SDS PODCAST

EPISODE 383:

YOU’RE NOT AN

IMPOSTER, YOU’RE

LEARNING: DATA

SCIENCE JOURNEYS

Kirill Eremenko: 00:00 This is episode number 383 with Aspiring Data Scientist

Sean Casey.

Kirill Eremenko: 00:12 Welcome to the SuperDataScience podcast. My name is

Kirill Eremenko, a Data Science Coach and Lifestyle

Entrepreneur. And each week, we bring inspiring people

and ideas to help you build your successful career in data

science. Thanks for being here today, and now, let's make

the complex simple.

Kirill Eremenko: 00:44 Welcome back to the SuperDataScience podcast

everybody. Super excited to have you back here on the

show. Today we've got a very special guest, Sean Casey

calling us from Abu Dhabi, United Arab Emirates. Very

interesting episode, it's going to be extremely useful for

those of you who are specifically starting out, starting on

your journey in data science, just dipping the toes into

the water. Sean shares his story of how he got into data

science, how he got into this field several years ago and

what a crazy rollercoaster it has taken him on. Or what a

crazy rollercoaster his life has taken him on that has led

him to be where he is now.

Kirill Eremenko: 01:30 He's doing data science in the space of visualization for a

large company in the United Arab Emirates. In today's

episode, we'll talk about quite a few things. We'll talk

about DataScienceGO Virtual, so if you were there, you'll

be able to relate to Sean's story very well and you'll be

able to cheer along as we're discussing the things that

happened, the people he met. We'll talk about creativity in

data science, the necessity or not necessity of a formal

qualification in data science. You'll hear Sean's story.

We'll talk about visualization, an amazing book that you

can read in the space of data visualization, why it's

important. We'll talk about the data science community

and Sean's tip for asking for help and why that's

important.

Kirill Eremenko: 02:13 In a nutshell, this is going to be a great episode. If you

need that boost of motivation, that inspiration to keep

going forward and to become the best data scientist you

can possibly be. So, without further ado, let's get started,

and I bring to you aspiring data scientist, Sean Casey.

Kirill Eremenko: 02:38 Welcome to SuperDataScience podcast, super excited to

have you back here on the show everybody. Today we've

got a super exciting guest joining us from Abu Dhabi,

Sean Casey. Sean, welcome. How you going man?

Sean Casey: 02:48 Good Kirill, how you doing? Morning.

Kirill Eremenko: 02:49 Very good, very good.

Kirill Eremenko: 02:54 What's the time for you? For me it's 7:30, how about you?

Sean Casey: 02:56 Yeah, 10:30, so it's getting to the hottest part of the day

at the moment, but it's Thursday so it's the end of the

working week here today.

Kirill Eremenko: 03:07 Awesome. Is it hot in Abu Dhabi?

Sean Casey: 03:10 Yeah. Yeah, it's up to 45 later today. 45 Celsius, so I

think that's-

Kirill Eremenko: 03:15 45 Celsius? That's [inaudible 00:03:17]. What is that in

Fahrenheit?

Sean Casey: 03:19 I think it's 115 or something. 113.

Kirill Eremenko: 03:22 115 degrees? 45 degrees, that's crazy. How do you cope

with that? That's like, I can't even imagine going outside

in that temperature.

Sean Casey: 03:30 Yeah, you stay inside for as much as you can. Everywhere

has ACs so you just try and avoid the heat as much as

you can. It's hot, for sure.

Kirill Eremenko: 03:46 So do you run to your car, how do you get ... like, how?

Sean Casey: 03:50 You get to your car but you have to drive with your

fingertips because the steering wheel's so hot when you

get in there first.

Kirill Eremenko: 03:55 No way.

Sean Casey: 03:55 You have to wait for it to cool down. Get the AC checked

every six months to make sure it's okay. But it'll start

cooling down again the end of September, middle of

October.

Kirill Eremenko: 04:11 Do they have emergency services in case your AC breaks

and they bring you a portable one?

Sean Casey: 04:17 They don't. It's a good shout though. There's a business

opportunity there.

Kirill Eremenko: 04:22 Okay, awesome. Well, Sean, really excited to have you on

the podcast. Tell us quickly how we met. It was like the

most random thing.

Sean Casey: 04:32 So yeah, it was three weeks ago at DataScienceGO

Virtual. I think it was the second day. I had spent the

previous night at the keynotes, at the presentations, in

the expo center and then moved to the networking center.

You get paired with somebody for three minutes and I met

people from all corners of the world, all corners of the ...

or all ends of the data science journey, some really cool

people. The second evening then, the first person I meet

in the networking center is you and it's 1 AM for me, I'm

standing on the balcony. It just blew my mind. We had

maybe a 20 second chat and then, I don't know, I was on

my mobile because our daughter was asleep inside and if

I was chatting to people in the networking center on the

balcony, she wouldn't have had the best of nights sleeps,

which wouldn't help anyone.

Kirill Eremenko: 05:31 Yeah, I think it was so random. We got connected. I think

you were also probably the first person I connected with

on that day. I'm not sure exactly. But I remember we

connected and then I was going to connect, click the ...

you get the button, connect, so we could stay in touch,

and then I just wanted to make sure that it went through

and I clicked the other tab and I think that's why the

connection broke. Like, that's it. I clicked the wrong

button. But luckily, once you click the connect button,

you get each other's details so you can stay and touch.

Sean Casey: 06:04 Yeah. That's happened, there's been a couple of people

that I've been in touch with since. People who are at a

similar point in the journey to myself, people who are

brand new to it. And just a couple of messages in

LinkedIn, a bit of support when people share posts and

it's ...

Kirill Eremenko: 06:22 That's awesome.

Sean Casey: 06:23 Yeah, it's been cool. And also, the presenters, a couple of

the presenters, the guys in Zeal, I spent the whole time in

that area just having a one-on-one chat with them

around data culture. Plus the access we had was

incredible. Jason, Jason Koo had a really interesting talk

on computer vision and I dropped in a question at the

end of the chat, or at the end of the presentation, and

Roberto put it up to him. And again connected with him

on LinkedIn later on afterwards. And he was able to share

the paper with me that he spoke about in response to my

question around bias in computer vision models, and how

physics is being introduced to machine learning models

to help them understand that this might not be the most

accurate picture, or the most accurate decision.

Kirill Eremenko: 07:29 Fantastic, yeah. That's really cool. That's really cool you

could stay in touch. So, people have heard from me about

this event, we were promoting it, it was a free event,

DataScienceGO Virtual, and moreover, there was 2,500

people, so a lot of people listening to this would have been

at the event and they can relate to. But for those who

didn't make it to the event, just in a few sentences, could

you describe why did you sign up and what your

experience was, just to encourage others maybe next time

to check out DataScienceGO Virtual.

Sean Casey: 08:04 I signed up because I've been listening to the

DataScienceGO real events. I've been listening to the

presentations from those shows for the last three years

and always wanted, God, I'd love to get over to San Diego

or I'd love to get to LA to one of these events some time

and this allowed me to be there. To be at the virtual

events, so that was why I signed up.

Sean Casey: 08:31 And what I took away from it was just the encouragement

and the opportunities for learning that are out there.

Emily Robinson's talk on the first evening just stood out

for me. It was just that motivation, that encouragement

that yeah, it's a journey, you're on a journey, you can be

at different point on this. You don't need to worry about

the label or getting the label immediately, as long as

you're enjoying it in you're on that journey it's worth

sticking with it, that's for sure.

Kirill Eremenko: 09:09 Amazing. And did you do any of the workshops?

Sean Casey: 09:11 It was 1 AM. I had work the next morning so I didn't hang

around for the workshops. I've been meaning to look back

at them but-

Kirill Eremenko: 09:20 No, totally understand. That's huge that you made it to 1

AM. That's kind of like the only challenge, is the

timezones. We had people from 123 countries and making

sure every timezone is satisfied is really hard. But apart

from that, if you've got the commitment, that's totally

cool.

Sean Casey: 09:38 Yeah, and it's all available online anyway to look back at

and to read up. Jon Krohn just made his Google Colab

book available for everyone to take. I just couldn't believe

that, it was ... It was that learning, for you to be able to

access it that easily was just phenomenal.

Kirill Eremenko: 10:00 Yeah, awesome, awesome. Fantastic. Speaking of

journeys, tell us a bit about your journey, because I

asked you to describe it to me and you sent me this huge

email which I had so much fun reading. Tell us a bit

about your journey.

Sean Casey: 10:17 Yeah, so my journey into data science, data analytics. I

started off with a mathematics and computer science

bachelor's back in Ireland. So, I would have had a

foundation in object oriented programing and just the

logic and the good solid foundation in the mathematics.

And I very randomly ended up moving to Abu Dhabi to

teach mathematics and computer science. A random

decision but one I was very fortunate to be able to make.

Sean Casey: 10:55 I arrived here in 2005, spent some time teaching, some

time in school improvements and professional

development. Did a Masters in Education at one stage.

And I was kind of at a point where I wasn't getting a

whole lot of personal satisfaction out of what I was doing

at work. It was great to see schools improving, it was

great to see students access better learning experiences,

but my own personal satisfaction of enjoyment, I guess,

in my job was waning a little bit.

Sean Casey: 11:34 So, I looked into different areas of what I might go down

next after I finished the MA. I looked into accountancy for

a while, wasn't for me. I looked at doing an MBA, again,

wasn't for me. I ended up going back to Java. It had been

10 years since I'd looked at Java, professionally anyway,

eight years. So, I went back to Java, did a refresher

course in Java and I got chatting to a good friend of mine,

Gráinne Dollan, who lives in Dubai, works for IBM, she

said, "Have you looked into data science yet?" Because we

would have done a similar course in university, back in

Ireland. She said, "Check out data science."

Sean Casey: 12:23 I can't remember, did she send me to one of your courses

first or did she send me to the Microsoft Professional

Academy? But there's so much happened so quickly once

I dipped my toe into it. I started just banging out courses

for fun. I was driving to Dubai a lot at the time visiting

schools. I'd have one of the Udemy courses or the edX

courses playing on the phone hooked up to the speakers

in the car. I wasn't watching the, obviously wasn't

watching the videos, but I was just letting it soak in while

I was driving. Just the buzz I got off it, being able to

spend 10 minutes watching a video or listening to a video

when I was in the car and going home and being able to

code that out in a bit of a race against a video playing in

the background. Just learning skills, techniques for a 10

minute investment.

Sean Casey: 13:24 With the Master in Education, I could have spent three

hours reading a research paper and feel that at the end of

it I was no better off than I was when I started. I get that

it's a different type of learning and you have to be able to

arrive at your own balanced argument. To get to that

argument, you need source of information. But for me,

the return on the time I invested watching a course on

Udemy or troubleshooting a problem on Stack Overflow.

Just the return was incredible. And yeah, just really,

really enjoyed the journey into data science.

Sean Casey: 14:08 I'm not trying to suggest that I'm anywhere near the end

of the journey, but it's a journey and I'm very much

enjoying it.

Kirill Eremenko: 14:16 Why did you enjoy it? What do you enjoy the most?

Sean Casey: 14:19 I don't know what it was like when you were in school,

but in Ireland, in your mathematics classroom in

secondary school, the answers were at the back of the

book. So, your teacher would give you homework

questions one to 10 and it could be on anything, but you

knew opening the back page, when you did all your work,

you knew opening the back page that the answer you had

in your copy book was the same as answer in the back of

the book. You just knew it. [inaudible 00:14:53] that

sense of achievement that yes, you've done it right, or

accomplishment, you've done it right and you flip to the

back of the book and the answer's there, as you expected.

Sean Casey: 15:03 I get the same sort of a feeling from analytics. You can

spend 20 minutes cleaning a dataset or prepping a

dataset or trying to work out a formula in Python or in

DAX and you eventually get there, you get it to do what

you wanted it to do and it's just that accomplishment.

That sense of, right, you've learned how to do something

new and this is your validation of that learning.

Kirill Eremenko: 15:36 Mm-hmm (affirmative). Okay. Because I was thinking you

were going to say the opposite. I thought you were going

to say that in mathematics in school, you get the answer

but in data science, it's an open ended question. You

don't know the answer until you find it and different

techniques might lead to different answers. How do you

know that it's the correct answer?

Sean Casey: 15:56 So sorry, when I'm talking about that sense of

accomplishment, the data science work I do in terms of

predictive stuff is minimal so far in my journey. It's a lot

of reporting is what I've been doing until now. I haven't

done a whole lot of modeling.

Kirill Eremenko: 16:18 Okay, so BI reporting.

Sean Casey: 16:19 Yeah, yeah, yeah, BI reporting.

Kirill Eremenko: 16:21 But still, even there, how do you know that you've got the

correct answer? Because you could structure a

dashboard in many different ways.

Sean Casey: 16:28 Yeah, you can, indeed. I suppose that it's accessible to

the people that are going to be using it. That it adds value

to the users of the dashboard. So, if we're creating one on

academic results, we'll try our best to sit with the people

who are going to be using it to find out what they need.

So, what do you need to dashboard to tell you, so then, if

you're looking to do a calculation in DAX, that there's a

rolling average of students and it displays the way you

want it to display or the way that your end user wants to

be able to extract the information from. Then it's, yeah,

then it's the right answer in my head. It might not be, but

it's the right answer in terms of what the users wants.

Kirill Eremenko: 17:17 Okay, okay, gotcha. I guess it's that satisfaction of

delivering usefulness to the end user. But in addition, I

find how it's different to school, high school, uni math is

that there is so rigorous. It's so like, okay, very

structured. There's usually just one way or one optimal

way to get to the right solution and you follow those

steps. It's just basically like mathematics. Like it's a

science. Whereas here, there's an element of creativity.

You can get a right answer but in several different ways

and I think the satisfaction is even greater because you

came up with your own way to get to that answer.

Sean Casey: 18:02 For sure. And, going back to your first part about it, it's

adding value is, if it's making someone's life a little bit

easier by being able to access a dashboard to get the

information they need as opposed to having to trawl

through the analytics themselves to get there, it'll

hopefully make their roles a little bit easier.

Kirill Eremenko: 18:26 Okay. Yeah, absolutely. Helping other people make their

roles a bit easier.

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Kirill Eremenko: 19:09 So, tell us a bit about the rest of your journey or up to

now. So, you said you started learning analytics through

sitting in the car, listening, going back, revising. What

else did you do? How else did you invest into your

learning curve?

Sean Casey: 19:30 Yeah. I got to a point where I had developed a load of

additional skills. I did your Python and R, your machine

learning courses. I did a load of stuff in Data Camp and

edX on dash boarding, on Tableau, on Power BI and so

on. And I had got to a point where I thought, right, you've

all these skills that are developing but you've acquired all

these skills. It's time to get some bit of a formal

recognition of that learning if you're going to take a step

into an analytics role.

Sean Casey: 20:12 And I enrolled in a Masters in Data Science and

Technology and I've kind of put it on the back burner for

the moment, for a few different reasons which I'll get onto

in a second. But I approached the modules in the

Masters, if the module was coming up on machine

learning or on visualizations or on Java, I'd enroll in a

MOOC, on an online course in Udemy or somewhere else,

on Coursera to get the foundations and the skill that was

coming up in the module for a fraction of the price. And

just was able to approach the modules then with a solid

foundation. And thankfully have been doing really well in

them.

Kirill Eremenko: 21:07 So did the online course just before the module came up

and so you came prepared to the module in the real world

course?

Sean Casey: 21:17 Exactly. And as a consequence, probably didn't learn as

much as I could have from the Masters module if I came

at it fresh. But it's the collective of the two that informs

your learning. So yeah, I've kind of put it on the back

burner for a while. I think I've six modules completed. I've

put it on the back burner for now because I started a new

job 12 months ago. I had a baby daughter nearly two

years ago so time's not ... it's not as easy to dedicate your

time to a full module at the moment. The online MOOCs

are a lot easier to complete.

Kirill Eremenko: 22:01 I wanted to know, why did you see the need for formal

recognition of your skills? I think it'll be a very interesting

useful question for a lot of people listening, because they

might be asking themselves the same question. Are online

courses enough or do I need a certificate from a real world

university saying that I have these skills?

Sean Casey: 22:21 I thought I did. I thought that acquiring a certificate from

a university would be what I'd need to make that

transition from the type of role I was in to a more

analytics role. And looking back at it, I probably didn't

need it. Don't worry, it definitely helped me because it

started opening conversations that yeah, I'm in the

middle of doing this. But the skills that I've developed

from the MOOCs and the online courses are, they're the

stuff that I uses day-to-day in my role and they're far

more accessible to people. They're far more affordable.

They're far easier to commit to.

Sean Casey: 23:22 You'll see posts on LinkedIn all the time about people

saying, what's more important? Is it more important to

have on-the-job training, online learning through your

MOOCs, enroll in a Masters. You'll see some people

saying that, stop posting ... I saw a post a couple of weeks

ago and some guy saying, "Stop posting these certificates

of online MOOCs, what you should be doing is working on

Kaggle projects." And I totally disagreed with that. I think

it's, you do what you enjoy and if you learn best through

an online MOOC and you feel like that you want to

accumulate a bunch of skills in this area before you can

even think about starting a Kaggle project, or maybe a

Kaggle project just doesn't seem as the best return for

you, I don't think anyone should say stop completing

these courses. Because it's all learning. It's all someone

just trying to learn more about the area and trying to

develop a set of skills in the area.

Kirill Eremenko: 24:25 Okay, thank you. That's very insightful. Let's talk a bit

about the way ... so, in addition to your learning, you told

me before that you read Cole's book. Cole Knaflic's book

about visualization in two days. I think I have the book

here. One sec, I'll just grab it. Actually, I have both her

books right here. I just bought them myself a few weeks

ago, so that's book number one, Storytelling Data. She's

got a second one, I messaged her, I invited her to the

podcast and she's like, "So, which book are you reading?

The first one?" It was like, "oh, you have a second book?"

And then there's a second one, it's called Let's Practice.

Sean Casey: 25:06 Need to get my hands on that.

Kirill Eremenko: 25:08 Yeah, hands on. So, I'm totally loving it. It's called

Storytelling With Data by Cole Nussbaumer Knaflic.

Fantastic book. You said you read it in like, two days, in

Thailand.

Sean Casey: 25:19 It was Vietnam.

Kirill Eremenko: 25:21 Yeah, Vietnam.

Sean Casey: 25:23 One of your guests, at the end of the show, you asked,

recommend a book. And one of your guests recommended

the first one, Storytelling With Data and the

recommendation was so strong that I went away home

that evening, bought the book, it arrived before myself

and my wife went on our Christmas holidays, I think.

Yeah, our Christmas holidays to Vietnam and I was sat in

this lovely little café in Hội An, read the book in two

afternoons and it was just that penny drop moment. It

wasn't that I was learning anything completely mind

blowing, it was just stating the very obvious facts that

you should have known when you were creating visuals

in Excel or in Power BI or so on.

Sean Casey: 26:15 So, up until that point, I would have had got a dataset for

a school I was working with, pumped it into Excel, ran off

a couple of visuals and the visual was the last part of the

step, up to that point, the visual was the last part of the

step. So, you did what ever transformations you had to,

and you produced a visual in Excel and you printed it or

emailed it or whatever. But you never did anything to the

visual. Whatever Excel recommend, you took their

recommendation. After reading that book, the visual is

only halfway along the process, because then you've got

the formatting power to tell your story through the visual.

So, simple things like just getting rid of noise, things that

should have been very obvious to me before that point but

you just needed to read it to realize it.

Sean Casey: 27:15 And playing with color, Cole is a big fan of grays and

blues and it just runs throughout the book. I've tried to

use that in many instances in my professional life just

what is ... A lot of my work would be around school

inspections and you can create a visual in whatever tool

you use and you can give it to someone and hope that

they take the message that the visual is trying to portray.

Or you can emphasize that message to a point that it's

impossible for the reader not to take a message. So,

putting the noise to the background in grays and just

emphasizing the key point. So, that's been really powerful

for me and the book just opened up my eyes to a whole

new aspect of, a whole new corner of data science.

Sean Casey: 28:14 Up to that point I guess I had seen data science as Python

or machine learning and that was the data science

journey, onto deep learning, AI and so on. But this

opened up a corner of it for me that there's a science

behind the presentation of information as well. And like

you've mentioned already, it's that crossover then

between creativity and how you present that information

is really insightful.

Sean Casey: 28:47 I got back from Vietnam. I think I'd already taken a Power

BI course before that through Microsoft, but Power BI had

changed so much since then. You've had guests on your

show, Tableau had come up a couple of times on your

show, so got chatting to a friend, said, "Here, what's this

Tableau thing about?" Same friend I mentioned earlier,

Gráinne. She said check it out and you've got a free trial

version with it. Played around with Tableau, it blew my

mind man. It was just how quick it was to get really

insightful visuals, interactive visuals that displayed a ton

of information and used a ton of data in them. So yeah,

that was mind blowing. And I used Tableau quite a bit in

my work when I could but my role wasn't, at the time,

wasn't solely on data. I had a lot of other hats I had to

wear at the time.

Sean Casey: 29:57 So, I could see opportunities in analytics for me and yeah,

that's probably the next question. You can cut it there

but there's probably another question you're going to ask

in a minute about how I got [crosstalk 00:30:13].

Kirill Eremenko: 30:12 No, no, please keep going.

Sean Casey: 30:17 Yeah, sorry. So yeah, I think it was around, yeah,

January 2018 I had been, I had a lot of modules done at

this stage, a lot of courses done, a lot of new skills that I

didn't have a few years previously and I got a random text

message from a good buddy of mine, Andrew, saying,

"Would you like to go caddying this weekend?" And I was

their-

Kirill Eremenko: 30:46 What is caddying?

Sean Casey: 30:48 Caddying is carrying someone else's golf bag around a golf

course.

Kirill Eremenko: 30:52 Oh wow, okay.

Sean Casey: 30:53 So yeah, so I'd never done it before but it was an

invitational that was on here in Abu Dhabi, so there was

a load of football players, ex Man United football players,

like there was Peter Schmeichel and Dwight Yorke, who

would have been the people we were roaring at the TV at

in back in the end of the '90s. Who else was there? There

was Luís Figo, Alessandro Del Piero. There was a load

more, Ruud Gullit.

Kirill Eremenko: 31:21 So, they all came in to play golf in Abu Dhabi?

Sean Casey: 31:23 They all came to play golf in Abu Dhabi and we showed

up as part of a group to caddy for them. It was an

invitational that was actually sponsored by the

organization I work for now, GEMS Education. So, the

two sons of the owner of the organization Jay and Dino

Varkey were playing in the competition as well as a

number of others. I got put on Jay's bag. Jay Varkey's

bag, so I was, carried Jay's bag around the course, had a

bit of a chat with him. He said-

Kirill Eremenko: 32:07 You were probably hoping for a football player.

Sean Casey: 32:10 I probably was but you know, it probably worked out a lot

better for me. I was probably hoping for Peter Schmeichel

but I think it worked out a lot better for me. I got chatting

to Jay throughout the round. He asked me what I did. I

obviously knew who he was. It would be kind of hard not

to know who he was over here.

Kirill Eremenko: 32:27 Even though you weren't working in the company, you

knew who he was?

Sean Casey: 32:30 Of course, yeah. GEMS, they're-

Kirill Eremenko: 32:32 So, it's a big company?

Sean Casey: 32:34 Big company, yeah. Very big in the UE. So, I got chatting

to him, asked me what I did, I said, "I work in school

improvement but I'm trying to branch into analytics, data

science," had a bit of a chat. At the end of the day, he

said, "Look, if you ever fancy coming to work for GEMS

send me your CV," which was very nice of him to say. He

didn't have to say it at all, but very nice of him to say it at

the end of the round. And then a couple of things

happened in my personal life. My wife had told me the

week before that we were expecting our first baby, so-

Kirill Eremenko: 33:15 Amazing.

Sean Casey: 33:16 Yeah. Incredible, incredible news and changes your focus.

But then the following week, the company I was working

for were going through some challenges and hit us with a

significant pay cut overnight. So, I-

Kirill Eremenko: 33:34 Must be tough knowing that you're expecting a baby to

face a pay cut at the same time?

Sean Casey: 33:40 Yeah, yeah. It was probably the fire I needed to get

moving. So, I said, do you know what? Jay told me to

send him my CV, sent him my CV and Jay set up a

conversation with my now boss Hywel Benbow who is the

GEMS VP for data, global data and analytics, so I set up a

chat with Hywel. We had a chat in a coffee shop in Abu

Dhabi for nearly two hours one afternoon and called me

for an interview. Went for the interview, thought it went

pretty well. But there was some challenges around

onboarding straight away. There was some ... I couldn't

join immediately, so I took a different analytics job with

the local Ministry of Education, stayed there for a year

but always had my sights on the GEMS role.

Sean Casey: 34:44 I thoroughly enjoyed the conversation that I had with

Hywel and the subsequent interview and could see that it

was a place that I'd be able to grow, I guess, be able to

grow in, grow professionally while also adding value. And

then I was lucky enough to be able to join them last

August and it's been a lot of fun since. It's been a lot of

fun.

Sean Casey: 35:14 I think I said to you at the end of my email that I know

it's a journey. I'm never going to know everything in

analytics. I'm never going to know everything in data

science, but I enjoy what I do. I enjoy getting up every

morning, going, all right, not going to work in the current

environment. Going to different parts of the apartment. It

doesn't feel like work when you enjoy it. Sitting at the

computer all day just playing around with data is very

enjoyable and trying to manipulate it so the dashboard

works the way you want it to. Or you're doing some

modeling that you're trying to increase the accuracy as

much as you can. It's a lot of fun.

Sean Casey: 35:59 So, I've been very fortunate with just answering the phone

call to my buddy that day, to getting an offer to send my

CV if I ever wanted to join their organization, to being able

to have a cup of coffee with my current boss. I've been

very fortunate to get those opportunities but I'm eternally

grateful to all the people who have helped me along the

way in my journey.

Sean Casey: 36:29 I think the beauty about data science for me personally is

that the community is so willing to help. It's so willing,

people are so willing to give you a little bit advice on the

way or try and help you solve a problem or direct you to a

different course or a different piece of learning. They don't

have to. They're busy themselves. They've got their own

demands at work and their own pressures in their

personal life but people are still on, you can post a

question on any one of the communities and you're pretty

sure you'll have an answer within 24 hours. For the ones

I've used anyway, the Power BI community or the

Enterprise DNA community. There's always someone

there to say, "Have you tried this?" So, that's part of the

reason I want to continue on with this, continue on this

journey.

Sean Casey: 37:31 Number one, I enjoy it. I enjoy it immensely. But it's the

opportunities to learn, or I'm never going to be bored or

stuck for something to learn in the future anyway, that's

for sure.

Kirill Eremenko: 37:42 That's awesome. That's awesome. And you're right. It's

important to enjoy what you're doing and I think we're all

fortunate in data science that the community's so

amazing. It makes it easier to enjoy what you're doing.

Imagine if there was a very back stabbing careerist type of

culture where you couldn't trust anybody, nobody was

willing to help. It would be quite hard to enjoy what you're

doing faced with that every day. So, I'm also very grateful

for that.

Sean Casey: 38:13 I don't think the area would be what is if it had that sort

of culture that you just described. I don't think the

advancements which have happened so fast in the last

five years, what's happened so quickly, wouldn't have

been possible if there wasn't that collaborative nature and

the willingness to help and the willingness to share my

piece of work. I go back to what Jon Krohn at

DataScienceGO Virtual, he didn't need to put up his

Google Colab book for everyone else to take. He'd spent

time working on that, spent time producing it, but he's

willing to share it. I think that's phenomenal. You don't

get that everywhere. You don't get that in every industry.

And it's because of that willingness to share and the

willingness to put your work out there that the

community's able to grow and advance at the speed at

which it has.

Kirill Eremenko: 39:16 Yeah. It's absolutely fantastic. You mentioned there is

some luck in your story by picking up the phone and

going, being put on the right bag of the right person while

caddying. Also, there was help from the community,

which is amazing. But I think it's important to also be fair

to you that you've did a lot on this journey to make it

happen. And with that, I wanted to ask you, what would

you say is the one biggest thing that looking back or

ability or skill or habit that helped you in this journey?

Something that you can share and other people listening

to this can replicate in their own journeys.

Sean Casey: 40:05 A hard one man. I think asking for help. I'll go back to the

asking for help when you need it is an important one. You

will encounter challenges along the way. There will be

hurdles that you're not able to overcome or parts of code

that you're not quite able to figure out. But asking for

help along the way, be it whatever, it doesn't have to be

an analytics journey. Whatever journey you're on, asking

for help when you don't quite get something or when you

just can't quite hack what you're trying to do or totally

digest what you're trying to learn, asking for help is a

really important one. Because people are good. People are

really good people. [inaudible 00:40:52] generally and

they're very willing to help.

Kirill Eremenko: 40:55 Why would you say that was a hurdle you had to

overcome?

Sean Casey: 41:00 I guess it's about your own belief in yourself that you

might be able to do this on your own without-

Kirill Eremenko: 41:12 Like asking for help means you've failed, type of thing?

Sean Casey: 41:17 Yeah. That might be a subconscious thought in your

head, but I think throwing that off early, no matter where

you are in your learning journey in whatever area you're

learning in, I think that's, throwing that off quickly and

knowing that it's okay to ask for help.

Kirill Eremenko: 41:37 Okay. How do you ask for help? Where do you ask for

help?

Sean Casey: 41:40 My team is, the team I work with is incredible at the

moment. And I think lockdown or remote working has

really helped us with that. We're a small team, but my

boss Hywel will set up a time where we can go onto

Microsoft Teams call and he'll share a piece of his work

from the last couple of days or I'll share a dashboard that

I've been working on. And you put your hand up straight

away. I've hit a problem here. Can anyone here have a

look at this? So, the team together will try and

troubleshoot the problem on the screen. But that could

be a first one if I'm at home trying to figure something

out. By night, I'll go to YouTube straight away because if

I've ran into the problem at my stage of the journey,

someone else has encountered it before.

Sean Casey: 42:35 Last night, my issue was around a refresh in Power Query

taking incredibly long in relation to the size of the dataset

I was working on and a quick video from, I don't know if

you know those guys from Guy In A Cube, it's five minute

videos on how to figure out your own challenges in Power

BI. So yeah, I was basically putting too many marges into

Power Query that I didn't necessarily need. So, that was

slowing me down. I go to the communities. There's always

someone in one of the communities who'll offer help.

Kirill Eremenko: 43:16 What communities?

Sean Casey: 43:18 Power BI. Most of my work's in Power BI, so the Power BI

community, I'll go there. I'll go to the Alteryx community

and someone will have published their workflow on the

Alteryx community which you can just download and

adapt for your own problem or your own project you're

working on. Stack Overflow if I'm working in Power BI or

in Python, Stack Overflow's definitely my go to if it's an

issue in Python. Unless you're at the very edges of the

data science space, someone else has encountered these

problems before. They're quick fixes. The code will be

there for you to copy and paste and use in your own

projects, in your own work. I think it just goes back to

that collaboration and that willingness for people to share

their work, put their work out there and let others learn

from it and then take it further. That's how it grows.

That's how we've got tech to the mind blowing space that

it is in the last 50 years. It's incredible.

Kirill Eremenko: 44:32 Yeah, yeah. Absolutely, absolutely. Yeah, so interesting.

Your advice about asking for help goes back to not just,

because first I understood as an external asking for help.

But it's a combination of asking for help externally and

searching for the right answers that others have maybe

already asked for and they exist. Ultimately, it is what

you said in terms of being able to be honest with yourself

and be kind to yourself that, hey, I don't know everything.

It started fine, I've tried to figure this out. Let me go check

what others suggest. And so not being stubborn, I guess,

and trying to prove to yourself that yes, I have to do it

myself.

Sean Casey: 45:19 That's an internal thing. That's something that you need

to ... I'm not saying it's something, it's a challenge that

everyone has but-

Kirill Eremenko: 45:27 That's true. Success is 80% psychology and 20%

mechanics. Most of the time what is stopping us from

progressing in our careers is internal. So, it's a very

useful piece of advice that you're sharing, that there are

people out there who are probably stuck because of some

internal stubbornness or fear of being an imposter. Or

fear of being, feeling that they're not good enough or that

they fail. Fear of failure. And that is really preventing

them. So, looking within will always yield much more

progress than looking without.

Sean Casey: 46:07 Yeah. And just something you said there about feeling

that you don't belong. I mentioned this in my email to you

but I remember that first certificate I got from one of your

courses on Udemy, and I think it was the Python A to Z

course. I'd seen loads of them on LinkedIn, I'd seen loads

of other learners posting them on LinkedIn up to that

point. And I got that first certificate. I can't remember the

exact day, so I'm going to guess it was some time around

late 2016, might have been late 2017, I can't remember

exactly.

Sean Casey: 46:49 But I posted that certificate on LinkedIn, at the time I

might have had 150 connections on LinkedIn. I wasn't

very active on it at all. But because I tagged yourself,

SuperDataScience, people started seeing it. People from

all corners of the world started clicking on it, writing a

little encouraging post. It was like their way of saying, "Hi,

you're dipping your toe into data science? We welcome

you. We welcome you with open arms." It was powerful.

Not that you're doing it for the likes or you're doing it for

other people, that's not why you're doing it, but it was

just the sense that right, the community's happy to see

someone else here and you're not an imposter. You're

learning like the rest of us. We all have to learn

somewhere.

Kirill Eremenko: 47:40 I love it. You're not an imposter, you're learning. That

should be the tagline of this episode. I love it. That's

awesome. Sean, what's next? What's next for you?

Sean Casey: 47:48 What's next? I enrolled in your data associate bundle and

there was ... that was free a couple of days ago. I think

the whole team enrolled in it so I want to complete that.

Kirill Eremenko: 48:04 Awesome.

Sean Casey: 48:04 And start ticking off a few courses. I'd want to be able to

spend a bit of time looking into computer vision and NLP

a little bit more, but I've a few other areas I need to tidy

up on first before I get there. Yeah, just keep learning

man. Just keep enjoying this and keep trying to find

better ways of doing what I'm doing already. I'm learning

a lot in, every day, just on the job I'm learning a lot in the

backend of Power BI and the Power Query part of it and

trying to make, try to spend more time in there. And

spend less time on the canvas if you know what I mean.

Just setting it up right in there.

Sean Casey: 49:00 What else? Yeah, just keep having fun man, keep

enjoying it. Keep sharing my learning with other people if

they ask. Along the way, I've had a lot of people ask about

... I don't try and portray that I'm a data scientist by any

stretch of the imagination, it's a goal that I'd like to get to

at some stage. I use a little bit of modeling every now and

again but that's the ... But if people ask you, "How did

you get into this, what were you doing?" I'll always send

them in the direction of a few different courses. At work, a

lot of people ask about Power BI. They see the product of

our work in the dashboards we publish and they'll ask

me, "Okay, where can I start learning?" We've got the

licenses to share with them and it's share learning

opportunities, share courses, just let people, welcome

people. I was welcomed in, welcome other people in.

Kirill Eremenko: 50:06 Fantastic. Well, very inspiring advice. Sean, this slowly

bring us to end the podcast, I wanted to ask you, to finish

off, what's your one message to those learning data

science? Those that are starting out this journey, people

who are feeling just like as you were, dipping their toes

into this field. What would your one biggest piece of

advice be for them right now?

Sean Casey: 50:36 To start small and all of a sudden new aspects open up

very quickly. When I say start small, take an online

course in a data vis tool or in a programing language and

once you've completed it and you still like it, all of a

sudden a whole new set of doors open. And when I say

doors, I mean doors within that learning journey. So, I

had no idea when I started out in data science that I was

going to end up spending most of my time in Power BI.

That was a door that appeared after I'd learnt a certain

amount of skills already, or developed a certain amount of

skills already.

Sean Casey: 51:29 And that's another part of it too with the learning thing is,

there was a challenge recently ... Yeah, so there was

something I hit recently on using a rolling average in

Power BI. It's the same in using it in, hitting a problem in

another area of a programing language. When you learn

how to do something differently, you then start applying

that new learning to your work, to your, be it your

dashboards or your code or whatever. Until you hit

another new problem because of what you learned with

this problem. I'll just take an example, all of a sudden I

can do rolling averages. Now, the next part I'm going to

hit is I'm going to hit a challenge around rolling averages

that are split over different quadrants or different ... So

it's [crosstalk 00:52:26].

Kirill Eremenko: 52:26 I think it's called a threshold concept. Because once you

learn it, it's something you can't unlearn and makes you

see the world differently. Now that you know rolling

averages, you're always going to think, oh, can I apply

rolling average here. You're always going to see those

same things that you saw a year ago but absolutely

differently because there's potential for you to apply this

new skill.

Sean Casey: 52:50 Yeah, definitely. And until you hit the next problem, and

then you're better. You hit the next problem, you go away,

you learn how to solve it, you ask for help, you apply it

and you'll hit another problem again. We're never going to

be bored anyway, that's for sure.

Kirill Eremenko: 53:06 So, basically, start small and if you like it, progress in

that direction. If you don't like it, try something else.

Sean Casey: 53:12 Exactly. Because there is so much to it. There's so much

in the data science, data analytics area. You don't have to

be working on the same tools. The tools are adapting and

being produced and being released quicker than we can

keep pace with. But it's the skills. It's the way you

approach it, it's your thinking that will get you through.

Kirill Eremenko: 53:43 Awesome, awesome, thanks Sean. Great advice. Great

advice. On that note, we're coming to an end. To wrap up,

I want to say thank you for coming on the show. And

also, before we finish off, before I let you go, where's the

best place for people to get in touch with you? Maybe they

have follow-up questions, just want to connect, network

with you.

Sean Casey: 54:05 So, LinkedIn's the easiest one. Sean Casey on LinkedIn.

I've taken a bit of inspiration from Emily's talk at

DataScienceGO Virtual and I'm in the middle of hopefully

setting up a blog post as well. It's not there yet but it will

be and once I have that ready I'll let you know because as

Emily said in her presentation, and as I've heard from

loads of other people already, that you have this

knowledge now, don't keep it. Share it. Let other people

learn from it. So yeah, I'll have a blog post later on. It will

be on InsightAndAnalytics.com, but it's just not there yet.

It might be by the time the podcast airs.

Kirill Eremenko: 54:52 Maybe, yeah. If you put in a bit of work very soon it might

go there soon, it might be there when the podcast goes

out. Okay, fantastic. And so, LinkedIn and you said

Insight and Analytics?

Sean Casey: 55:08 Yeah, InsightAndAnalytics.com.

Kirill Eremenko: 55:12 InsightAndAnalytics.com. Awesome. Well, fantastic. One

final question for you, what's a book that you can

recommend to our audience?

Sean Casey: 55:18 I think you probably have it within reach there, do you?

Kirill Eremenko: 55:22 Ah yeah, this one. Storytelling With Data. Definitely.

Sean Casey: 55:25 Amazing book. That doesn't have to be for people that

work solely with data. Anyone that presents information

in any aspect of their role, if you want to make sure your

message, the message you want the audience to take from

the visual is what they take, that book's definitely going to

help you.

Kirill Eremenko: 55:47 Fantastic. And it's such an easy read. It's big because,

like as in the size, the height and the width is big because

the images, but there's a lot of images and there's a lot of

margins. I can tell you, when you said you read it in two

afternoons I was so surprised but then when I started

reading it, it's so easy. You can read a whole chapter in

under an hour very easily.

Sean Casey: 56:11 Yeah, and I was, I don't know, I suppose the setting

where I was at the time, the book, everything, it's just one

of those moments that I look back on, like that penny

drop I said earlier on. It was so enjoyable. And a great

read and I followed Cole as well on LinkedIn and seen

some of the stuff that she's [inaudible 00:56:33], some of

her talks and presentations and it's great. It's great to

keep learning [inaudible 00:56:39].

Kirill Eremenko: 56:39 Fantastic, all right. Well, Sean, thank you so much for

coming on the show today. It's been a pleasure.

Sean Casey: 56:45 Nice one man, thank you very much for having me and

thank you to all the community, you're great.

Kirill Eremenko: 56:55 So there you have it, thank you so much for spending this

hour with us. I hope you enjoyed the conversation with

Sean and got lots of valuable take aways. I actually had

read his story, he sent it to me in the email before the

podcast, so I knew lots of, many parts of it, but at the

same time, during podcast, I found myself listening and

mesmerized by how he was describing the things that led

him to be where he is now.

Kirill Eremenko: 57:21 Every story is unique, every story is so interesting and

thank you very much, Sean, for coming on the show and

sharing your story. My favorite part probably was the

advice that Sean gave at the end. Start small. It's such

valuable advice. Data science is such a broad field.

Doesn't mean if you're into data science you have to do

machine learning, computer vision or artificial

intelligence. Don't have to be an expert Python

programmer, you can go into data visualization, or you

can go into machine learning and Python. Or you can go

into data preparation and SQL and databases. Or you can

go into data science leadership and management and

things like that.

Kirill Eremenko: 58:01 There's lots of areas to get into data science, and by

starting small, you reduce the downside. Basically, you

don't invest three years of your life into a degree that

might not be exactly that part of data science that you

want to be doing. So, starting small, trying out a few

courses, understanding what you actually like about this

field is a great, great thing. And of course, talking about

the data science community, that was fantastic. I love

everybody in the data science community. It is so friendly.

Kirill Eremenko: 58:31 As usual, you can get the show notes for this episode at

SuperDataScience.com/383, that's

SuperDataScience.com/383 where you will find transcript

for this episode and any materials we mention on the

podcast.

Kirill Eremenko: 58:44 And if you found this episode inspiring, educational,

motivational, that it challenged you, that it approached

you to think in a different way, then share it with

somebody you know. Somebody who might need that

extra boost of motivation or inspiration to keep going with

their data science journey. Very easy to share, just send

them the link, SuperDataScience.com/383.

Kirill Eremenko: 59:04 And on that note, I look forward to seeing you back here

next time. Until then, happy analyzing.