Welcome to episode #199 of the Super Data Science Podcast. Here we go!
“When do you use and not use a pie chart?”
This question is easy to answer for some but it still is one of the common mistakes done by data practitioners.
So, in today’s episode, Kristen Sosulski, a data visualization expert, is going to answer all queries related to data visualization so you’ll be more than prepared for your upcoming presentations.
About Kristen Sosulski
Dr. Kristen Sosulski is a Clinical Associate Professor of Information, Operations and Management Sciences at New York University Stern School of Business. She has long been passionate about sharing his expertise in data visualization through her courses, workshops, and publications. She is the author of the book Data Visualization Made Simple which was just recently published.
Writing a book let alone a series of books wasn’t part of Kristen’s life plans. She has long been passionate about technology, learning sciences, and data visualization. Rooted from these passions, being in the education sector is what she saw as her entire future. But when she started teaching business executives and MBA students, she saw something that needs addressing. She saw that though there are a lot of fantastic data visualization books out there, they don’t cater to her teaching style and the needs of the people around her. So, after that, she started writing books on data visualization.
Her third book, “Data Visualization Made Simple,” serves as a practical guide for everything data visualization. This is an easy-to-understand book for everyone in any field to help them in the field of data visualization and do more with the data that they have. For example, business leaders should consider including data visualization as part of their leadership skill set. Kristen says that properly communicating data insights and reports to their clients or their team is fundamental.
Make sure not to miss Kirill and Kristen discuss some chapters from her latest book. There are many hidden gems that could help you improve your visualization skills. Learn how to avoid the common data visualization mistakes from Kristen herself. It’s important that you use the right kind of visual representation to have an actionable response from your audience.
A great visualization involves two things: a clear takeaway and a clear picture. Everything is about how you ‘walk’ your audience to reach their destination. So, to achieve your own great data visualization, be mindful of the details when preparing. Understand the density of your data, show it to other people in advance, consider what format you’ll use, etc. Kristen and Kirill also share their favorite data visualization resources so that, in the future, you could use it too.
There’s a lot more that Kirill and Kristen discuss in the episode – from the common mistakes in data visualization to evolution of the field to the possibility of job takeover of technology in this field. Another interesting takeaway from this episode is that Dr. Kristen Sosulski will be one of the speakers for the DataScienceGO Conference next year (2019)!
In this episode you will learn:
- Kristen shares her experience on her data visualization journey. (04:55)
- What struck Kristen to start writing books in data visualization? (08:54)
- Kristen’s experience as a data visualization educator for business leaders and MBA students. (11:20)
- Data Visualization in Art. (16:44)
- Does getting in data visualization need programming skills? (18:40)
- What makes a great visualization? (20:26)
- During presentations, you should be the center of attention and your visualization materials should just be assisting you. (25:00)
- Kirill and Kristen go over some chapters on Kristen’s latest book. (26:55)
- How to properly use visual representations
- Formats of Data
- Getting to know the audience
- Common mistakes to avoid in data visualization. (49:30)
- Top favorite resources for data visualization chosen by Kirill and Kristen. (52:31)
- What fascinates Kristen about data visualization (56:45)
- How is data visualization evolving? (58:30)
Items mentioned in this podcast:
- Data Visualization Made Simple: Insights into Becoming Visual by Kristen Sosulski
- NYU Stern School of Business
- Deconstructor: An Online Film Analysis Tool
- Tools: Microsoft Excel, Tableau, IBM, Google Charts, Gartner Magic Quadrant Research Methodology
- Mike Bostock’s Blocks – bl.ocks.org
- DataScienceGO Conference 2018 | October 12-14 | Marriot La Jolla | San Diego, California
Kirill Eremenko: This is episode number 199 with associate professor at NYU Stern's School of Business, Kristen Sosulski. Welcome to the Super Data Science Podcast. My name is Kirill Eremenko, data science coach and lifestyle entrepreneur. Each week we bring you inspiring people and ideas to help you build your successful career in data science. Thanks for being here today, and now let's make the complex simple.
Kirill Eremenko: Welcome back to the Super Data Science Podcast, ladies and gentlemen. Very excited to have you on the show, and today I've got a super interesting guest for you. Joining us all the way from New York is Kristen Sosulski, who is the associate professor at NYU's Stern's School of Business. What you need to know about Kristen is that she teaches people how to visualize data for a living. That is her job to teach people how to visualize data, how to get insights, how to present the findings, and not just to just anybody. Kristen actually teaches managers and leaders and people who go to the NYU Stern's School of Business. As you can imagine, she has tons and tons of experience, not only in the aspect of visualizing data, but also communicating findings and presenting the insights and helping people better understand how to read data and how to understand charts and graphs and all of these amazing things that we can create in the space of data visualization.
Kirill Eremenko: This has been an amazing podcast. I'm very excited for you to hear. Some of the things that we discussed on today's show were Kristen's third book, which is coming out now. It's actually available on preorder. At the moment when you hear this podcast, it's actually coming out on Amazon, so make sure to check it out. It's called Data Visualization Made Simple. We also talk about visualization for managers and leaders and why that's important. On the flip side, we talk about using visualization as an entry pathway into data science. At whatever state in your career you are now, this is going to be helpful for you. Whether you're a manager or leader or you're just starting out into data science, you will see how you can use the power of visualization to your advantage.
Kirill Eremenko: We'll go through Kristen's top tips for visualization. This is something you don't want to miss because Kristen has been doing this for a very long time and she knows exactly what people need in visualizations. In fact, we'll actually look at some examples of visualizations in this podcast. Kristen will walk us through how she thinks about visualization in two specific case studies that I will just randomly throw at her, which is quite a fun experience. Plus, of course, lots and lots more things you'll learn about Kristen's personal journey into data science and the space of data visualization. We got a jam packed podcast, lots of exciting and interesting topics. Can't wait for you to check it out, so let's dive straight into it. Without further ado, I bring to you Kristen Sosulski, associate professor at the NYU Stern's School of Business.
Kirill Eremenko: Welcome to the Super Data Science Podcast, ladies and gentlemen. Today I've got a very exciting guest on the show, Kristen Sosulski. Kristen, welcome. How are you doing today?
Kristen Sosulski: I'm doing great. Thanks so much for having me.
Kirill Eremenko: It's so great to have you. Tell us where you're calling in from.
Kristen Sosulski: I'm calling in from New York City.
Kirill Eremenko: And the weather there is not the best right now?
Kristen Sosulski: Not the best. Well, it's close to 70 degrees, but it looks like it might rain.
Kirill Eremenko: I just made the mistake of just before we started making the comment that you guys are moving into winter. What was your reaction?
Kristen Sosulski: No, we're barely in fall.
Kirill Eremenko: Yeah. I heard New York is a beautiful time to visit in the fall. Is that true, like when the leaves are coming off?
Kristen Sosulski: I think it's the absolute best time. Definitely need to visit New York before Thanksgiving, before the holidays pick up. It's really a great ... Right now is the best time to visit New York.
Kirill Eremenko: Okay, awesome. That's really cool. Very jealous of you and I would love to, like in a good way obviously, I would love to see New York in the fall. Okay, well thank you again for coming on the show. We've got some very exciting topics to cover. Kristen, you are into the space of data science and visualization, and you have been teaching this topic for quite a long time in different universities based on what I can tell from your LinkedIn, so I'm very excited about diving into this space and learning about your background, learning about your journey. But to get us started, could you, for the sake of our listeners and everybody who's tuning in to this podcast, tell us how you would introduce yourself to somebody off the street. Who are you and what do you do?
Kristen Sosulski: Okay. My name is Kristen Sosulski. I'm a professor at NYU Stern's School of Business. My area of, my passion, my area of scholarship lies in data visualization technology and this new field called learning science. It's using technology for education and to help learning, and visualization actually plays a role in that, so I'm kind of really in a lucky spot right now in my role as a professor.
Kirill Eremenko: Gotcha. So on one hand you're passionate about creating visualizations and explaining data and information through visualization. On the other hand, there's this whole new field of learning sciences, as I understand it, where you use visualization to aid and facilitate the learning process. Is that correct?
Kristen Sosulski: Absolutely. Absolutely. I just released my third book on data visualization. It's called Data Visualization made simple.
Kirill Eremenko: Oh, congratulations.
Kristen Sosulski: Thank you. It's really intended to help anyone who is looking to get into the field of visualization or just do more with the data that they have.
Kirill Eremenko: Oh, that's so cool. That's so cool. Let me check it out. Oh, I can see it on Amazon. Oh, that's so awesome. Kristen Sosulski, Data Visualization Made Simple: Insights Into Becoming Visual. Wonderful. There's another whole topic we're going to dive into. And third book, that is crazy. I'm going to ask you this. I released my book at the start of this year. It took me one and a half years of writing. I was very excited about it, still am, but it's such a complex process, way more complex than I thought, and so much more involved than I thought that this is one thing that I'm not even sure if I'm going to write a second book. Maybe, possibly, but I wouldn't jump into it. How about you? This is your third book. Where do you find the inspiration to write them?
Kristen Sosulski: When I finished my dissertation, I was like, "Okay, I'm never writing a book." And then when I co-authored my first book, which wasn't even [inaudible 00:07:42] I was like, "I'm never doing this again." For some reason, it just kind of struck me. I was like, "I need to write this book on data visualization." Because all the books out there are fantastic, but there was something that was missing that didn't really go with my teaching style and meet the needs of people in the world of analytics and business and data science. There just needed to be a little bit of a different take, and so I saw an opportunity to try to fill that gap.
Kirill Eremenko: Okay, gotcha. So it's more like your need and desire to contribute to the world, it overpowered other aspects that are involved in writing a book and the fear, I guess, that comes along with looking ahead at this huge project that you're about to undertake.
Kristen Sosulski: Yeah, yeah. And then not having to be social for about a year and a half, you know? [inaudible 00:08:48] every weekend and every work night.
Kirill Eremenko: I know. I know, yeah, yeah. Well, very excited about that. I'm going to pick a copy up myself and highly recommend to all listeners Data Visualization Made Simple by Kristen Sosulski. You can get on Amazon. Very, very interesting. I love data visualization. If you don't mind me asking, do you have pictures in your book?
Kristen Sosulski: Do I have pictures in my book? Yup, of course.
Kirill Eremenko: I love books with pictures. They're the best. It's very easy to read. Yeah, I was joking. Of course a visualization book's going to have pictures, and yeah. I always like to browse through books to pick up some ... One I really liked reading, or even just looking through, was it called like A Year of Visualization where two ladies, one in New York, one in London, they were sending each other postcards and they were doing these hand drawn visualizations, and then for a whole year, like once a week. There was 52 times two, 104 visualizations in there about what they did in that week. It was really cool. You can get some great inspiration for your own visualization from books like that.
Kristen Sosulski: Oh my gosh, yeah. That's amazing.
Kirill Eremenko: Yeah, I'll find the title and share it with you. All right, so we've got a book that you just published and you work at the NYU Stern's School of Business, so tell us a bit about that. Are you teaching visualization or are you teaching other topics through visualization?
Kristen Sosulski: Great question. I teach a course called Data Visualization to executives and MBA students. I'm also teaching a new online certificate called Visualizing Data that's open to anybody in the world, so you don't have to be matriculated at Stern, and I'm launching that in the spring. Yes, I'm very lucky. I get to teach visualization at school. Part of that is really making the business case for why visualization is so important for managers, and it's really a leadership skill. Being able to communicate, right, your data insights, your results, through visually to any audience is critical.
Kirill Eremenko: Mm-hmm (affirmative), yeah. Definitely. That's a very interesting space for, as you say, leaders, executives, managers to see the power of visualization. Do you find that it's usually when students attend your class for the first time, do you find that this skill is underrated in their eyes and then you have to turn it around, or they're already quite proficient and you just need to add some extra powerful skills into their arsenal?
Kristen Sosulski: That's a great observation. It's underrated. When students take my class and when they've completed it, they can't look at a chart or graph the same way ever again. I think it's something that is not so clear from the beginning that, "Oh, I'm going to be the person creating these visualizations." It's more like, "Oh, I'm going to have an intern do this when I'm a manager." As the class progresses, it becomes clear that we have to find ways to communicate these complex analyses that we derive to our stakeholders, whether it's prediction customer conversion or identifying new markets, well designed data graphics can really reveal and translate important information.
Kirill Eremenko: Mm-hmm (affirmative), mm-hmm (affirmative), [crosstalk 00:12:38]
Kristen Sosulski: So I really, yeah, and it shows ... If you could make a great data graphic of your insight or result, it shows that you understand your data, and now we can talk about taking actions or making decisions with that data.
Kirill Eremenko: Mm-hmm (affirmative), yeah. Yeah, that's definitely, definitely true. Do you find people who attend your class, they're like receptive to the idea of learning data science? For instance, I can imagine there could be executives or managers who just have the mindset that okay, data visualization is powerful, however just as you mentioned just before, that like I'll have somebody else do it. I don't need to be able to do these. And rightly so. A lot of managers, they don't have time to sit down and create a visualization. What are the benefits for managers who will never actually be creating these visualizations themselves, what are the benefits of them actually having these skills or understanding how visualizations work?
Kristen Sosulski: Oh, that's a great question. First off, I would say to your first question about-
Kirill Eremenko: [crosstalk 00:13:47]
Kristen Sosulski: ... how the students kind of, yeah, are they receptive to this? I'd say absolutely. It's actually amazing how receptive students are. From my class, several students have even created their own data viz consulting firms, which I'm like, "Whoa."
Kirill Eremenko: Wow.
Kristen Sosulski: It's amazing.
Kirill Eremenko: That's awesome.
Kristen Sosulski: It's really an often overlooked area, and the way I sell it is it's really the extra 20% that you need to put in. Whether you're writing a business report or creating a website or dashboard for executives, it's the extra 20% that really helps reveal those important insights so someone can take action. If you're not building them yourself, that's totally fine, right? There are people that are really expert in not just visualization, but in data modeling and data mining, really understanding the ways in which data can help with prediction and other aspects. For managers, it's having that knowledge to be able to lead and critique and offer advice to their colleagues that are doing this work. Not just accepting things at face value, but really to know how to ask the right questions.
Kirill Eremenko: Yeah, yeah, that's what I was thinking as well. For me, for instance, the skills I learned back when I was in consulting and doing visualization there really helped me understand visualizations more. Even taking it further, like when I was a kid and I attended art school and you'd learn how to paint, I never thought like maybe I would become a painter, but I didn't. But still, those skills, they help me understand better how colors work together, what elements are standing out and what elements should be standing out and why they're not and so on. The whole concept of aesthetics, I think is important for people to develop that as well.
Kristen Sosulski: Oh, absolutely, absolutely. Everything from recognizing that certain hues together can't be really perceived that are colorblind or having acute colorblindness, and so that's really important. And just basic readability. Can I see that chart from the back of the room? Can I read the Y axis? And then just having the consideration of the audience, right? Like just because you put a chart up there doesn't mean that everybody understands the key takeaway. And so [inaudible 00:16:34] those explanations and really walking folks through that chart or graph.
Kirill Eremenko: Yeah, definitely, definitely agree with that, and tell us a bit about how did you get into this space? What made you get started into the area of visualization? Was it a conscious choice or did you end up here by accident?
Kristen Sosulski: Well, I've always been involved in technology, and the way that I got into visualization was really a unique story. But in a nutshell, I was working for this education center at Columbia University, and I started working with this film professor. We were creating digital educational technology projects to help students learn. The idea was to look at a film, a particular scene in a film, and be able to deconstruct that and do it all over the web. This is in 1999, to actually look at a film and to be able to cut it into little, small pieces and to be able to analyze each particular shot in a film. Through that analysis process, we were stripping out all the narrative and dramatic content and just focusing on the structural elements of film. We used quantitative values to describe what was happening shot by shot in the film. At the end, we actually visualized that through a data visualization.
Kirill Eremenko: Wow.
Kristen Sosulski: To be able to visualize art was such a ... It just, it totally made my head spin at first. It was such an amazing project that from there on out visualization became part of my practice, together with teaching, like I said, and my work with technology.
Kirill Eremenko: Wow, that is so cool. I would have loved to see. Do you have the results of that project available somewhere still? I know it's [inaudible 00:18:23]
Kristen Sosulski: I do. I do. Yeah, it's called The Deconstructor. I can definitely send you a PDF. We did a little research report on it.
Kirill Eremenko: Nice. Is it okay to include it in the show notes for our listeners?
Kristen Sosulski: Absolutely. Absolutely.
Kirill Eremenko: Okay, yeah, please send it through. I will definitely do that. It sounds like an interesting project. I can totally see now how you fell in love with this space and visualization is such a great area. For the benefit of our listeners, visualization, like a lot of time, like not a lot of time, but many people have asked me if you can get into the space of data science without getting heavily involved in programming. Some people just don't like programming or aren't really passionate about learning how to program, but they are passionate about data science. They see the power there. What would you say in that case, Kristen? Is it possible to get into the space of data science analytics without having to learn programming too heavily?
Kristen Sosulski: Oh, absolutely. Absolutely. I'm a coder myself and I think that there are tools that are available, like Tableau or you could even use Excel, that allow you to create dozens of visualizations without knowing so much about coding. The key is to really understand your data and what your data represents in the real world. Without an experience in coding, you still have an opportunity to use these tools to visualize data, so absolutely. Again, the key is knowing what your data represents in the real world and knowing if the visualization you create is accurate.
Kirill Eremenko: Yeah, yeah. Totally agree with that. Visualization is your pathway into data science. It's like a quick way to get into the space of data science. Whether you want to later on learn machine learning programming skills or not, visualization skills are going to be very beneficial in either case. Well, on that note, let's shift gears a bit. I wanted to pick your brain on some tips and hacks in visualization. How does that sound?
Kristen Sosulski: Sounds great.
Kirill Eremenko: Okay. First question would be, what goes into a good visualization? What is the difference between, or let's say great visualization. What's the difference between an average or a good visualization and a great visualization that actually delivers where it's supposed to? I understand there will be lots of different elements, lots of different details, but what are the key cornerstones of a great visualization in your opinion?
Kristen Sosulski: Okay, so I would describe the most important thing of a visualization is that there is a clear takeaway. I call this the party favor. You know when you go to a wedding, at the end of the wedding you usually get a little trinket or something to remember this day.
Kirill Eremenko: Oh, yeah.
Kristen Sosulski: You have to make sure your audience walks away with that little trinket or that party favor. So important, otherwise why did you create it in the first place? It's so important that your message resonates with your audience. There's a lot of tricks and hacks to make sure this happens. One, show it to other people in advance. Don't be afraid to show your work and see the reactions. You're almost doing a test of how well one can perceive and interpret this graphic.
Kirill Eremenko: Gotcha. Okay, yeah, totally agree with that. The way some people see visualization and probably the way I saw it before, is you have something in mind, like you put it together and depending on your experience in the space, and I was not experienced at all when I was starting out, you might already have something close to the [inaudible 00:22:16] or further away, but in essence, anything you come up with in your head at the start is probably not going to be the final product. It's going to have adjustments and different elements that you weren't expecting or something might not fit in. You might have to cut something out.
Kirill Eremenko: It is an intuitive process. Visualization, inevitably you're going to have iterations of what you're creating. Starting out and trying to go for the perfect solution right away I think is a mistake. I think you need to start out, you put a prototype together, and as you said, Kristen, show it to other people. Get their opinion and see how they react to it, and then adjust it based on that. Then, go through another iteration, another iteration. Would you agree with that, that it's an iterative process?
Kristen Sosulski: It's an iterative process. First you start with, I would almost say it's first an exploratory process. As you understand and develop a data understanding or understanding of your data and you start asking better questions of your data, as you query it, as you choose to select different display types, as you choose to either aggregate or disaggregate your data, right? Are you going to show every point on a map or are you going to fill in just more geographic regions? Does that tell your story better? Dealing with the amount of data or density of your data is also very important. What level of grain are you going to show?
Kirill Eremenko: Yeah, yeah. Exactly. Exactly that. Yeah, that's probably a great starting point. A good visualization or a great visualization has to have a clear picture, a clear takeaway that a user's going to get, and you got to show it to other people in advance to iterate that process correctly. Anything else?
Kristen Sosulski: Oh, absolutely. The second thing is consider the final format of your visualization. Are you going to be presenting it in a room of 1000 people like on a PowerPoint or keynote, or something that your audience is going to interact with online or on their phones? Or, is it a report that you're giving stakeholders that's printed out? That format really does make a difference on how you design it. You design for interaction if it's going to be online. You design for clear readability and you probably add a lot more text if it's going to be printed. If you're going to show it, you're probably going to not show as many details and think about your role in narrating and walking someone through that chart.
Kirill Eremenko: Mm-hmm (affirmative), mm-hmm (affirmative), yeah. That's a very good point. Tell me, I'd like to get your professional opinion on this. My thinking around visualizations, especially in the case or specifically in the case when you're presenting it, is indeed, it's very different to if you just hand it over as an interactive online tool or report. Because in the case when you're presenting it, I feel that the audience's attention should be on you rather than on the visualization. The visualization should be assisting you and therefore should be minimal text, minimal confusing things. Should be one picture at a time, and then you tell the story, and people are focusing on you rather than reading the slides. Do you agree or do you have a different opinion on that?
Kristen Sosulski: I absolutely agree. One of my favorite visualization designers is Donna Wong, and she says precisely that, that she is the presentation.
Kirill Eremenko: Mm-hmm (affirmative), yeah, yeah. But it's different though if you send it, like you say, as an interactive report or a PDF report. You're not there, so it becomes a whole art. How do you incorporate yourself and your story into the visualization as like through footnotes or as call outs and other ways like that. That's very interesting, isn't it?
Kristen Sosulski: It really is. In a report, how would you guide somebody to look at the particular aspects of the chart that you want to draw their attention to? You might use different colors or shading. You might use call outs. You might show the graph in different stages, almost like a progressive reveal, frame by frame with some text in between that explains what's happening so you can pace the reader as they go through.
Kirill Eremenko: Mm-hmm (affirmative), mm-hmm (affirmative), got it. Okay, that's a very interesting idea about doing it gradually frame by frame. That's very cool. By the way, while we're on this, is that what your book is about? Do you give tips on how to visualize things better and dissect visualizations in your book, or is it got a bit of a different angle?
Kristen Sosulski: No, absolutely, absolutely. There's a huge chapter all on the design and the aesthetics of visualization. There's a whole chapter on picking the right chart. That's all based and driven on your data. There's a whole chapter on data and different data formats and how those are really important to consider the format of our data to get the type of visualization that we want so we don't make errors. For those non-data-science folks, that chapter is really important. And then, I have a chapter on audience, which is how to relate and resonate with your audience with that key takeaway, and also a whole chapter on presentation, like different tips and tricks for presenting with data graphics specifically.
Kirill Eremenko: Wow, that's very cool. I'm so glad you included that, because a lot of the time that's a place where the dropout happens. People create a, do the analysis, do the insights, and even create a beautiful visualization, and then they don't follow through to really act as the bridge between the insights and the business decision makers. That's where the real value is, right? The visualization can be amazing and the insights can be really life changing or business changing, but unless you can communicate it to the people who are going to act on them, what's the point?
Kristen Sosulski: You said it perfectly. Yeah, totally agree.
Kirill Eremenko: Okay, well, if you don't mind, without disclosing or giving away the whole book, let's go through a couple of these chapters and maybe you can give us one tip from each one of them. How does that sound?
Kristen Sosulski: That sounds great. That sounds great.
Kirill Eremenko: Okay. Well, let's start with the one, with the pick a chart chapter. How do you pick charts? Favorite question everybody has about pie charts, when do you use and when do you not use a pie chart?
Kristen Sosulski: Okay, so I'll answer the second question first. Okay, so every pie chart can be converted to a bar chart. If you're ever in question, "Oh, should I use a pie or a bar?" well, you can always use a bar, but you can't always use a pie. Why? Because you can only have a certain number of slices in a pie. You know that as soon as you put more than six or seven slices of a pie, it's really hard to distinguish between those different areas, right? Especially if they're kind of close in size. We're just better as human at perceiving length over area. Picking a bar is generally a better choice, but I'm not one of these people that's like, "Oh, you can't have a pie chart." If you want to have a pie chart for some variety, I think that's perfectly fine, as long as it's saying something.
Kirill Eremenko: Thank you.
Kristen Sosulski: If you're showing a pie chart that's split in half with 50/50, that's not really saying much.
Kirill Eremenko: Yeah, yeah, thank you. I understand the whole hassle about pie charts. I agree, if it's got ... I would even go as far as saying more than three parts of a pie, like a bit too many. But sometimes people are so adamant about don't use pie charts. I agree with you. If it fits, if it looks good, if it says a story, use a pie chart. Other than that, try to stay away from them, I guess.
Kristen Sosulski: Yeah, definitely. If you're just saying you want to show proportion of a whole of people who use different types of devices, like their laptop versus mobile versus tablet, okay, well you can show that in a pie chart, and it might actually show you very clear the proportion of people that convert, buy your products on their iPhone versus their tablet. That's, I think, a fine use.
Kirill Eremenko: Okay, gotcha. Now, moving on to that other question, how do we go about picking a chart? There's so many different types of charts to choose from. How do you think about this?
Kristen Sosulski: Besides thinking about the question that we want to ask of our data, we really have to have an understanding of our data. If we have time series data, this means that now we can choose time series displays. This means line charts, area charts, for instance. But if we don't have time series data, we can't pick a line chart. Same for if we want to map locations, we need geospatial data. I'm not going to map locations, it's probably not going to be a great use of a bar chart to map 30 locations. It can be very hard to see those differences. But perhaps if I want to show location, I could do that on a map. I would need latitude and longitude or I would need a zip code or area code or a country code, some type of geospatial data. The data does really limit your choice.
Kirill Eremenko: Mm-hmm (affirmative), mm-hmm (affirmative). That's a good point. Okay, so let's say we've narrowed it down. Let's say it's time series data and, for instance, I have a specific example. How do I choose between ... Let's say I'm plotting the unemployment rates for the US in the past 10 years month by month, and I can either plot is a line chart, and it's split by age groups, like 18 to 25, 25 to 35, 35 and so on. I can plot it as a line chart and I'll have five lines on one chart, or I can plot it as an area chart, for example, where you have the first 18 to 25, and then there's all shaded in, and then after that you got to the next line above it to stack on top of each other and they're shaded in.
Kristen Sosulski: Yes.
Kirill Eremenko: Which one would you choose? I've encountered that dilemma before, and both are valid. Both represent the data quite well. But how do you make the decision which one is the best one?
Kristen Sosulski: Great. That's a really great question. If you want to see the proportional change in unemployment amongst the different groups, this is where you would choose your stacked area. Visually, your stacked area also looks very compelling. If you were to show the stacked area in a presentation, those colors or different shades would be very vivid, right, and you could label directly in those areas, so it could tell a very compelling story.
Kristen Sosulski: Another great thing about the stacked area is that you can make it a 100% stacked area, or you could just actually use the absolute values. Then you can see the percentage change, which is also a nice telling metric. You have a few more options with the stacked area. Also for interactivity. If viewers are going to be seeing this chart online, being able to mouseover and just click on a particular stacked area could reveal additional information, so you have the opportunity to add additional variables, for instance, for each data point.
Kristen Sosulski: The line chart is great and it will tell you literally those values and each value for each month for category or demographic, which is fine. I think for showing just the unemployment rate in a general trend is best with a line, but in the circumstance that you pointed out, I would like to see that as a stacked area.
Kirill Eremenko: Okay, gotcha. Thank you for explaining it so succinctly. Yeah, I can see now that if you want to compare them one against the other, or you have like, as you said, it's more vivid if it's a area chart. That's very cool. Well, let's do another one.
Kristen Sosulski: Okay.
Kirill Eremenko: This is fun. This is fun. Because I know in your LinkedIn profile you said that you do consulting in the space of data visualization-
Kristen Sosulski: I do.
Kirill Eremenko: ... so we're getting a free consultation right now, so might as well make the most of it. Okay, let's say I have categorical data. Let's say I have sales by different product. Let's say we sell chairs, tables, and all these different type of furniture. I want to compare them and see which ones are selling better, which ones are selling worse, and what's going on, maybe sort them by highest sales volume sales to lowest. Would I use a bar chart or would I use a tree map? And just for the sake of our listeners, if you're just starting into visualization, tree map has nothing to do with trees. It's just like a big box that is ... You've probably seen this chart where there's like the biggest part, and then there's boxes inside of a box. There's boxes split into lots of little boxes. I'm probably not doing well explaining it. How would you describe a tree map, Kristen?
Kristen Sosulski: A tree map, a famous scholar by the name of Ben Shneiderman came up with [inaudible 00:36:24] algorithms for something called a tree map. It's the arrangement of categorical data by proportion, so it might be by proportion of profit, proportion of sales by product. The larger the rectangle with ... Picture one large rectangle and dissecting that into 10 pieces, and each of the 10 pieces would represent a product. The size of those 10 pieces would all be different based on some numerical value like sales or profit.
Kirill Eremenko: Wow, described by a professional. That was such a great explanation. I think everybody can totally understand that, even if they've never seen a tree map before. Yeah, going back to the question. Tree map or bar chart to describe volumes of sales?
Kristen Sosulski: Okay, volume of sales. For instance, if you were going to show the most popular products by, say, sales, I would love to see that as a horizontal bar chart to show rank, okay? Clearly the longest bar would be on top of the range horizontally and I would know that like, wow, those beautiful Cherner chairs are selling and they're very profitable and they're our biggest seller at $1000 a pop, right? That would be for popularity, we like to use horizontal for any kind of rank. If you want to see ... Let's make the example a little bit more-
Kirill Eremenko: Sophisticated, yeah.
Kristen Sosulski: ... [crosstalk 00:38:01] We have a tree map that represents every furniture product by a large category. We would have something like chairs as one big rectangle. We'd have something as tables as another rectangle. Let's say that there's 10 different types of furniture product, end tables, coffee tables, desks, and showing which area is more profitable. We have this view of our business, furniture business, and the largest rectangle would show us which product area is most profitable. Then I can drill down and click on, let's say they are chairs, click on that large rectangle that says chairs, and then I can zoom in and see which chair is now most profitable.
Kristen Sosulski: Tree maps are great for interactivity when you want to drill down. You get the big picture. You get the big picture at first. Out of all the furniture in my furniture store, which category is the most profitable? Oh, chairs. Now I can click on chairs, drill down, and I can see which type of chair is most profitable.
Kirill Eremenko: It's kind of like a tree map inside a tree map.
Kristen Sosulski: Exactly. They're best used when you can use them on a dashboard display or web-based display where you can drill down and interactive. Less useful if you're presenting it to an executive. They're pretty hard to interpret.
Kirill Eremenko: Mm-hmm (affirmative), mm-hmm (affirmative). Okay, interesting. That even means that if you have the same insights and if you were presenting them to executives, you might use a bar chart. But then once you deliver that presentation and now they want that interactive tool, you might change it up and send and create a tree map inside a tree map type of scenario and send that, because it's better for interactivity.
Kristen Sosulski: Absolutely. Absolutely. And with maybe a sentence or two or a minute or two of training just to describe what this display is actually doing, just so they understand the use. Because, like I said, it's not something that all of a sudden we see a tree map and we immediately understand what it means.
Kirill Eremenko: Gotcha, okay. All right, well thank you for those quick insights. I think there's great two examples of picking a chart, even though simple. [inaudible 00:40:31] sense, right? Like right now we got a few listeners, maybe a few hundred listeners, listening to this who are like, "Well, I'm in machine learning. I want to go into that space. Visualization's not for me." Just for the benefit of people in that mindset, I want to say that visualization is for everybody. That is where ... That is the language. Machine learning is great and programming is fantastic, but that's the language of computers.
Kirill Eremenko: At the end of the day, the value that data science brings is how much does it add to the bottom line of a business, or how does it change lives? How does it help a non-profit? What is the actual change? That is translated through business decisions. As we already mentioned in this podcast, you need to be able to communicate your insights to people who are making these decisions, and therefore visualization is important. In this case, we looked at two relatively simple examples of visualization, but even I for myself already learned something about area charts and tree maps and so on. Think of it as a great start.
Kirill Eremenko: We're not going to go digging deeper into that. I'm sure you describe that quite well in your book, or awesomely in your book in that chapter on picking a chart. Let's move on to the next one. Let's talk about the formats of data. You said for people who are starting out into data science, this would be a valuable chapter. Tell us a bit more about that.
Kristen Sosulski: Oh, absolutely. A lot of times when we get time series data, for instance, it's not organized or structured in a way that we can visualize it. For instance, there might be a year for every column, okay? If you think about plotting something on X and Y axis and you want to plot all years on the X axis, all values on the Y axis, you would think that you would have a column for year and a column for value. A lot of times the data structure, especially if you get it from like the World Bank or something where you actually have a year for each column. Now you're thinking, "Well, what am I supposed to put on the X axis? I have to drag every year?" A lot of software programs won't allow you to do that. What you need to do is to take this wide format and actually convert it, or pivot it it's called. A lot of you might have heard of pivot tables, of course, to pivot your data. Now you have a column for every year and a column for every value.
Kirill Eremenko: Gotcha, gotcha. Yup, that's one of the examples, pivoting. It's kind of like translating data format from what humans are better used to reading and understanding where every year has its own column, to something that machines are better at reading. That's where all the years are in one column. All of the categories or all of the types of one category ... What is it called, by the way, when you have a category and then you have subelements in a category? The different years, what would they be in the year category?
Kristen Sosulski: Do you mean like there would be a different time dimension, or?
Kirill Eremenko: No, I mean like okay, we have a category of year, and then like each individual year. What is that called?
Kristen Sosulski: Oh, each individual year would just be like a value. It would almost be like observation.
Kirill Eremenko: Yeah, there we go. Such a silly question. All right, so each value in your category is contained within that one column. Okay, gotcha.
Kristen Sosulski: Yeah, and this is something that Hadley Wickham, who's at R Studio and has written a lot about this, but it's called tidy data. You have every observation in a row and every variable in a column. That's the foundation. Just taking a look at your data and making sure that it's in that tidy form is going to save you hours. That's like one of the biggest takeaways from the book. I have many others where we talk about how you aggregate your data or how you can present different metrics besides the values that you just have alone, so how you can calculate new metrics based on your data [crosstalk 00:44:34] those.
Kirill Eremenko: Yeah, that's also an important kind of like feature engineering type of thing.
Kristen Sosulski: Yeah, yeah. Or even something like the five day moving average or year over year or percentage change. Those types of things require a small calculation, and usually most of these software programs will have a function to do it so it doesn't take any coding. But just knowing that that exists is really important.
Kristen Sosulski: I'll give you one more example. Let's say I'm studying my customer base and I have their age. Now, in a bar graph I can plot every age of my customer, and that's going to be pretty boring, right? It's going to be maybe from 18 to like 82, and I'm going to have a bar for each age. What you can do instead is reduce the level of detail that you provide and actually group age into different bins. I might have 18 to 25 in one bin, 26 to 32 in another bin. This makes the data much more interpretable. I can look at these more logical groupings.
Kirill Eremenko: Yeah, and to your point, what I once discovered for myself was that when you're doing data visualization, you are always inevitably reducing the amount of information that you have. You have some data ... It's kind of like when sculptors are working with marble or something. They have this big block of marble, and then they carve out of it. They're always going to reduce the amount of material, the amount of whatever they started with to create the final result.
Kirill Eremenko: In visualization, it's okay to think about it in those terms. Like you said in this case, how about reduce the level of granularity and go from instead of one bar for every year, have a bar for 18 to 25, a bar for 25 to 35. There's nothing wrong with it because in visualization, if you think about it, there's no way for you to add data to your, add more information to your initial data. If you're doing that, then you are manipulating the data, then you are doing something, like you're making something up. Kind of like that mindset of yes, I'm going to, I'm just going to see how I'm going to reduce that information that I'm providing to my user in order to still maintain the insights that I want to convey to them.
Kristen Sosulski: Absolutely. Absolutely. Very well said.
Kirill Eremenko: Thank you. Okay, awesome. That's was on formatting data. Let's move onto the next one. The next one is how to relate ... Sorry, I forgot. What was the name of the chapter, the next chapter?
Kristen Sosulski: Oh, it's just called, oh the audience chapter?
Kirill Eremenko: Yeah, the audience chapter.
Kristen Sosulski: Yeah. Great. I think you started by saying how to kind of relate to your audience.
Kirill Eremenko: Yeah. That's the one, yeah.
Kristen Sosulski: Well, first of all, it's great to know a little bit about them. I know this sounds pretty obvious, but you might not think about this when you're creating a visualization, like really thinking about understanding your audience, but it's so important. What do they already know? What don't they know? We could take this, for instance, we talked about the tree map before. Are they familiar with more complex type of visualizations? If not, this might not be the time to introduce them to one, unless you're going for some type of wow factor or you're planning on taking quite a bit of time to explain it. This is just one example of ... or what they already know.
Kristen Sosulski: Another way to look at prior knowledge is to really think about how you could build upon it. Can you, in your narrative, can you build on something they already know, an experience you know that they already had? Even if it's like taking the subway to work or something like that, but something you know that there's some kind of common baseline that you can start from. It's a great way just to engage and get people paying attention and along with you for that narrative that you're describing.
Kirill Eremenko: Mm-hmm (affirmative), mm-hmm (affirmative), yeah. That's a great way of putting it. I've definitely been in a situation where I picked to explain something to my audience, like a certain type of distribution, and I knew consciously that they're not ready for this. I'm going to have to spend time on that. That's a great tip that know your audience and know what they know and what they don't know, and how you are you going to use that to your advantage. Fantastic.
Kirill Eremenko: Well, we're not going to go into the one on how to present because we talked about that a little bit already before. But what I wanted to do now is I wanted to go for a rapid fire list of questions, and so like get your opinion on some different topics. Are you ready for that?
Kristen Sosulski: I'm ready.
Kirill Eremenko: Okay. First one will be, we've talked about some good tips and hacks already on visualization and how people can enhance their skills. What are some of the common mistakes people make when they're creating data visualization? Some things that you've seen that really stand out and our listeners want to avoid at all costs.
Kristen Sosulski: Okay, so a common mistake as a professor is they forget to cite their data source, so they don't tell the audience where the data came from.
Kirill Eremenko: Yeah, okay. Yeah, that's big one, especially if you're using ... Like even if you're using internal company data, right? You still, it can come from so many different sources. It's important for even an audit trail to know that, right?
Kristen Sosulski: Absolutely, and make sure you put the year down. It's also important to cite yourself as the author of that data graphic.
Kirill Eremenko: Mm-hmm (affirmative), okay. Gotcha. Anything else?
Kristen Sosulski: Oh, absolutely. Another one is what I call data integrity or lying with data. Really easy to do this with a bar chart, not setting your Y axis to zero tends to over-exaggerate the change in the data that's not really there by an over-exaggeration of the change in the length of bars of the graphic itself.
Kirill Eremenko: If you don't set the Y axis to, like bottom to zero, is that correct?
Kristen Sosulski: Yes.
Kirill Eremenko: Yeah, oh, yeah, yeah. I know that one. Andy Kriebel from The Data School, The Information Lab, he talks a lot about that. I've heard him talk about it that, yeah. If your X axis crosses the Y axis at somewhere above zero or below zero, and you got bar charts, vertical bar charts, then you're in for a lot of trouble. It's going to be-
Kristen Sosulski: Absolutely.
Kirill Eremenko: Yeah, okay. Gotcha. All right, and maybe another one?
Kristen Sosulski: Okay, so color, so using color sparingly. We tend to like to use color to highlight. Sometimes that I see that people end up highlighting everything so nothing stands out. If you want something to stand out, you could use a contrasting color. I always say the most underused color in the data viz world is gray. I'm boring. I really like gray and I like to use color, like a bright green or any other color that would contrast with that if I want something to stand out, like my most important data point.
Kirill Eremenko: Gotcha, gotcha. How great is color? Like just by changing the colors in one visualization, you can take it from something that is average to a really great visualization by picking the right color combination.
Kristen Sosulski: Absolutely. Absolutely.
Kirill Eremenko: All right, cool. Next rapid fire question is what are some of the favorite data visualizations you've seen others create?
Kristen Sosulski: Oh my gosh, there are so many. I guess I'll just list them off. I love, basically there's one by Lee Byron and David McCandless which is peak break-up times on Facebook where they [crosstalk 00:52:53]
Kirill Eremenko: I've seen that one.
Kristen Sosulski: Yeah, that one is like so fun. I always use that in my class because how they go through that visualization, they have this progressive disclosure. First they show you the chart. They don't even tell you what the data is, so you have to think about it. The second thing is, then he puts the title of the chart, Peak Facebook Break-Up Times, you know? Then you start laughing. And then he annotates the chart for you. He says, "Okay, it looks like as low point might be around the holidays, and a high point for breakups is around spring break." And so, just the way he guides you through it is why I love it so much. But all it is is an area chart. It's nothing fancy. It's the way that it's delivered is why I love it so much.
Kirill Eremenko: Yeah, and humor is important, isn't it? You can deliver the same chart in a very dry, monotonous voice, or with a bit of humor. A bit of audience engagement makes the world of difference.
Kristen Sosulski: Yeah, yeah. And then, just like on a more serious note, Vox did this amazing video called The State of Gun Violence in the US explained in 18 charts. A lot of those charts are bar charts, and they use annotations, so somebody with a red marker actually marking off the different bar charts and annotating it as the narrative is going. That one is fantastic. I would definitely share that with your viewers.
Kirill Eremenko: Okay, okay. That's a good one. Anything else?
Kristen Sosulski: I love anything that Amanda Cox does from the New York Times graphic team. There's a famous chart about how people spend their time from the American Time Usage Survey of the US Census. One of the thing is that you can compare how employed people versus unemployed people spend their time. There's a little bit of humor there because there's a category for leisure, like movies, and you'll see over the course of a day the viewership of movies and television for unemployed versus employed people, and the answer's obvious.
Kirill Eremenko: Well, that's awesome. Yeah, there's quite a few gems online and some places to find them. Before the podcast, I mentioned Nadieh Bremer's visualcinnamon.com. That's a great source of fantastic visualizations really well made about professional topics and just some of her hobbies. Another one I know is, well obviously the Tableau Public Repository where you can look at the featured items that are quite cool. Michael Bostock has a website for D3. I think it's called blocks dot, bl.ocks.org or something like that. Yeah, and he has some really [inaudible 00:55:47] visualizations there. Anything else that comes to mind, like where people can actually find lots of different visualizations in one place?
Kristen Sosulski: Oh, I love the FlowingData website by [inaudible 00:55:59], yeah. I'm a big fan of Nathan Yau. He does a lot of visualization in R, and a lot of it is around topics that everybody can resonate with. Being someone coming from business, it's always fun to see visualizations that I'll never be able to create because they're much ...
Kirill Eremenko: Yeah.
Kristen Sosulski: Much beautiful and ...
Kirill Eremenko: Yeah. Yeah, FlowingData. FlowingData is a good one as well, so that something to check out. We'll include all of these links in the show notes as well for our listeners. Okay, cool. That was that question. Let me see what else we got here. All right, this one. What fascinates you about data visualizations? What's the thing that makes you get up in the morning and be so excited about your job?
Kristen Sosulski: Oh. Oh my gosh, so much. I mean, just that, gosh, it's such a tool for like just to investigate your data. It's such a pleasant way to approach a data problem by coming up with a question and being able to dig down and explore and struggle and wrangle with it for a while, and at the end come up with something that actually can help humans better understand a phenomena I think is amazing. And being able to have this medium at our disposal, that's what makes me wake up everyday, besides my family and my son.
Kirill Eremenko: Yeah, yeah. Totally, totally. It's very ... I'm actually quite glad we're not just machines, we just look at numbers and sometimes you look at Excel spreadsheet with thousand columns and million rows. Imagine if you could just look at it and understand it. How boring would that be, like when you didn't need visualizations? Visualization is so much creativity involved color, just feeling even. I think it makes things much brighter and this professional data science and analytics much more pleasant, I guess, and exciting to be in.
Kristen Sosulski: Absolutely. Absolutely. I think that we expect it these days too. We expect to have a visualization to help and guide with that interpretation.
Kirill Eremenko: Yeah, yeah, totally. Okay, next question. Interesting question on technology and the rate of change. We know that data science is growing exponentially. Technology is evolving exponentially. What do you find, in terms of data visualization? How is data visualization evolving as technology improves?
Kirill Eremenko: Yeah, wow. That's a very good overview of all of that. Are you familiar with the Gartner Magic Quadrant?
Kristen Sosulski: Yes, yes.
Kirill Eremenko: I've been observing it for the past couple years, and it's been very interesting to see how all these different players, IBM, Microsoft, Tableau, Click, and some others, how they are all participating and how like before it was just Tableau. That was the top company in the space. But now everybody's catching up and all of their features, all of their missions and ways they present, they allow users to create visualizations are becoming more and more, on one hand, sophisticated in what they can product, on the other hand easier in terms of actual usage. It's just been really cool to see how all of these companies have shifted into that very lucrative space of the Magic Quadrant. Yeah, it's just a very exciting space to be in.
Kirill Eremenko: Actually, on that note, I wanted to ask you, do you think that technology will ever take over visualization from humans? Will the skill at some point become obsolete and just machines will be visualizing things for us on like automatically?
Kristen Sosulski: That's a really, really interesting question. I mean, I think there are certainly tons of instances now where our data is visualized for us dynamically. Someone had to start and create those archetypes, right, for you to show your progress on your running app or how many calories that you lost, or on your dashboard displays for a stock price, et cetera. Those things are already happening in a dynamic way. I think that there's always going to be a need for inquiry and inquiry driven by humans. Any time we have a question and we're looking for data to answer that question, we might have to actually mine that data for insights and to see if we can find those answers. If we decide we want to visualize those answers, we'll probably still have, there'll still be some, I think, labor involved in that.
Kirill Eremenko: Mm-hmm (affirmative). Well, that's good news, isn't it?
Kristen Sosulski: I guess so. I think so. I think so.
Kirill Eremenko: Don't want machines to take everything. Okay, well that's fantastic. All right, well we're slowly, slowly coming to the end, or quickly approaching the end [inaudible 01:02:45] Can you imagine that it's already been close to an hour that we've been chatting?
Kristen Sosulski: Oh my God. This has been really fun.
Kirill Eremenko: Yeah, yeah, for sure. What I wanted to also ask you is, before I let you go, what is ... Oh, no. Even before we do that, I have an important, exciting announcement that we discussed. Just before the start of the podcast, I spoke to Kristen and, first of all, for all of those of you who are coming to DataScienceGO 2018, which is at the time when this is released. It's going to happen in the coming weekend. Kristen might be there and you might get to meet her in person. But the most exciting part is Kristen will be joining us as a speaker at DataScienceGO 2019, next year. Super excited about that. Kristen, how do you feel?
Kristen Sosulski: Oh my God, it's such an honor. Thank you so much, Kirill.
Kirill Eremenko: Oh, it's such an honor for us. I forgot. I should have introduced you as Professor Kristen at the start. You're a professor at NYU. It's going to be so exciting for us and our audience to have you there and for you to share all of your amazing insights with everybody, so very, very much looking forward to it. It's going to be fun.
Kristen Sosulski: Likewise, likewise.
Kirill Eremenko: Awesome. Okay, so on that note, what is the best way for our listeners to contact you? After listening to this podcast, maybe somebody might want to take one of your classes at NYU, or maybe engage you for consulting job, or maybe they just want to follow your career and see where it goes from here and what kind of amazing visualizations you're going to create in the future.
Kristen Sosulski: The easiest way is Twitter. It's just my last name @-S-O-S-U-L-S-K-I. @sosulski on Twitter is really the best way to contact me, but you could also feel free to email me at [email protected]
Kirill Eremenko: Okay, gotcha. Twitter, email. Is it okay for our listeners to connect on LinkedIn as well?
Kristen Sosulski: Absolutely. Absolutely.
Kirill Eremenko: Awesome. Awesome. Very cool. And, everybody, I'll remind you once again, the book. Don't forget to pick it up. It's called Data Visualization Made Simple: Insights Into Becoming Visual. On that note, thanks so much, Kristen, for being on the show today. Very, very exciting, and I can't wait to meet you in person, whether it's at this DataScienceGO or at the next one.
Kristen Sosulski: Same here. Thank you so much, Kirill. This was a blast.
Kirill Eremenko: There you have it. That was Professor Kristen Sosulski all the way from the New York Stern's School of Business. I hope you enjoyed this podcast as much as I did and got lots of valuable takeaways. For me personally, one of the most valuable ones was something I already use in my career, but it was very nice to hear it reiterated by a person who professionally teaches data visualization, and that is the fact that when you, you need to think of the formats differently when you present in person versus when you create an online interactive tool, and when you create a PDF, a downloadable PDF report. It might be the same findings, but because they're presented different mediums, you need to think of how you'll present them differently. I'm sure you had your own takeaways. Jam packed, this episode was jam-packed with lots of interesting knowledge and fun insights.
Kirill Eremenko: Make sure to check out Kristen's book. It's called Data Visualization Made Simple. And, as you heard from Kristen herself, she will be joining us for DataScienceGO 2019 as a speaker. If you enjoyed this podcast, you're definitely going to enjoy her speaking there. You'll be able to buy the DSGO 2019 tickets on presale very soon, so check them out next week. At the same time, Kristen might be joining us for DataScienceGO 2018, which is happening this weekend. I can't wait for this to happen. I'm actually, I'm recording this while I'm on my way to San Diego, so I'm already going to be there when you're listening to this. Can't wait to see you in person if you're coming to DataScienceGO 2018. If you haven't picked up your tickets yet, you can get them at www.datasciencego.com. Make sure to head on over there. Last chance to get your ticket and have fun with 400 other data scientists who are going to skyrocket their careers this weekend. Once again, tickets are at www.datasciencego.com, and I can't wait to personally meet you this weekend in San Diego. Until then, happy analyzing.