SDS 089: Excel & life-long learning in Data Science

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Welcome to the SuperDataScience podcast. My name is Kirill Eremenko, data science coach and lifestyle entrepreneur. And 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.

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Hello everybody and welcome back to the SuperDataScience podcast. Today we’ve got a very exciting guest, Chris Dutton on the show. So if you haven’t met or don’t know about Chris yet, then you are very likely to encounter him on Udemy, a platform where I also teach. Chris is the top instructor on Excel courses. And so what do we talk about in this show? So first of all, just to make everybody feel comfortable, we’re going to answer the question, why data scientists should still learn Excel. Because a lot of the time, you hear comments that Excel is not a data science tool, and data scientists should be using other tools, and so on, which are fair in some cases, but there are some valuable benefits of actually learning and knowing Excel, and we’ll dig into that. So if you’ve wondered the question, “do you need to know Excel, and to what extent,” this is going to be a great podcast for you.

Also, Chris runs his own business. He’s got a website, excelmaven.com, and he’s running both an education business, and a consulting business, so he’ll tell us all about that and how he transitioned in his career from working in an agency in Boston to doing his own thing, doing freelance work, doing consulting work. So that will also be very, very valuable. And plus we’ll dig into some of the things around how you can start into teaching if you like, how you can explore different avenues of careers, and we’ll get some of Chris’s thoughts on what is coming for data science.

So a very exciting podcast ahead, can’t wait for you to check it out. And without further ado, I bring to you Chris Dutton, CEO of ExcelMaven and top data science instructor on Udemy.

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Hello everybody and welcome to the SuperDataScience podcast. Today I’ve got a special guest, my buddy instructor from Udemy, Chris Dutton. Chris, welcome to the show. How’re you going today?

Chris: Thank you very much. Excited to be here.

Kirill: Awesome. And where are you calling from today?

Chris: I am calling from Boston, Massachusetts.

Kirill: Nice, nice. How’s the weather? It’s summer, right? So it must be good weather in Boston.

Chris: It is. It’s actually a beautiful day. It’s got 76 degrees. No complaints.

Kirill: Fantastic. I’ve got this thing with Boston, because when I was there in May, it was raining and cold and everything, so whenever I get a guest from Boston, I’m always like, “how’s the weather?” Because for me, it’s like London, pretty much.

Chris: It happens.

Kirill: Pretty lucky day today. Alright, so Chris, it’s fantastic that you’re on the show. You’re the best selling instructor on Udemy for courses relating to some of the top Microsoft products, such as Excel, PowerBI, and others. So tell us a bit about your background. How did you get into teaching on Udemy?

Chris: Sure. So I’m a marketing analytics consultant, and also the Founder of Excel Maven, which is all about providing online training and consulting with a focus on Excel and PowerBI. So going back to the college years, I studied quantitative economics and multimedia art, so kind of a strange balance. A lot of the left-brain-right-brain balance. After college, I was looking for a role in analytics, found a job in the strategy and analytics group at a large Boston-based advertising agency, which was kind of the perfect fit for me because we were doing really interesting data science and analytical work, but in the context of this really creative marketing field. So I got to work with a really wide range of clients across just about every vertical: automotive, healthcare, e-commerce, insurance. And my role in that job was to essentially help them develop measurement plans, track performance, better understand their customers, and ultimately optimise how they’re spending their marketing budgets.

So it was in that role that I really started to get exposure to Excel, and specifically some of the more advanced Excel tools and techniques. After a couple of years there, I pretty quickly became the Excel guy at the office, so three or four times a week I would have co-workers coming by with their Excel challenges, their toughest problems they were trying to solve. And, you know, some people would be annoyed by that, but I actually loved it because it was a chance for me to kind of continue upping my game and continue trying to solve progressively more challenging problems with Excel.

That eventually developed into producing and teaching the advanced Excel training materials for the incoming analysts’ classes at the agency and that was really my first taste of teaching analytics and I loved it. I don’t know what it was, I think it was just that excitement of seeing people’s eyes light up when they get a complex function to operate or build their first tool. It was just really inspiring and I found that I really loved teaching and had a lot of fun with it. So, fast forward about six years working with the agency, I decided to go out on my own, start my own consulting business, and at the same time offer this Excel training on a broader scale and that’s what brought me to Udemy and this whole e-learning world.

Kirill: I’m glad you mentioned your website, Excel Maven. It’s actually very well-made. Congratulations on that. I was looking at it today and it’s a very, very professional website. I’m very impressed. Did you code that yourself? Or did you get someone else to help you out?

Chris: No, that was one of the areas that I outsourced to a friend of mine who I’ve done some work with in the past who does some really, really high quality work, so I’ve been really happy with it as well.

Kirill: Yeah, it looks very good. I can really relate to your story, the whole being the Excel guy in the office. When I was at Deloitte, I wouldn’t say—like, I knew the basics of Excel, but at some point I set myself a goal to know it really well because in consulting, you use Excel for pretty much everything, especially at the start of the project. At some point, everybody—not in my department because I was in the data science department and everybody knows Excel very well there, but people from other departments were coming up to me and saying, “How do you do this? How do you do that?”

You know, I remember once when one lady, she was using the keyboard to move up and down through the cells, but it was actually moving the whole sheet and she was like, “How do you undo that?” And I looked everywhere and you just have to unclick scroll lock on the keyboard and it was so funny, that was like a 5-second thing but you needed to know that. I have an interesting question for you, for me it’s always been the metric of how well I know Excel. What percentage of your work in Excel can you do without touching your mouse?

Chris: That’s a great question. The keyboard shortcuts and the Alt key tips are huge timesavers, so I actually have a whole section dedicated to using shortcuts to work efficiently in Excel. As far as the percentage goes, if you get really good at it, I would say you can probably do 75%-80% of your work without touching a mouse.

Kirill: Yeah, and that for me was the main selling point when I personally started learning Excel. I remember I was in a project somewhere in the middle of nowhere in Mackay in Queensland, it’s the middle of the desert pretty much, and these consultants flew in from America and one of them was doing everything on his computer, on Excel just by using the keyboard. I was so impressed that I set myself a goal, “I’m going to be like that.” It took me like a year to master that, even though probably if I put in more effort I would have mastered it earlier.

But then one day I was flying on the plane and doing some work on my laptop and the person sitting next to me was like, “Oh, wow, you can use Excel without your mouse,” and at that point I was really happy about it. Yeah, so it’s really cool that you teach that in your course.

Chris: Yeah, for sure. It’s definitely a little tricky to get used to, but it’s worth the effort for sure.

Kirill: Yeah, definitely. All right, I’m keeping the listeners, or we are keeping the listeners here, in a bit of a suspense because this is a podcast – and I warned you about this question – this is a podcast for careers in data science and a lot of the time in data science literature, in data science courses, in data science conversations, you hear that Excel is not a great data science tool, like data scientists should not be using Excel.

You know, the whole point of that stems from the fact—like most of it, there’s lots of arguments for that, but I think the main one is that Excel combines data plus function in the same space. In SQL, for instance, you can’t put a formula inside a cell, you have data separate and you have the command separate. In Excel they combine and that can get confusing if you then try to do more advanced data science. So let’s break that myth. Let’s completely destroy it and let’s tell our listeners, give our listeners a reason why Excel is still important to know for data science.

Chris: Yeah, that’s a really good point and I’m happy to talk about it. I think you’re right, Excel does get kind of a bad rep, especially in the data science field. Just to preface, I would say that there are certain projects where Excel is absolutely not the right fit for the role, but others where it is—a lot of times what I see is this issue of people not knowing what they don’t know, it’s people who are very familiar with a very small fraction of Excel’s functionality and in their opinion that’s kind of the entire program so they don’t really expose themselves to some of the more interesting advanced capabilities of the program.

The other thing I’ll say is that a lot of people who share that sentiment about Excel have that perspective because they’re trying to use Excel for the wrong purposes. People who are trying to use Excel as a database tool or a data storage tool are going to run into problems because that’s really not what Excel is built for. That said, I have been exploring some of the newer business intelligence tools that Microsoft has been rolling out, things like Power Query and Power Pivot, and more recently Power BI, that are really starting to break down a lot of these walls that have kind of prevented people from using Excel for more serious data science projects or things that involve larger datasets.

I’ll give you an example. Just yesterday, I loaded up a raw dataset and connected to a table with 30 million rows, stored it in Power Query within Excel itself, it’s a brand new compression engine where you can store way more data than you ever could before, and then I used tools like Power Query and Power Pivot to basically create relationships between tables, build data models, and then develop completely custom visualizations on top of that model. That kind of stuff, it’s things that you really couldn’t do 3 or 4 years ago in Excel, but you really can now. So, it’s opening up a lot of doors and a whole new world of Excel capabilities.

Kirill: Okay. That’s really cool. So, Microsoft is bringing on all these capabilities and even new tools such as Power BI. I totally agree with that. How about this question: Why would somebody learn Excel, being a data scientist, beyond the basics if they can do all those same things in tools such as R and SQL and Python and so on? Are there any other reasons for people to take on Excel? You know, why would it, in some cases, might even be better or more advantageous to know Excel instead of those tools or maybe in addition to those other datasets?

Chris: I think you bring up a good point which is, ‘in addition to those tools.’ I would never say that Excel is a replacement for R or Python. I would say that those tools do a really nice job supplementing each other. I also recommend for newer data scientists or people who are first getting into this analytics world, Excel is a really great way to learn and master the fundamentals, so there’s something about seeing the data in front of you and seeing the output as you manipulate, transform and shape that data in a way that’s a little bit less tangible for programming languages and tools like R and Python, for instance. I think that’s one benefit of Excel.

The other thing that I found out as I’ve started to push the limits of Excel further and further is that the level of customization with Excel is actually pretty outstanding, especially once you start getting into the data visualization side of things. You know, I’ve used Tableau, I’ve used Domo, I’ve used Custom R Visuals, and honestly, I often go back to Excel strictly because I can personalize and customize my visuals exactly how I choose and can kind of hack together these interesting visualizations that quite honestly I wouldn’t be able to build elsewhere.

Kirill: Okay, very interesting. Actually, one of our other guests on the podcast, previous guest Nadieh Bremer, she said that she had a similar reason why in terms of visualization she moved in the opposite direction, to move to a more advanced tool, which is D3, because she wasn’t able to make the custom visuals that she wanted in Tableau, etc. That’s a great point.

And also I like what you mentioned, you know, ‘in addition and also starting out.’ I completely agree with that. For somebody who’s just starting out into the field of data science, the whole notion of what Chris just mentioned, of seeing what you’re doing with the data is invaluable. Like, it might not be a tool that can handle any data science problem in the world, but at the same time, Excel is really good for seeing what you’re doing with the data and therefore understanding how to speak the data language better on an intuitive level. That, for me personally, has been an invaluable journey that I went through to get through the whole space of data science to where I am now. That’s some very good insights. Thank you so much. Tell us about how many courses you have total on Excel.

Chris: Let’s see. I have three courses on Udemy. I launched the first one just under two years ago, and those are kind of the comprehensive full-scale courses. I’ve got one covering formulas and functions, second one covering data visualization, charts and graphs, and the third is data analysis with pivot tables. And I’ve got three others on Lynda.com and LinkedIn Learning. Those are more project-focused, shorter, more niche courses on that platform, so six total.

Kirill: Cool. Congratulations on getting on Lynda.com, I heard they have very stringent selection criteria for the instructors that they select. Do you know that LinkedIn Learning—they do courses, videos on whole flights between Europe and Australia? Like, I can watch these. Did you know about that?

Chris: No, I didn’t.

Kirill: Yeah, I’ll check next time on the flight. I’ll check if I can see your courses there.

Chris: Sounds good.

Kirill: That would be really cool. Okay, I want to ask you this question. Your lowest rating for any of your courses on Udemy is 4.7 out of 5. This is incredible, this is with like thousands of reviews. How do you manage to have such high student satisfaction? What is your secret sauce?

Chris: Oh, that’s a good question. Going back to when I first published course number one, obviously I did the research, I looked at supply and demand for Excel courses on Udemy, and even two years ago it was an extremely competitive category. You know, if you search Excel on a platform like Udemy, there’s something like 40 or 50 pages of courses. So, for me, trying to break into a category like that as an instructor with no current student base, no following to speak of, no e-mail list, I knew that I had a pretty steep mountain to climb ahead of me.

So, from day one, it was really just about producing the highest quality content that I possibly could, including content that you really can’t find in any another course, so things like custom datasets, really interesting unique examples. I like to use kind of fun and interesting datasets. In my pivot table course, for instance, there’s a whole section at the end with just different types of case studies to take what you’ve learned in the course and then apply it in all sorts of different contexts.

So I’ve got a dataset on San Diego burrito ratings, I’ve got a dataset on all shark attack records over the last 100 years, I’ve got salary data, Major League Baseball data, social data, all different things. I think that just makes it more interesting and it makes people want to learn and want to stay engaged.

The last thing I’ll note on that is I take a much more serious focus on student engagement and interaction and I think a lot of instructors do. I take pride in the level of one-on-one attention that I give to my students. I’m there answering every single message that people are sending me, I’m offering support and one-on-one guidance for anyone who posts to the course discussion board. And I think that just goes a long way over time and I think it helps students trust me and it helps me offer more value as an instructor than anyone else.

Kirill: Okay. And what are some of the most common questions that you get, like the most common question that you get from the students?

Chris: “Can I take this course on a Mac?”

Kirill: (Laughs) And what’s the answer?

Chris: Most of the content, yes, although there are some caveats because the user experience is frustratingly different across platforms in certain cases. With tables and charts and graphs specifically.

Kirill: Yeah, I can totally imagine that. We’ve had similar questions on our other courses. It depends on the platform, but you can still have Excel on Mac, so I think it’s a worthwhile thing to learn it.

Chris: Absolutely.

Kirill: You know, I’ve never taken your courses before, but this is what I’m gathering from what you’re saying and I’m actually looking at your course right now. Even if somebody has Excel skills, they will still get a lot of value from your courses not only because they’ll get the tips and hacks that they’ve missed out on and maybe some shortcuts and so on and some ways to do things that they haven’t thought of before, but also they will get these case studies, these great examples of how to apply Excel to the real world. Given that, where do you think somebody should start – out of your three courses that are available on Udemy – where would somebody get started?

Chris: I usually recommend the formulas and functions course first. That was the first one that I produced and I think it’s kind of a good starting point. And then there, depending on—if you’re more interested in the analytics side of things, I’d recommend the pivot table course next. If you’re more interested in data visualization, I’d say the charts and graphs course as a good follow-up. But honestly, you can take them in any order and within each course – to your point – I try to create content that’s appropriate for students of all skill levels. So I do try to cover the fundamentals and basics relatively quickly early on and then kind of progress into more and more sophisticated and complex examples and case studies.

Kirill: That’s really cool. I’m looking at your Excel formulas and functions course and I can see you have array formulas and that’s a really powerful tool. For me that was one of the latest things I’ve learned when I was at Deloitte and it really changed a lot in terms of working with clients. It was really helpful. Yeah, that’s really cool that you’re going through some very advanced topics there.

Okay, we’ve talked about some of your courses, that’s great, and some of your teaching methodologies. For somebody listening who wants to start into maybe teaching something online themselves, maybe not as advanced as you having a whole business in that space, but maybe just giving back to the world and contributing and explaining some sort of skill that they have or they’ve developed, in a certain tool or a methodology or something relating to data and analytics. What would you say is the best place to start? How do you take that first step?

Chris: I think, number one, you have to ask yourself two questions. Number one, am I truly an expert and do I feel like an expert in this topic? And number two, do I love to teach? If you don’t answer yes to both of those questions, it’s just going to be a struggle and an absolute grind. So that’s why I really only teach Excel and now Power BI courses because that really is my true area of expertise. You know, I have working knowledge in other tools and programming languages, but my opinion is that if you want to be the best teacher and instructor that you can be, you really need that deep, deep expertise in the topic that you’re teaching. So, if you do answer yes to both of those questions, the next step would be to evaluate the landscape and go on Udemy and type in some search terms related to that topic and see what the competitive landscape looks like, make sure there’s demand for that topic, and then just lay out a roadmap for building content and starting to produce your course.

Kirill: Really interesting. I can see your point, but I’ll have to disagree with you on that first one about the expert. I would say that if you have that working knowledge, you can still start to teach. I just want to encourage our students here as well that you can still start to teach, you can start a blog, you can start something basic, something like explaining things on maybe YouTube or in a blog or so on, and still give back to the community.

My personal opinion – again, different opinions, I totally understand – is that you don’t have to be an expert to teach something, but at the same time I can see where you’re coming from with this and you have that integrity that you have to give the absolute best. And to your point, you have 4.7 stars on all of your courses, so you definitely are the expert in all of those areas and you obviously love to teach so that stands as a testament to that.

But at the same time, to encourage our students—in my personal opinion, I’ve taught subjects where I’m not an expert on something, but I learn something for myself and while I’m learning it it’s just easier for me to learn it even better if I teach it to other people. So that’s also an approach. Would you agree with that kind of sentiment?

Chris: Oh, definitely. And I’ve had this conversation with a number of other instructors too, and I totally appreciate your perspective and viewpoint as well as one of the top overall Udemy instructors yourself. I’ll make a couple of points about that. For one, I think you bring up a great point about learning and teaching yourself as you’re teaching these courses. I became a much stronger Excel user and expert through the process of teaching so you’re right, I don’t want to discourage people from starting if they don’t feel like they’re at this ultimate level of expertise.

The other point that you mentioned was YouTube. I think that is such a great platform for people to test the water on a slightly smaller, more informal scale. And that’s a great place where you don’t need tons of technical equipment or recording gear, you don’t need a huge 6-hour curriculum for a full-scale course. You can just pick little topics or individual lectures and just throw them up on YouTube and see what kind of response you get. I think that’s a really awesome testbed for people who are interested in eventually teaching larger courses.

Kirill: Yeah, I totally agree. We’ve tested out a few things that way as well. For instance, somebody could just google Sankey diagram. You know, before creating a course about these things, we tested these things out and you can really see if there’s demand or not for certain topics. Thank you for that. That was an interesting discussion. Let’s move on to something else that you’re very passionate about, and that is consulting. Tell us a bit more about the consulting side of your business.

Chris: Sure. Basically, my area of expertise is marketing analytics consulting. Basically, there are thousands of companies out there that are sitting on huge amounts of data with no idea what do with it. My focus as a consultant is really just helping them collect, transform and visualize that data, and then, most importantly, actually translate it into something meaningful. You know, I started at the advertising agency in Boston, and after about six years, I kind of broke off and decided to do this on my own, similar types of work working with other companies individually, working with other ad agencies, but generally speaking, the majority of my projects involve helping companies blend together data across a number of different sources, help them define those relationships between their sources, and then building the tools and dashboards to help them explore and visualize that data and ultimately optimize the way that they’re spending their marketing budgets.

Kirill: Fantastic. And do you educate the teams as well?

Chris: I do. That’s another part of my consulting role, is obviously leveraging the training content that I’ve developed on Udemy and through the Excel Maven stuff, offering that as kind of a service as a consultant as well.

Kirill: Okay, I understand. Moving from your previous career—this is a question which some of our listeners will find interesting, who are contemplating whether or not to stay in the space of being employed or moving into freelancing and having their own business. If you’re one of those students, yes, I’m talking exactly about you. So how would you describe the difference between when you were working in the agency in Boston, and now that you have your own business? What are the pros and cons and why would somebody pick one over the other?

Chris: Yeah. It’s not the right fit for everyone, but for me it’s been pretty liberating. I haven’t looked back since. I’ve been doing my own independent consulting for about 3 years now, and the biggest pro I think is really just flexibility in terms of creating your own schedule, managing your own time as you see fit, and also the beauty of consulting on your own when you really have the reins is the ability and the option to get exposure to a really, really broad range of projects.

Generally, people who are working a traditional full-time job tend to be on a relatively narrow path or have a relatively narrow scope of work; whereas consulting kind of opens up that door and allows you to have a lot more flexibility to explore many, many different types of projects. That’s been far and away the number one pro for me.

As far as cons, you lose a little bit of stability, you lose a little bit of predictability of that 9 to 5 salaried role which some might see as a downside. You do have to sell yourself a bit more, you’re constantly looking for new opportunities, selling your services to new clients, and that requires a different skillset that not everyone has, which again is why I say that this path is not for everyone. But once you establish yourself, and once you get some clients, and really start getting exposure to some of that work, and start adding value as a consultant, it really is a wonderful thing – at least in my experience.

Kirill: Okay. And tell us a bit more—I can feel, I can sense that a lot of people who are thinking about this, right now they have this question, “How do you get the client?” How do you go out there and find the clients who are going to pay you for your work, for your consulting engagement?

Chris: For me, I was fortunate because I made a lot of connections during my six years working in client services, both on the client side and among colleagues who I’ve worked with here in Boston at the agency. So, a lot of those existing relationship helped turn into some of my initial contracts and projects. That said, that’s not the case for everyone.

The other thing that’s been really helpful for me has been kind of getting my face and my work out there publicly. Honestly, that’s a big reason why I decided to become an instructor. You know, it validates my skillset, it provides unbiased, objective proof of my expertise, you know, just looking at the student counts and the student reviews and the content that I have published out there. That has actually created a number of new relationships that have then turned into consulting projects as well. So, the online learning and the teaching has really been a nice—almost like a lead gen source for the consulting work, and vice versa.

Kirill: Yeah. And to your point, more than anything, it’s a testament to your skills. If you can teach something, it’s obvious you can perform it and run a consulting project in that space. It’s a no-brainer to hire you at that point.

Chris: Yeah, exactly. It’s one thing to put a tool or a language on your resume, and it’s another to prove that you can actually teach it.

Kirill: Exactly. It’s a great inspiration for those listening. Maybe some are thinking of going into consulting, and going on your own. And if you already have a solid plan on how you’re going to keep that cash flow coming in, then go for it when you feel confident. And if you don’t, then this is a good solution on how to build that plan. You know, start building an online presence, whether it’s through Udemy or through a blog, or through YouTube, or something, so you have something to stand for you, so that it’s not you going around saying, “Hey, do you want me to do some consulting work?” but people are coming to you because you are the expert or one of the experts or one of the influencers or teachers in the space.

Yeah, that’s a great place to start. Thanks a lot for the quick rundown on your consulting business. What are your plans going forward for your business? How do you plan on expanding and growing it, if you can share that with us?

Chris: Yeah. So, you know, I really would just consider my journey as an online instructor just starting. Like I said, I just got into this space less than two years ago, so for one, I’m really interested to try to build more content, really see how far that path can take me. To be honest, instructors like you and Phil Ebiner and Mark Price, these are guys who’ve built these pretty impressive followings and have really proven the potential that there is in that space.

Kirill: Thanks.

Chris: That’s exciting, you know, the exposure that you can get as a top instructor on a platform like Udemy is phenomenal. The other benefit I think is that it really keeps you honest by forcing you to constantly keep learning to stay relevant. So I’m excited to kind of continue pushing forward in that path and producing new courses and potentially partnering up with some other instructors to see how far I can take that route.

And, you know, also the business model that I’ve created with Excel Maven, of which the online self-paced course is one component of it – the other components being on-site group training and project consulting – that business model is really starting to prove itself. So, my other focus is eventually trying to scale things up on the Excel Maven side of things, and hopefully broaden the focus, find some partners, and potentially expand to a broader range of analytics resources.

Kirill: That’s some really solid plans. And with the expansion, just out of curiosity, are you starting to hire people, are you starting to build a team, or are you planning to do this on your own for some time?

Chris: I think I’m at the point now where I will be looking for some partnerships. You know, going back to the whole concept of—for me, I’m really only comfortable teaching what I feel I’m an expert in, so rather than me trying to become the teacher for all other courses, I think I would try to identify the experts and build some partnerships and start to grow.

Kirill: Oh, yeah. Yeah, totally, but I mean more the administration side of your business, because then you don’t have to take care of the courses, the website and everything like that. So are you planning on getting some admin staff on board?

Chris: Yeah, that’s the plan. I’ve been doing a pretty bad job about outsourcing some of those roles, to be totally honest. I’ve really tried to wear too many hats up to this point. So, yeah, I’m definitely starting to look for people to help support other aspects of the business to help things grow.

Kirill: Yeah, I’ve been there, I’ve done that. I got to the point where it was just too much. I was answering close to maybe 30 questions per week on Udemy – which doesn’t sound like a lot, but in addition to all these other things, it was just driving me crazy so, yeah, at some point I had the same realization and—once you start adding people to your business who are helping you and who are assisting you in your goals and mission, you get to focus on the things you actually love, and it’s a great feeling.

Chris: Yeah, definitely.

Kirill: Okay, we’ve talked about learning and teaching at the same time, you have these plans for growing your business. What are your plans for learning new stuff? What are you excited about learning yourself in the coming months or maybe a year or so?

Chris: I’m really, really excited with some of the stuff that Microsoft is coming out with in their BI stack. I referenced some of those newer tools earlier: Power Query, Power Pivot and Power BI. Really just in the past year or so, I’ve been integrating those tools more and more into the work that I’m doing for my clients, and I’ve been incredibly impressed by the capabilities of those tools. I’ll be teaching a Power Pivot/Power Query course next, followed by a Power BI course. I’m looking for opportunities to practice and learn those tools every chance I get. So, it’s really exciting stuff that Microsoft is doing in the BI and the data science world.

Kirill: Fantastic. That sounds really amazing. I can attest to that. I’ve worked with Power BI and I also have a course with Power BI and I have seen how Power BI has grown. Like, in the Gartner Report, it was somewhere in the middle of the quadrant a year ago, or a year and a half ago, and then last time they released it in February it’s now at the top, near Tableau. They’re releasing updates literally every month, major updates as well, so they’re really focusing on this analytics space, and indeed it’s very exciting to see what they’re coming up with.

Chris: Yeah, absolutely.

Kirill: Okay, thanks a lot for sharing that. Let’s do some rapid fire questions about your career. Are you ready for this?

Chris: Sure.

Kirill: Okay. What’s been the biggest challenge you’ve ever faced in your career?

Chris: Oh, man. I’m going to give you a kind of general answer to this, but in general, the biggest challenge that I’ve had personally is just trying to keep up with everything. Data science is one of those fields where it’s really easy to feel inadequate. You know, you ask yourself, “Am I a slacker if I don’t know both Python and R? Am I falling behind if I haven’t learned TensorFlow yet?” You’ve got people throwing around these acronyms and these tools left and right, you’ve got new things showing up what feels like every single day, so honestly, I think one of the biggest challenges of working in this field is, a) trying to keep up with what’s relevant, and b) reminding yourself that it’s okay to ignore some of the stuff that isn’t, which can be easier said than done, but at the end of the day, no one has the capacity to learn all of it. So it really comes down to picking your battles, which has certainly been a challenge for me.

Kirill: I totally agree. I think that choice has to be guided by everybody’s—their own passion. Yes, there are lots of tools, but don’t just get carried away running after the latest, greatest, newest, biggest thing if your passion lies somewhere else. There is always going to be space in this field of data science. There is always going to be space for you to realize your passion if you’ve really focused on it.

Chris: Right. And you don’t want to end up learning ten tools at a very shallow level, as opposed to one or two really well.

Kirill: Interesting. Yeah. Okay, thank you. Next one is, what is a recent win that you can share with us that you’ve had in your role, something that you’re proud of?

Chris: One project in particular has stuck with me. And when you say recent, this one was a few years ago, so not the most recent, but—

Kirill: That’s totally cool.

Chris: It was probably my favourite project that I’ve worked on, which was actually back in college. I’m a big baseball fan as a player and a fan of the sport and the Red Sox, and also a fan of the data and the statistics behind the game. So, back in college, I actually started a group called “Baseball Analysis at Tufts.” I went to university outside the city, and our goal as a group was basically to come up with really interesting questions and hypotheses about the game of baseball and then try to answer them with econometrics and statistical models and data analysis.

And one of the projects that really took off for us was a project to try to quantify luck, so how lucky was a given hitter in a specific season. And the way we did that was we essentially tried to quantify and identify every single factor that contributes to a hitter’s batting average, so things like their power, their speed score, how well they can spread the ball across all fields.

So we took all of these individual elements, these independent variables, and we fed them into this regression model that essentially would spit out an expected batting average. And what we were able to do then is look at actual hitter’s performance, compare it against their model’s output and call the delta, something called the ‘luck factor’.

So, that was really interesting and the best part was we were able to take a given season’s worth of data and identify the list of players who outperformed the model by the widest margin, those were the ‘lucky’ ones, and the players who underperformed their model by the widest margin, those were the ‘unlucky’ ones, and then track how their performance changed year over year. 

And what we found was that it was actually remarkably predictive of which hitters would improve the next year and which hitters would regress. It was really awesome to see and it ended up getting a good amount of coverage in some baseball blogs and websites, and there was a feature in the ‘New York Times’ about it, which was very exciting.

That was a really meaningful project to me: a) because it was just a lot of fun, and b) it really made me love analytics and really appreciate its ability to expose these patterns and trends and stories in the data that you otherwise never would have seen. It almost feels like becoming fluent in a new language, except the language is data. So that was a really meaningful project, and really I would say one of the biggest influences in guiding my career into the analytics and data science space.

Kirill: That’s so cool, such a cool story. I’m burning to find out—so, players who had higher luck, in the next season they dropped down, and who had lower luck, they usually went up. Is that correct?

Chris: Yeah. And as a follow-up to the project, we actually partnered with a really popular baseball researcher who compared our model against seven or eight other predictors, other predictive tools, and at the end of the day ours was the winner by a pretty wide margin, which was really satisfying to see.

Kirill: So you might even say your model was lucky?

Chris: Oh, yeah. There you go. (Laughs)

Kirill: So how old were you then?

Chris: I was 21 at that time.

Kirill: 21? That’s really impressive. It’s a great example. I’ve also had stories like that in my life where I was passionate about physics and I would go do a physics project on my own and build this magnetic thing that I thought was the first one in the world or do something in programming, create this programming algorithm with 10,000 lines of code just in my free time. I actually totally agree with you that these are the projects that—I wouldn’t find a better way to put it—that shape your career. These are the projects that shape your future. It doesn’t matter what you really do at work. That’s all great and that’s what you’re told to do, but when you’re really passionate about something, and you go and you spend your free time, your blood, sweat and tears on that, because time ultimately is the most valuable resource we have; if you’re spending your time on something, it means you have to love that thing so much.

And when you spend a lot of your free time on something and you really, really work on it and you get that final result which you’re working towards, that, my friends, really shapes where you’re going to go in life. So if you haven’t done one of those projects—I’m sure everybody has at some point in their life—but if you haven’t done one recently, I would highly encourage you to go and do that and find some time to invest into something that you’re passionate about.

Pick some problem, pick some challenge and solve it. No matter how long it takes, no matter how complex it is, you will be super satisfied at the end and it will reveal to you more what your passion is all about and how you can dig deeper into it. Thank you so much, Chris, for sharing that. That’s a great testament to it.

Chris: Of course, couldn’t agree more.

Kirill: Okay, so next one is, what is your one most favourite thing about working in the space of data? What excites you the most?

Chris: Data visualization. Yeah, I love data viz. It’s always been my favourite part of the job. I think there’s just something really powerful about turning a mountain of raw and unstructured data into something beautiful. And, more importantly, into something that has meaning and insight and can actually guide decisions. I think data viz is an underappreciated skill, to be honest, and I think it’s one that tends to be surprisingly uncommon among data scientists. But I love data viz, I love getting creative with it. I love constantly looking for new and interesting ways to present my data.

Kirill: Fantastic. Thank you, I totally agree with that. It’s a very, very exciting and powerful skill to have in your arsenal. And slowly wrapping up the show, a very philosophical question which I’d like to get your opinion on: Where do you think the field of data science is going, and what should our listeners prepare for so that they’re ready for the future?

Chris: Yeah, it’s a great question. For one, I think we’re going to start to see data play a much more critical role in industries and in scenarios that we haven’t considered to be very data-driven to this point. We have things like sensor generated data, IoT, wearable technology, and all of those things are creating data in places that it hasn’t existed in the past.

That’s really exciting to me and I think that’s going to lead to some really fascinating developments. Thinking about the field of medicine, for instance, being able to predict health issues before they’re even diagnosed just based on patterns of behaviour. Or manufacturing and using data to replace components before they actually break. Or personal health and fitness, getting real-time feedback through things like biometric monitoring.

These are the types of possibilities that are already becoming reality today and it’s only going to continue down that path in the future. So that’s number one that I think is really exciting, just to see data play a role in places where it really hasn’t in the past. And second, I think we’re going to see a lot more accessibility to advanced tools and techniques, things that up until this point required years of training and even a PhD to deploy. And we’re certainly seeing this already with the rise of self-service BI tools and with open-source libraries, but it’s now kind of getting to the point where, in some cases, some guy off the street could build a pretty decent predictive model using some free software and a few clicks.

Now, whether that is a good thing or not, I think that’s a different question altogether. Personally it’s a little bit frightening to me, but I’m trying to be conservatively optimistic about it. But that certainly feels like the path that things have been going. It’s just this concept of self-service BI and this accessibility to very advanced tools and techniques.

Kirill: All right. That’s such a cool description. And how do we prepare for that? How do the listeners of the podcast prepare for that future so that they have careers that are aligned with this future?

Chris: So, as far as preparing for a future in data science, there are two things that I would recommend. Early on, I think it’s really important to get exposure to as many different types of diverse projects as possible. You know, having a role in a consulting firm like Deloitte, for instance, or with an ad agency, anything that’s client service-focused where you get exposure to different types of projects, or even just exposing yourselves to different types of Kaggle competitions or exploring personal projects like the ones you and I talked about, just try to get exposure to a really broad range of projects early on. I think that’s really important.

But eventually, what I personally would recommend is starting to focus really on becoming an absolute badass in one or two particular areas. There’s something called a T-shape skillset which I personally believe in, which is basically having solid working knowledge of a pretty broad range of skills. So if you think about listing those skills horizontally and then really having one or two where your level of expertise goes really deep, that’s like the vertical line of the T.

So, I believe in developing a T-shaped skillset, and in my personal experience, I found that the strongest teams tend to consist of complementary T-shaped people, each with the ability to speak intelligently about a very wide range of topics, but who have one or two or even three specific world class skills. That’s recommendation number one as far as preparing yourself: Get a lot of exposure early on and then think about starting to focus on what you really feel passionate about.

And then number two is just learning how to constantly adapt. We’ve talked so much about how fast this field is changing, be it the tools, the techniques, the best practices. So at the end of the day, those who evolve along with it are the ones who are going to thrive. And there’s really no excuse these days, given the accessibility of educational content out there today. You and I know Udemy is a great example of that, Coursera, edX, Lynda.com, the list goes on. So, I think learning how to adapt and evolve and learning how to learn is a really important skill for someone who wants to get into a field that’s changing as fast as data science.

Kirill: Very cool, Chris. That is really very cool. You made me think about this now and personally I think that learning how to learn has been a killer skill in my arsenal. Without that, I really wouldn’t be able to be where I am right now. And speaking of your T-shaped approach, personally I think my vertical one, the one that I’m really deep into, is probably the communication side of things. So it’s not a technical skill, it’s more the people side of things that I—like, when I need to, I can communicate the complex insights and so on.

Yeah, that’s a very, very good overview. Thank you so much. I hope that will make other people listening to this podcast also think about their approach right now. Thank you so much for coming on the show. How can our listeners contact you, follow you, find you if they’d like to learn more about how your career develops from here?

Chris: Sure. You can find me at ExcelMaven.com, contact me through the website. I’m also on LinkedIn, happy to connect with anyone who wants to get in touch. And if you’re interested in the coursework, or the training side of things, you can find me on Udemy or on Lynda.com.

Kirill: Perfect. Fantastic. We’ll definitely include all of those links in the show notes. And one final question for you today: Do you have a book that you can recommend to our listeners to help them become better data scientists?

Chris: Instead of a book, I am going to give you two blogs which I actually have become a huge fan of and I think everyone should become familiar with. Blog number one is InformationisBeautiful.net. It’s a collection of some of the most unique and powerful data visualizations that I’ve ever seen. So if you’re into data viz and you’re into charts and graphs, and really unique ways to present data, check out InformationisBeautiful.net. That’s a great one.

And then the second recommendation that I have is FiveThirtyEight.com, which is Nate Silver’s blog. It’s really just about taking an extremely analytical approach to popular stories and politics and economics and sports. It’s a really, really entertaining read. Those are my two recommendations.

Kirill: Yeah, fantastic. Thank you so much. InformationisBeautiful.net and Nate Silver’s blog, FiveThirtyEight.com. Once again, Chris, thank you so much for coming on the show and sharing all your insights about business, education, consulting, Excel and so much more.

Chris: Thank you very much, Happy to be here.

Kirill: So there we go. That was Chris Dutton, a top instructor on Udemy and also the founder and CEO of ExcelMaven.com. I hope you enjoyed this episode. Personally, for me, probably the biggest takeaway was this whole situation which we talked about at the very end about the different things that you need to focus on going forward and one of them was the ability to learn all the time, which I personally love doing and I know love doing because you’re listening to this podcast. And, of course, that T-shaped skill personality – I think that’s what it’s called – that was very, very valuable as well and it made me think about my skills from a different perspective and in a way I haven’t thought of it before.

Hopefully some of these elements on this podcast made you think as well and maybe now you’re a bit more excited about learning Excel if you weren’t previously. For me, personally, once again, Excel has been kind of like the foundation on which I built my future data science career, so it was a necessary step, and I’m really glad I did learn Excel to the extent that I did. So, thank you very much to Chris for sharing his insights today. And of course, you can get all of the show notes at www.www.superdatascience.com/89. Make sure to follow Chris on LinkedIn and check out his website, ExcelMaven.com, it’s very well-made. And of course, you can find him on Udemy as well. And on that note, I look forward to seeing you next time. Until then, happy analyzing.