SDS PODCAST

EPISODE 89

WITH

CHRIS DUTTON

Kirill: This is episode number 35 89 with Best Selling Data Science

Instructor Chris Dutton.

(background music plays)

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.

(background music plays)

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.

(background music plays)

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: 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.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.