing with mortality figures you can even find tablets etched with sheep sales from one you know town leader to another so we've been working with data this is not new but in 1979 visicalc comes out on the apple ii this turned a hobbyist toy the pc into a legitimate business tool and that really in some people's minds completely fueled the pc revolution that just took off now uh here's an ad from the 1980s how to turn a sea of data into data you can see and you got a big stack of paper there with tables and now all of a sudden it's being replaced with these fancy colorful charts and graphs right and so um we work with data all the time now and it's just continued to accelerate you take a early screenshot of tableau my former employer there on the left 2003 and now you can see what sorts of things are possible in uh the current version of that tool that has evolved so amazingly in the past few decades and that's not the only one there are so many of these tools you know all about them it's interesting some of the most commonly used tools have been around for a long time sql [Music] was invented in the early 1970s you know by chamberlain donald chamberlain and and um and boyce the two of them came up with sql still one of the most common tools but we've had on top of that just this massive evolution of tools but with new tools still know new zealand i didn't miss new zealand was there a world map oh it's got to be in there it's got no you got to tell me it's in there right it's there but it's being covered by the legend oh my gosh mike you're right i gotta fix that new zealand it's there but it is being just rudely covered by a legend even worse maybe even worse just slap right on top on them as if they're not even there so thank you nice catch so when we talk about technologies clearly as i've just shown well here's what happens you take a take the automobile 1920 it starts to be introduced on streets all around the world city streets and the number of miles of um you know uh traveled just climbs doesn't it you know exponentially really um to to the present day and so along with the adoption of that technology the automobile we see this corresponding plummet in fatality rates per mile per 100 per 100 million miles so you know there is this simultaneous adoption as the adoption accelerates we start to see some of the problems with that technology being addressed okay it is not hard to imagine why it was so dangerous back then to have a car around you know the whole city was not structured to have automobiles people were being hit left and right you know sadly right but then you get seat belts then you get lanes then you get you know all the safety requirements and regulations that go along you get so many different um safety um sort of aspects kind of put in place and then you see the fatality rate plummet uh and so you know this is what we need to do with data i think we're in the early let me go back we're in the early stages i think here uh as a species certainly we've been around uh for a few hundred thousand years the homo sapiens those are some when the earliest were discovered and so when you think about that and and the percentage of the history of our species that has been working with data in digital form at the level we have we are just at the very the very beginning stages of it and so we have a long way to go and so we need the time to get it right we need time to address some of the problems right and this is where data literacy comes in um so this is a book we wrote called the 17 key traits of data literacy i'm a real big fan of alberto cairo he's on our board he's written a number of great books i'm sure you already know about them how charts lie the truthful art the functional art and he talks about working with data involving numerical and graphical literacy called numeracy and graphically and this is also you know uh ability to work with numbers but he talks about even there being a sixth sense that's involved there we'll get more we'll get back to that he also mentions graphicacy this ability to understand the visual language of data charts and graphs hence the word graphically and so we can say that you know being data literate involves you know each of these involves being both numerate and having acquired a certain level of graphically and so i think there are other things as well technical skills communication skills also being able to apply that to your domain wherever that may be if we think about numeracy and if you don't believe me yet that we're in the early stages with the automobile uh bumping into people all over every city street and every corner then let's talk about it a bit you know we've certainly here in the united states we've seen studies that show that the u.s adult lags in numeracy this is a study that was done a little while ago um about eight years ago now i suppose and so you can see that the united states ranks you know fairly low in numeracy uh so um you know let's try a little quiz okay so here you go let's use the chat together a bat and a ball costs a dollar ten the bat costs a dollar more than the ball what's your first gut reaction for how much the ball costs put it in the chat your very first gut reaction to the question how much does the ball cost if the the bat costs a dollar more so i see some answers coming in here yeah right i'm with you all right the the uh the 10 you think it's 10 cents right you think it's 10 cents well if the bat costs a dollar more than the ball and together they cost a dollar 10 that means well clearly the ball costs 10 cents but no that's wrong i mean we're seeing the right answer coming in now meg and david uh getting it right it's five cents it's five cents because if the bat costs a dollar more than the ball and together they add up to 110 think about it again you know it's it's got to be five cents and then the bats a dollar five and then together those add up to a dollar ten but if the ball costs ten cents and then the bat costs a dollar more then it costs a dollar ten and if you add those together you get a dollar twenty so the point is and this is really just a little trick question they did this exact quiz to ivy leaguers and you know a high percentage of them got it wrong like more almost half i believe if i'm not mistaken so we have these glitches you know as humans we get kind of like you know get the numbers wrong we're not always we're not always really kind of having the the right intuition about numbers and how they work they get us into trouble a lot you know percentages rates these things make it even more complex so we we need to really build our immune system let's say to these sorts of hiccups uh and uh and i don't think you have an immune i don't think your immune system helps with hiccups so that's a terrible analogy i don't really want to make the other analogy about the immune system because we're tired of hearing about it but we do as a species need to get good at knowing these little glitches and when they pop up and so that's something that that i think that uh we can we can all help with i mean we all need to work on that together when it comes to graphics you're reading charts and graphs most people if you think hey can you read a chart i said well yeah of course i can read a chart i mean you know the basic right just show me the chart i get it i can tell you what it means well the fact that matter is you know people struggle to read and interpret charts here's a study from pew research where 63 percent of american adults were able to get a correct answer okay so you tell me what you think it is there's a scatter plot here average sugar consumption on the x-axis average number of teeth decayed per person and every dot as a country okay so what is the correct statement that the statement that the graph most uh let's say uh describes and and so a in recent years the rate of cavities has increased in many countries b in some countries people brush their teeth more frequently than in other countries c the more sugar people eat the more likely they are to get cavities or d in recent years the consumption of sugar has increased in many countries which do you think it would be yeah everyone's nailing this one c yeah c is the right answer but and this is a data savvy group because it's a fact that 37 of the folks pulled by pew research got that wrong and i would say you know that's a fairly basic chart right and so uh this is this is true in general that we struggle to read and interpret charts we're not there yet we get it wrong a lot i do too now you talk about i said i wasn't going to mention it but here it is right we talk about covid while there's a disease that spreads okay well unfortunately as we learned that can spread at an exponential rate or uh according to maybe even a power law let's say and so that's an appropriate way to at least in terms of engineering relate to that data in a log chart like the one on the right over here where we see the logarithmic scale vertically and we see also then you know another version of the same data on the left which is a linear scale where the grid lines are 10 000 each over on the right it's the grid lines are a factor of 10 each and so when they show these charts to people and ask them to some basic questions of interpretation 84 were able to answer correctly using the linear chart whereas only 40 percent were able to answer using the log version of the line chart and so you know you could say well then don't use a log version of the line chart but there might be instances where the growth rate is so high that you're not able to see anything everything collapses immediately to the x-axis those of you who have toyed with or played with switching between those axes you know what i mean so you know these are more challenging situations it's not a basic scatter plot it's a line chart that has a log scale to it and now we're losing even more people but you know this is an important topic we all needed to understand what we needed to do to keep ourselves and our families safe and so you know there are potentially um you know great benefits to being able to read a log chart uh well and so you know again not everyone has that skill yet and so this is something we need to learn i love this quote this is the mu she is my muse for my level one course mary eleanor spear she worked for a lot of different us federal agencies the irs bureau of labor and statistics for decades from 1920 to like the late 60s and her name is mary eleanor speer she wrote a book called practical charting techniques and this is the quote that i put in front of me every day when i was trying to create my level one course learning to see data because she says that we need to learn to see details there's quite a difference between simply looking at a chart and seeing it looking as your first visual impression while seeing involves the studying of distinct parts of the visual it's a key skill we all need to have that right now alberto talks about i mentioned a sixth sense that it isn't a science here we can't get it perfect he talks about needing to have an intuition and even tentative grasps of these concepts of what to do of what works well in certain situations this is not exactly a perfect process we can just nail it down and get it all right and i agree with him on that i think we have to become more experienced we have to have more of a spidey sense about the kinds of pitfalls we've been talking about the kinds of problems that you encounter on the trail of data working as you go through the mountains and valleys and try to find your way to a higher place so this is a question that i think we need to address as a species at this point in our species is history how do we steer clear of common pitfalls now that the tools and technologies have evolved to be so powerful what's missing is our own skill and that's where i hope our courses can come in what's also missing is a good process and that is missing this is a screenshot you're looking at here from our uh our data literacy score team-based assessment we send out surveys we're doing one right now where we pull and survey or uh team members within an organization a number of questions in each of these categories you know what are the sorts of things that you're struggling with how well do some of these areas apply to your team and so it's very subjective you know it's their opinion their point of view but it's a very useful lens what we see at the bottom every time is the process category and so we think about other domains other disciplines science they have their scientific method this is nothing new we all learned this in grade school now a practicing scientist would tell you it isn't very clean i mean you know it's a lot messier than it looks here on this fancy flow chart but there is a method that they have as their kind of overarching um you know bible for lack of a better word uh if you're in quality control you've learned the pdca process plan do check act developed by quality guru w edwards deming actually even earlier than him i think he popularized it but this is a process to try to improve and reduce defects and flaws and manufacturing and such so they've been doing that i spent a lot of my time in lean sigma movement with a with a black belt my dad was like i thought i put you through engineering school what do you mean you're a black belt but uh nonetheless i did learn how to improve processes using a methodology you know it was pretty kind of got a little bit old after a while but it worked we saved the companies a lot of money going through this process of identifying and improving and tracking and sustaining the gains hopefully so there's a process for that so this is what i have tried to do i've tried to build a process for analyzing data for working effectively with data for thinking critically about data and that's what we're teaching in our level two course i thought about a lot of things should it be a loop should it be a circle should it be you know what what is the structure of it and ultimately i want it to be a stairway type of process and then this could be then something we continue carrying on right so we just continually step up using data to become um you know wiser more mature improving things and that is i guess the the the overall kind of the high level you know at least the ideology that i'm trying to put in place and implement and here it is in all his glory now and i was always told never show this all at once because it's way overwhelming but we try to organize it you know to make it feel like it's less intimidating it's a flowchart you know and it just like the scientific method it is not something you step through perfectly linearly no it's messy you loop back you're not really sure where you are maybe sometimes but this is this idea that we can teach people when they're first starting to work with raw data you know jumping over that gulf from the world of a chart reader to the world of a chart maker we need to get more people over that gulf when you land on the other side of the gulf after some big huge leap you find that the ground is very muddy and messy so we'll get to that in a minute but the idea is how do you now make your way through this terrain you've landed within where you are now actively exploring data taking a look at it seeing what's there and as you can see the process starts with an observation the best skill that you can learn as a data analyst is to be highly observant to have very keen skills of observation that's more important than learning how to use tool x or tool y or any of it for sure you know being able to observe um marcus aurelius roman emperor stoic philosopher he says nothing has such power to broaden the mind as the ability to investigate systematically and truly all that comes under the observation in life speaking of observations did you notice anything strange about those cards now that i ask it that way do you see something that that presents itself to you as being odd or strange as i'm looking here at the chat you just looked at these exact same cards i promise it's the same it's the same one is it a nice poker it is a nice pokemon this would be of course a straight i would love that and so yeah tamara or tamara and david and marcy got it right so maybe you noticed it look i promise everybody's like yeah yeah right you you change this slide no and i didn't there's no way i could have changed it you see here it is same one okay now i proved it to you and so what's crazy about the human experience is we think we see things you know but we miss things we miss things that are right in front of our nose that should be of course in a standard 52 card deck a red heart of course no no doubt and so some early research about into something called inattentional blindness in the 40 in the 40s was conducted in harvard bruno and postman they coined something called the incongruity the thing that's right in front of you that is out of place but but you don't see it yet uh because if you still don't believe me that it was not just the five of hearts that was out of place maybe if you were really savvy you noticed that the original version actually had let me go back to it to prove it to you i actually had eight clubs on the seven here right so that is actually an eight of clubs maybe a little more tricky to notice that one but the point is again we there are things right in front of our our noses and we miss them all the time so being keen observing observer i love this quote this is my favorite quote on the topic dido moriyama japanese street photographer you're not going to develop a discerning eye unless you hone your ability to give something your full and undivided attention of course we need to learn how to ask good questions of data voltaire said to judge a man by his questions rather than his answers nancy willard american writer says sometimes questions are more important than answers we can all know the journalism school 5ws and 1h who what when where why and how in our courses we teach people to go beyond that we talk about a dozen how's to ask your data these are specific questions that are tailored you know to data yes the 5ws and h sure we can use that to act as a guide as we start to ask questions about a data set but sometimes we need something a little more detailed than that a little more nuanced that really gets to the sorts of questions that data is really good at answering okay but there's always this critical skill piece sometimes the most important question isn't about the data it's about what's not there in the adventure of silver blaze sir arthur conan doyle this sherlock holmes story holmes is having a conversation with gregory scotland yard and spectre when the um a uh a horse's trainer was killed and gregory says is there any point to which she would wish to draw my attention holmes says to the curious incident of the dog in the night time gregory said the dog did nothing in the night time holmes that was the curious incident the point is he was keen enough to recognize that he needed to think about the thing that wasn't there the dog made no noise how is that possible that there was a trainer dead in the horse stable when there was a dog there too and the dog didn't make any noise you get the point is this is not about tableau or r or python this is about you thinking this is about you thinking and having your brain engaged we're big believers you need to explore the contours of your data before you dive in and use it to answer anything you just have to profile it you have to see what's there walk around it size it up see what's there so tasman you know you go to this area right now this is not called zee haines bite it's called cook straight because james cook 100 years later british explorer he and his crew circumnavigated new zealand not the island of new zealand the plural islands of new zealand too north and south separated by a navigable waterway 22 kilometers in its narrowest point notoriously boisterous by the way and he sailed right through it tasman didn't even notice that there was a null there to use data terminology a blank a missing piece of land and so tasman sailed away with a complete wrong idea about new zealand not just a detail about new zealand by the way the single most important geographic fact about new zealand is it has two major islands and he totally missed it and james cook didn't and that's why it's called cook straight and that's why when we take a look at data before we use it we have to look closely at it see what's there luckily tools now make it so easy you know in the left tableau prep this is a tool with a profile pane you get a bird's eye view of your data same thing in power query editor if you know enough to go in and turn on the data preview options in the view tab of power query editor you can see the bird's eye view of all of your variables their shapes their mins their maxes their averages you can just in two minutes or less get a very thorough profile of your data set you got to do that cleaning and structuring it absolutely need to do that too we're going to q a here in a minute but talking about cleaning data well it's always dirty isn't it it's very rare the data is super clean and pristine very rare i come across data all the time it's in terrible shape a notoriously dirty data set that i encountered was baltimore city towing records you can see the url here there was over 61 000 vehicles or tows between uh in about a half a decade a half a decade period there and you can see the spreadsheet here on a screenshot of it and it tells you when the tow happened the vehicle make and model and how much was charged and all the rest right so let's take a look at this data what if we have a basic question like what's the most common makes that get towed honda ford chevy toyota dodge nissan toyota honda acura ford wait a minute we're starting to see some repeats here aren't we what's going on there well you can see ford in the 61 000 rows you also though if you look carefully find ford in all caps you find fourth and you find ford with three r's there's peterbilt there's also peter belt there's even peter butt and there's just pete and i don't think he would really like being towed if it's just pete but those are all i think referring to a large semi-truck i wouldn't recommend mitsubishi use one of the misspellings of their car for a marketing campaign but there you have it i won't say it but mitsubishi there i said it uh there's also bert carr who knows what that is i don't know bert carr do you know volkswagen is spelled 36 different ways in that data set and you here you are and you can see them all and i and i laughed at that until i realized wait a minute i think i would have probably spelled it wrong myself uh so it's dirty data right so we need to clean it up okay so we do that we clean it up we use some fancy tools to go in and change values and switch it around so what what happens well what's the effect of all of that cleanup now we get to analyze the data what is it telling us well what it's telling us is that the top three aren't even in the right order before on the left the cleanup it was honda ford chevy after the cleanup it's honda ford toyota right so they're in a different not only are they in a different order you can see the number of hondas towed 5200 jumps to 7700 that's 50 more toes we were off by quite a bit it's not like a little bit half a percent or a percent or even 10 percent it's off by 50 in terms of the number of toes that were occurring remember our friend volkswagen it goes from the 26th ranked toe up to the 11th because there are so many ways it gets misspelled it leap frogs almost into the top 10. and so you know again you know we can see that our analysis completely changed because we spent some time cleaning the data and so i'll stop there but and i want to kind of get to some questions but actually it looks like there's one in here already i'll get to the point i want to make though is that you know what we need now is a way to step through this process now i do not think that uh it is in the the best way to use this pro i do not think the best way to use this process is to follow it step by step no it's like if you learn how to ski you know or snowboard or something at first yes you need to pay attention to every little move and your balance and the edges of the the ski or the snowboard and you know and all the rest if you've ever gone through or like i was saying hiking i didn't need to pay very close attention to many details that now that now i don't the gear is second nature i just just do it and you know it's it's just very much natural and so working with data is like that too first you learn this process very carefully step by step eventually the process sort of just becomes the way you do things in my experience and that's where we're trying to get to where these things are kind of again you know second nature to us as a species um and and so that's my hope you know is that we can find a way just like my two boys here uh climbing this nice little trail carved out of this gnarly looking hillside this is a twin falls hike just outside of seattle here and it's a journey to a higher place right there they are stepping it up thankfully the forestry crew have made a nice trail there for us that's like all the tools we had a nice trail map that's like our process you know and so these um sorts of components i think help us to get places and have experiences and learn and grow and that's what it's all about okay so i'll stop there i want to get some questions going here but by the way i do want to let you know though uh while you all just submit your questions that uh if there are any that you can't we're about to launch this as an on-demand course and we teach this for corporate groups all the time and so if you go to this link right here on our website which is dataliteracy.com there's a waitlist here and it's going to be just a matter of a week or less hopefully i'm in the final stages of putting together and the finishing touches on the on-demand course it'll look something well here this is a this is a view at it of it live right now and so you're going to be able to kind of you know get into a few different of these different modules and lessons this is going to be something that helps you hopefully you know kind of go through some tutorials as well ways to learn how to not just talk about it and think about it but how to actually roll up your sleeves and do it sql excel we're making a tool agnostic we're trying to teach the process which means we have to layer in uh tutorials for many different tools along the way okay so that's our goal and we've been working hard on it i think it's um something that's hopefully going to help a lot of people i know i would say it's i feel like it's my life's work up until this point where i go from here i don't know but i do know that this is something i really wanted to get out there when i was at tableau i saw many people making mistakes i noticed i was doing that myself too i wanted to create a map that we can maybe try to follow and learn and that's been my goal okay so with that let's see what questions we have here yeah thanks a lot kevin i appreciate it jose but wouldn't your experience as a whole uh entirely affect how you analyze 100 yes your experience has a huge impact on how you analyze how you go and i love that about it how you go through this process jose very different than how i would go through this process using the exact same data set so what this and i'm glad you mentioned that because this does not guarantee that you and someone else arrives at the same location using the same data and even the same tools even sitting in the same room no because your brain is going to be going through emotions slightly differently sometimes sometimes dramatically differently the intuitive spark that you have my favorite branch of the process is this one at the top find another question oftentimes you're working with data all of a sudden you realize wait a minute if that's the case then i have another question now that's not going to happen to everybody the same way there's no way and this is why it is good to be working with data as a team member on teams of other people who are highly data literate because you're going to get to different places it isn't like the trail map where you hope to get to the waterfall like everybody else you know if you end up somewhere else you're probably in trouble not that at all here you're gonna end up in somewhere very different from me and from everyone um so yeah thank you for for mentioning that because i think it's a really important point to make and i've never thought to make that point that never occurred to me that it was something worth stressing i'm looking at some more comments coming through here uh come on do i need a college degree to break into the data analytics career great question come on i have uh three boys right now they're in college you know um and so my my thought is that it doesn't hurt but i also think there are so many great uh resources out there now you know that if you have spent the time learning it whether it's at a university or in boot camps uh or if you like to read books you know there's lots of great books out there the bottom line is if you learn it and then you have a good portfolio of work that you have done and this is where it can be helpful to do different kinds of work that you can put in the public domain your own passion projects you as you learn things try them out on data that's out there and um that's a way to build your portfolio i think it's possible to have a really great career with no degree in the data world in data science and all that um but that's my guess you know i'm not in that world right now of trying to break in i see my boys doing it and those questions are on my mind as well but yes i think it's possible of course having a degree does not hurt at the end of the day though it comes down to how good are you and you know can you be personable can you meet and connect and make human connections and then find ways to collaborate those to me are the key skills and i hope we're moving away from a world where a specific checkbox like a degree of a certain kind or what have you is less important because then that's all everybody does they just check the box but they may not really have learned you know but anyway we could talk more about that and just the education system in general but there are some great schools out there now when i was at tableau i ran the academics program and i worked with people that were thinking day and night about how to make their college their university data programs better and better and better you know and so it is a a good place to learn these skills it's tricky though right because the skills are changing very quickly and so that is not always academia's virtue that it pivots and adjusts quickly um and so even if you get a degree you probably need to bolster it you augment it or enhance it with some of these other more nimble approaches that are out there you know yeah yeah practice practice practice that's what yeah that's what it comes down to yeah no doubt right yeah uh nathan where should we go i know we only got a couple minutes left so let's wrap it up we have let's answer two more questions um the first one and i i know you i think you've just been going through the chat let's answer jose's question in the chat about analysis and writing and then we have one question in the q a that's been in there for um for a little bit so i want to make sure we get to answer that one yeah someone actually followed the instructions nathan thank you no but actually but i know the different instructions so everybody's like wait a minute yeah okay so jose says so is doing analysis kind of like writing to be a good writer you need to read a lot of books to see different techniques and ideas to be a great analyst do we need to see and understand different analytical projects trying to figure out how to get beyond the crowd you know it isn't really about um seeing other people's analysis it's more about eventually doing it you know um but in that sense okay so to be a great i'll never forget my first book communicating data with tableau i was really lucky to have a great editor her name is julie steele she worked at o'reilly media at the time she announced that she was leaving the day the book published but i was really lucky to get her um her input as my as my uh my editor because wow did she um completely tear me up at the beginning i mean my writing was terrible at the beginning and she helped me understand that and i had read a lot up until that point you know i had read books and books and books i love reading but when it came to writing down my own thoughts and thoughts and articulating them my thoughts were all over the place and the writing was run on sentences like you wouldn't believe and she almost like made fun of me you know i kind of probably needed it so julie if you're out there you know thank you um but i had a right to get better at writing i couldn't read more books to get better at writing i think it's like that with data you know we can see other people's analysis but it would be kind of like watching a movie of someone going on a hike you still don't really know so ultimately i have to put my boots on the trail and i think it's like that with data you have to crack open the database or the csv or whatever whatever it is and you have to step into it so that's i think really the key you know it's a participative kind of a thing so i'll stop there hopefully that answers your question it can't hurt to see what other people do and see the way they analyze data you can certainly learn a lot that way but really to get good at you've got to do it um okay so uh nathan you mentioned there was a question right uh in the in the q a box so did you want to i don't think i can see that oh there it is i can see it yep now it is i do see it this is from drew do you feel there is a difference between data literacy and what might be called scientific experimental literacy as a species we have a much longer history in the latter still a long ways to go for both yeah you know scientific literacy to me the scientific method so what is a scientist doing they're trying to uncover universal truths about the physical world in many cases and so when it comes to business intelligence or data analysis we're often working on very particular questions and problems that only apply in a certain space you know like what was our sales in the apac region in q2 of a certain product category that doesn't involve any the trick is and actually we bring this up in our process when we talk about this guess or hypothesis notice how i said guess or hypothesis because the word hypothesis tends to more apply to this the scientific realm as well as we know the statistical realm where we're making inferences about general rules or populations but sometimes in many cases i think the vast majority of cases in the business intelligence space we're just working with basic questions about what happened in the past you know and so in those cases yeah i think the what's interesting is um it's a simpler scenario and a more common scenario for everyday people who aren't engaged in science but it's also one that we just botch we just really have a lot of cognitive flaws about the way we ask and answer very basic questions with data in my experience and so we try to talk a little bit about the reasons why we get those things wrong you know sometimes over generalizing you know it's not just that um instead of saying people in the survey um liked product a more than product b we run around the building and say product a is the winner you know it's it's it's what's going to be better it's it's what everyone likes more and then someone says wait a second what was your sample size again on that survey you ran you know and so we tend to and um i include myself in that we tend to overreach we tend to almost treat it as if it were scientific so i the short answer is i think there's a very big difference between data literacy and scientific literacy there's an overlap there because scientists use data don't they but i think a lot of times when we're using data it's not in a scientific pursuit even though we call it data science well those questions about predicting things that are going to happen those are getting more into ground that is to me much more similar to science because you're trying to make you know predictions and forecasts you're trying to adjust processes automatically based on some rules that you learn about the way things work based on what's in the data and then there's just the general analysis or reporting kind of a role where it isn't very scientific i don't think or i would say it's not like like pure science right so anyway that's a bit of a long-winded answer but hopefully that makes sense i do think there's a lot of ways we we say literacy matters financial literacy media literacy data literacy these are all just kind of becoming proficient in areas of our life in which you know maybe we're being asked to do things we we weren't asked to do before um so i think that's also important so let me stop there nathan i'm turning back over to you i know we're out of time yeah i was actually gonna ask you if you have another like five minutes uh tomorrow asked a really good question that i oh yeah i do yeah that's okay yeah perfect have you seen it it's in the chat and it was tomorrow yep tamara peterson i love your point about expanding our critical thinking skills what advice do you have for sharing our analysis with those that are not data savvy or possibly don't have complex critical thinking skills yeah so first piece of advice is assume they do have great thinking skills and speak to them as if they do in the mature phase we do talk though about ways you can step someone into an understanding of something you know we show people slides with a whole bunch of boxes right and what do we do we build the slide step by step one box then one arrow then another box we've been doing this with powerpoint for years now but when it comes to data we just throw the whole chart up there and slap it in the face there you go there's the chart well why don't we build a chart why don't we show an axis and then put the dots out on the x-axis and then move them vertically based on how the other variable you know and then add size to the dots and talk about what that tells us and then add color to the so you take a scatter plot you know how can you actually kind of gradually think of it like you know part of what i'm doing right now is and i need to find a way to do this that's not condescending to my audience but i'm trying to teach them about what it is and it's a training it's a presentation about the data but to do it well i need to train them i've been looking at the data for weeks now and it's just ingrained in my brain but i need to remember they haven't seen it yet i'm going to show them a chart that i've been looking at and it's just you know i can probably tell it to you in my sleep but they haven't seen it yet so how do i get them to the place where they come along and understand and i think that it involves yeah i don't think you need to spend a lot of time on it like i can make a chart i think we have an example of it here i'm talking about this example of a uh of a scatter plot right so i'll just show you what i mean real quick uh that would kind of illustrate a practical point i think um about oh that's just so that's just so not surprising okay hold on i'm using a different um i'm using a different uh browser than i normally use here all right so let's see if we come in here maybe i'm hiding it and that'll be bad uh yep i sure am oh no it's right here so here's my point right i can say okay i'm going to tell you i'm going to show you how life expectancy correlates with urbanization and i say well hey let's start by placing all the 200 countries on the horizontal axis based on the life expectancy so there's you know cambodia over here over here we have iceland in terms of how long people are expected to live and then you know hey let's move these dots we have all these states up here why don't we just move them based on what percentage of people live in a city oh interesting look at this kind of curved shape maybe we can see if there's interesting something going on here by by region let's take a look at that you know and see our european countries over here sub-saharan african countries what about the big countries where are they oh they're over here china india and then let's you know what let's just set this thing in motion so let's just see what happens if we move forward you know from the 70s and then i can speed it up and go all the way up until today so you see this is what i mean and now if i just show this thing to an audience they're going to be like what are all these dots and they're going to be overwhelmed but i think it's not that hard to make simple steps and build simple steps into our presentations to communicate to them in a way that helps them get caught up you know bring them up to speed in a way that's i what did that take me like a minute to 60 seconds i'm terrible at guessing time when i'm presenting but i do think that was a fairly easy build and we do that with slides why don't we do it with data you can be a data literacy champion by doing this kind of a thing and you know taking it upon yourself right to teach to teach what it is that you're trying to show so there we go i'll leave you with that practical tip that we bake into uh into our course so how we doing nathan another one or should we call it good i think we should call it good because i have a couple of things that i need to go over before we all sign up um so i'm just going to take over the screen here so i can show uh on and maybe i can get our faces out there we go so okay so uh on friday um june 17th at noon we have jimmy nguyen two he's gonna be presenting to and through data science um for all of you who were here today asking questions about you know career-oriented questions or uh even tomorrow's question about um you know giving a presentation to i'll say non-technical uh people um jimmy's gonna be a really good resource for you he's talking about his his journey through data science and you know his highs his lows um you know how to be triumphant he did a part-time masters for seven years and he's gonna so he started out in accounting and finance and now he's a senior data scientist at linkedin so if you have any career oriented questions i would really push you to uh join our webinar on the 17th and then going back to tomorrow's question about uh data-driven presentations uh july 6th we actually have a webinar on that so tomorrow um i think you should or anybody else that also has that question i think you should join our webinar on july 6th um thank you very much ben it was a pleasure having you and uh thank you fatima and uh megan for for helping get this set up um thank you all for joining thank you all for your questions i think this was a really good session and hopefully we'll see some of you if not all of you on friday and i hope everybody has a good rest of their day