sds podcast episode 383 · 2020. 7. 15. · kirill eremenko: 00:00 this is episode number 383 with...
TRANSCRIPT
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.
Kirill Eremenko: 18:33 This episode is brought to you by SuperDataScience, our
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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.