Did you come to Python from the academic side of the world? Maybe you got into working with code for
research or lab work and you found that you like coding more than your first field of study.
Whatever the reason, many people make the transition from the academic world over to
tech and industry. On this episode, you'll meet three women who have made this transition and
you'll hear their stories. I'm excited to speak with Jennifer Stark, Kaylee Haynes, and Esleen
Becamu about their journey into the tech field. This is Talk Python to Me, episode 259, recorded
April 6th, 2020. Welcome to Talk Python to Me, a weekly podcast on Python, the language, the libraries,
the ecosystem, and the personalities. This is your host, Michael Kennedy. Follow me on Twitter,
where I'm @mkennedy. Keep up with the show and listen to past episodes at talkpython.fm,
and follow the show on Twitter via at Talk Python. This episode is sponsored by Datadog and Linode.
Please check out what they're offering during their segments. It really helps support the show.
Kaylee, Esleen, Jennifer, welcome to Talk Python to Me.
Thank you very much for asking me.
Thank you very much for having us. Thank you.
It's really great to have you all here. I was in a PhD program. My wife is a professor. So I've,
I spent a lot of time in the academic world. And I saw that you all were participating in this,
I think at the time was going to be a live event around sort of sharing stories going from academia
into more of the tech side of the world. And I thought, oh, what a fun topic. I have to reach out
to you and have you all on the show. So I'm happy you're here.
It's great. Great. Thanks for reaching out.
Yeah, absolutely. It's a really interesting story how people spend so much time working on,
you know, getting a PhD or getting some academic position. And it's just, I find the whole academic
space is really different than the tech space. And just to give folks a sense, you know, like the story
for my wife, if she wants to change jobs, like there's only a certain time of the year that jobs are kind of offered,
right? Like interviews are done in January and you apply to them and there's like 30 or 40 in like a very specific
area in the world. And it's just such a different world than the tech space. But at the same time, college campuses
are beautiful places. Like the energy is just, I love being on campuses and whatnot. So it's, it's just such a trade-off to make,
I think. So I'm really interested to dig into that with you. But before we get specifically to that,
I want to start with your story. How do you get into programming in Python? Jennifer, you go first.
Yeah, well, it's by accident. So I was working in the US at the time in Baltimore for the NIH. And it was
during the recession, I guess, so 2009, 2010. So the funding was frozen for a number of years. So we had to cut
back what we were like the fancy, not it wasn't even that fancy, but software we're using and use
open source software. So we started with R. So I learned R.
Okay, nice. And what were you using before? Was it like MATLAB or SAS or something like that?
Yeah, some MATLAB and some other statistical programs that were made. But actually there were some of those
were also open source, specifically for the kind of data that we're analyzing.
And this I'm guessing is in the health and medical research area if you're at NIH?
Yes, it was NIDA, so National Institute for Drug Abuse.
Yeah, so you learned R. And then somehow you got from R. Are you doing R only? Are you doing R in Python now?
Currently, I've not used R for a really long time. I moved over to Python because the kinds of things I wanted to do
broadened and Python's good for so many things. So it just seemed like a natural step for me.
Yeah, I think that's a trade-off a lot of people have to make between R and Python. I feel like R
is very specialized and it's very powerful, but then Python allows you to say like,
I have these same skills. Oh, and I could go build this web app or I could go build this other thing
that is not actually just about visualizing and processing data.
Yeah. I mean, I think the last R package I used was for looking at diffusion. And the only place you
could find that could allow you to do cartotic diffusion was an R package. So that's how
specialist it is. It's amazing.
Yeah. Yeah. Wow. Very cool. Aislinn?
Yeah. So I started, I think, well, I learned Python when I was in my second year as an undergrad.
So in my first year, I was using MATLAB and my second year, I was introduced to Python.
So yeah, I was studying maths. So yeah, I did a degree in maths and I just fell in love with Python.
So yeah, since then I've just been using Python, but yeah, it was good.
Yeah. Yeah. Very good. And I also was working on in math for a long time and
it's a fun world, but I got to say, I like programming better.
Kayleigh, how about you?
Yes. I went to university to study physics and then after a few months, I realized I didn't
really like physics. So I moved into a maths and statistics degree. So I've got the statistics
background. So I've come from R. So I think I must've learned R about my third or fourth year
undergrad. And then from that, I then decided to go and do a master's and then a PhD in statistics.
I sort of got quite a strong R background. And just recently, I guess I'm trying to learn
Python, but very, very like beginner level. And I guess that means Jennifer sort of knew
each other through like PyData and R Manchester.
Nice. And I guess it's worth mentioning that you all come from the same town, Manchester in
the UK. So you, you could actually see each other at meetups and whatnot, right? Not just
necessarily online.
In the old days.
Yeah, that's super cool.
Yeah. We're all in Manchester now, but we're not all, none of us are from Manchester, I don't
think.
No.
Well, and you know, I mean, we'll get into it later in the show a little bit, maybe,
but the idea of getting together and seeing each other right now is kind of, you could
be on the other side of the world. It's, it's still a weird time. So yeah. Well, I guess,
you know, I hear a little bit of your story and how you got into these, these various areas,
but what got you interested in sort of computational side of, of computers and programming and whatnot?
I mean, Kaylee, go in reverse order, I guess. So if you're not into physics and you want to
switch into math, like when I was in math, it was pen, paper, chalk, chalkboard, right? Like
let there be a theorem. I did end up doing a bunch of cool, like visualization work and stuff
near the end, but it definitely didn't start out that way. It started out with, okay, we've got,
you know, seven axioms in a corollary and you've got an hour to come up with something.
Right. Right. So what got you sort of going down that path?
So I think the reason I changed from physics to maths was I didn't really like
the practical lectures at the time that I was being taught when I went to university,
which is sort of interesting now because I'm more interested in the practical side. But I think
first year physics, when I went to uni was very basic and very boring. And I remember I always like
back to the same lab that we had where we just had to swing a pendulum for an hour. And I'm sure that's
interesting to some people, but it just really didn't interest me at all. So then I had moved
into maths and statistics. I just had this really great lecturer in statistics in first year who was
so enthusiastic about data, which I think that sort of helped seeing someone else be so enthusiastic
about it. So then I decided that statistics was the degree that I was going to do. And I think,
is it like my third to fourth year? So we do four year undergrads up in Scotland. So I think in
between the summer of my third and fourth year, I then decided to go do an internship at Lancaster
University. And there's a really good doctorate training centre on statistics and operational
research. And that was really interesting. It was sort of like an eight week programme,
where you got given a stats project to do. And it gave you an insight into doing a PhD.
And the doctorate training centre at Lancaster is sort of built up and geared towards giving you an
insight to doing statistics, but in industry. So it gave us a real like practical sort of use to our
data. So as you said, like a lot of maths was like theorems and just different like feature species and
geometry, which was all about abstracts and something that I can't really like bring my mind to visualising.
Whereas I'm more of a, I like to see the data and insights and see plots and visualisations.
Yeah, I think having some kind of concrete project like that, just, it completely changes
how you experience it, right?
Yeah, definitely. And it's sort of, you can then see like actionable insights and you can see the point.
Whereas I feel like going down a more theoretical maths route, it's quite difficult to see.
Well, for me anyway, like what's actually going to be the point of this at the end?
You know, it's a little bit how I felt as well. Like I felt like it was okay. Now at some point you just start
solving these abstract problems for the sake of, well, they've solved them up to here. So the next logical
problem someone's got to solve is to keep pushing it. But it's like, well, I'm not really sure what this is
contributing back to the world compared to so many other things that you could do, for example, like healthcare
or something. So yeah, that was sort of my thought as well. Esleen, how about you?
So I, when I did my degree in maths, I did pure maths. So that was even more abstract, but I quite
enjoy it. I really enjoyed it. And it's only, so after my degree, I didn't know what to do. So I wanted
to get into accountancy. So at the time I wasn't thinking about big data or anything like that. So I
had a conversation with my family, a project supervisor. So can I mention his name? He means a lot to me.
James, Dr. James Burridge. So we had, we had a conversation about what I wanted to do after I
graduated. So I told him about getting into an accountancy firm. And he said, have you considered
about, you know, doing a PhD? So I thought in my head, I was thinking, oh my God.
But I did say yes, I did say yes, because I really enjoyed, I really enjoyed doing my degree in maths,
especially my final year project. I loved it. I really enjoyed it. So I did say yes. So in 2015,
I went back to the same university. So the University of Portsmouth, and then I did my PhD. Now my PhD was in
applied maths. So that was applicable.
That's a big switch. Yeah. And that's when I realized how important maths is in a way, and how many problems you can solve with maths. We in my PhD, we actually came up with a model. So we derived the model from scratch, and we're able to implement the model in Python, and then get some data and then analyze the data. So we just became so real and so much interested. So yeah, I mean, that's when I found it.
I love with like data. And yeah, even more maths. Yeah, absolutely. And the really interesting thing that I hear from both of you is like, there was this one professor who made such an impression. And I would say the same reason I went and spent a lot of time studying what I did was, there was, I think it was like a Calc three class or something like that. And it was just such a great person I connected with, and encouraged me to go down that path. And you know, I see in my daughters who are in their second year, going to be in their second year,
college. Same thing. There was one teacher in high school that was just such made such an impression, they're both doing their thing. And it's, it's, it's amazing how these small interactions can have such huge effects. Yeah, yeah, definitely. Yeah, Jennifer, how about you? The question is how I got on to how did you get into like, working with data and like sort of computational programming side of things?
Just the nature of the PhD, I did actually, so undergrad never had anything, particularly complicated, just regular biology type, statistics, like t tests.
Yeah, very, very, very, yeah, exactly. Very few. Yeah, just, just enough to like, like do the lab work, right? Yeah. Very few people, I think, go into biology thinking, I'm going to be a biology programmer type person, right? No way, yeah.
But it's somehow it seems to like, draw you in, right? Totally accidental. Yeah.
Yeah, okay. Went from t tests and an overs on, you know, a couple of subjects to, to doing well, so my PhD was looking at appetite systems. And it was a PhD that was the first, it was in Manchester as a first day, first time they'd had two different departments collaborate on a PhD.
So it was between a traditional biology wet lab, which I was comfortable in, and the neuroimaging lab. So we were doing MRI studies to look at appetite systems. And of course, MRI, you've got three dimensional data sets. And if you take a brain image every three seconds, then you've got four dimensional data. And that requires a very different analytic approach.
Yeah, that's very different than taking measurements in a lab or growing something in a Petri dish or something.
Very, very different. And then you're in the world of signal processing, which wasn't what I was expecting. So now you've got to deal with drift of the signal over time. And then there's so many different ways to pre process signal, signal data that or time course data that not everyone agrees on how it should be analyzed. So that's fun as well.
So yeah, at the time, there's several university developed software that's specifically designed for MRI. And so that makes it open source, because it's designed, it's created with public money. There's three main software tools available. And then usually like user interface, you know, you sort of click and, and build up your process that way.
But they're also developed in a way that you can you can code it instead, you can write a bash script that will then link all the different steps together. And then you can do batch style analysis. So I guess that was my first entry into that kind of analysis using bash scripts, and which was terrifying at the time.
Yeah, you're probably thinking, how did I get here? Like, this is not what I signed up for. I didn't. What happened? Huh?
Yeah, but it drew me in, like you say, as I got into it.
Yeah, I think I mean, that's my theory about a lot of these things is people don't go into the data science side of this and that, you know, the data science Python tools necessarily, because they start out with the plan to be a data science programmer type.
Like, they just have they're, they're doing work just like you described. And they're like, well, I, I've got to figure a little bit of this out.
Because otherwise, it's untenable, or it'll be, you know, 10 times more effective or whatever. And then a little bit more and a little bit more than you look back and like, wait a minute, I've been programming all week. And that's all I've done. Like, how did this happen? How did I get here? Right?
Yeah, it's brilliant.
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I think it's going to be interesting, though, in like the next generation of data scientists, because I think we've all come from a background where data science wasn't a thing a few years ago.
Like, I only ever heard of data science when I was like finishing off like my PhD, probably in the last like year or two, where I was then looking for a career and starting to see like my peers a couple of years above me, starting to apply for data science roles.
Whereas now, there's more data science courses.
And I think I say you're going to then get a different breed of data scientists who have gone into it wanting to be a data scientist, not necessarily come into it from a different background thinking, oh, wait, I've been doing MRIs and data processing, thinking processing, and now I'm a data scientist.
I think that's so interesting. Yeah, go ahead.
Yeah, I totally agree. I mean, a few years ago, when I was doing my degree, I didn't know nothing about data science.
I think it's a new, it's quite new, and it is sort of improving.
There are new tools constantly being built.
So it's very, I think it's an interesting area.
So I think for anyone joining into the data world, they're realizing that there's so much to learn and to do.
And it's just like finding out what you can do with the data.
It's not, it's enjoyable.
Yeah, it's, yeah, it's really interesting.
I don't think that for very long, there's been, I don't know how many data science programs there are, where they say, here's a data science university program.
I don't even know if they've been around long enough for people to graduate from them yet.
I think there are some master's programs at the moment.
So one, one year master's programs.
Yeah. So if they're one year people probably, or there's probably some graduates, but certainly 10 years ago, I don't think, I think it was that way.
So it's a really good point that when you kind of look out and you say, well, what can I do?
You look at the degrees that you can go get and then what those kind of lead onto.
And you're right that now I definitely think there's people that are like, oh, this AI stuff is really interesting.
Machine learning is interesting.
The visualization is great.
And when I was studying math, the choices for what you could officially do with your math degree were extremely limited.
It was, let's see, you could go into, you could be, what's the analysis for life insurance?
But actuary.
Actuary. Yeah, actuary.
Or you could go into like Wall Street or you could be a professor or a teacher.
And that was it, right?
And I think that probably is happening across all these different areas.
It's probably happening in biology and it's happening in economics and so on.
And I feel like it probably this side of the world has opened up new paths for all those different focuses as well.
What do you all think?
Yeah, definitely.
I think as well it's good though because it means that like we get quite a mix of backgrounds.
So you do get the people that are now data science coming from a physics degree, biology degree.
So I work at a company called Peak where we've got a team of about 25 data scientists.
And everyone's come from all these different backgrounds and it's really great to get sort of like the different expertise.
Some people have different ways of like visualizing things.
Some people have come from more computer science type degrees.
I think that's my point I made a bit earlier.
It's like if you're then going to just get students that are going to go straight into data science,
you sort of lose some of that background and the expertise of different areas coming together.
Yeah.
It sounds like everyone agrees on that.
You're all shaking your head.
No one can see on the audio, but yeah.
Yeah, for sure.
And I think by working with people from different backgrounds, you get an opportunity to learn from them.
And so I think it's great.
Yeah, for sure.
You touched on what you're doing now at Peak with 25 different data scientists.
Let's dig into that for all of you.
So maybe you could expand on that just a little bit.
Like what kind of projects are you solving?
What general area are you working in and so on?
Yeah, so actually I'm a bit of a weird one at the moment.
So I'm in my last two weeks of Peak because I'm moving companies in a couple of weeks' time.
Okay.
Yeah, maybe tell us what you have been doing and where are you going?
Or what your future holds?
So at Peak, we've got 25 data scientists.
We're a company in Manchester, a startup-based company, who we sort of sell AI to businesses
that don't have the capabilities in-house to do data science and machine learning.
We've got a wide spectrum of customers, ranging from like household retail brands to like manufacturing.
So within the data science team with 25 offers, we've got two teams.
We've got one team that focuses more on like customer AI.
So customer AI is things like recommendation systems, churn analysis, lifetime value.
And then the team that I sit in is in the demand AI side.
So we do things like demand forecasting, inventory optimization, warehouse optimization.
So a lot of projects I've been working on are things like forecasting.
So my background in time series analysis.
So I did a PhD in change point detection, which I think is quite relevant at this moment that we're now in a big change in life
and like being able to detect change points and be able to work with them.
Just a plug there for change point detection.
So yeah, so that's how I sort of come into this team.
A recent project I've been working on has been more in like warehouse optimization.
So we've got a retailer that are trying to optimize how they pick their clothing orders around the warehouse
and trying to like come up with like the most efficient ways to walk around the warehouse to pick things up as quick as possible.
So that's what I've been working on at Peak.
I'm moving to a company called Residently in a couple of weeks time,
who are another Manchester best startup,
but they're more in, they do like sort of property technologies at PropTech.
So they've got like rentals.
So a bit like, I guess, Airbnb, but more sort of long-term rental space.
Sure. Okay. Yeah.
That sounds like a bunch of fun projects.
Jennifer, how about you?
I'm now working at Ladbible, Ladbible Group.
So they were born on a Facebook platform.
Now it's a website and on Facebook and all the other social media you can imagine.
Sure.
So I'm the lead engineer, data engineer on that team, on the data team.
There's only three of us.
We're tiny.
And current...
You know, some of those small groups though are so cool because you get to,
you don't get pigeonholed into a little tiny thing.
You get to actually see and work in the whole system, all the software.
And it's hugely valuable.
It's a little stressful because like all of a sudden...
Yeah.
Everyone wants something, which is great.
It's a great problem to have.
And yeah, I wanted to work in a small company so that I can be involved in all the things.
Because I want to do all the things.
Everything interests me.
I always want to learn new stuff.
And this afforded me the opportunity to do that.
So yeah, there's just three of us.
We're currently working on the new data warehouse using Google Cloud Platform.
So we currently got a data warehouse, but it's...
We want to have a...
GCP can be more scalable and more robust.
It's got great monitoring.
And the dev team uses GCP as well.
So it's good to be on the same stack.
Yeah.
Nice.
Yeah.
Also, it's got...
You can use Python with it.
So that's good.
Yeah.
So we've got big plans for that.
Currently building out the data warehouse so that we can then carry out our big plans later.
Yeah.
Cool.
The Lad Bible.
This is like...
It looks like sort of a new take on news in the social media landscape.
Something like that.
Is that roughly right?
Yeah.
It's more lighthearted news, I suppose.
Yeah.
Spreading positive attitudes, which is really great.
Like I really like working for them.
The teams are amazing.
So great to work with.
Yeah.
It sounds really fun.
It's definitely something that we could use.
Esleen, how about you?
So I also work for a startup company in Manchester.
It's called Agent Software Limited.
We aim to revolutionize the way estate agents across the UK market their services using targeted
prospective material.
We've seen that agents have sort of seen sensational results with a fraction of marketing outlay.
I work in the data team.
So I work as a data scientist.
I joined as a junior data engineer.
I'm still learning about engineering and I have a great manager and a great boss.
He hates when I call him boss.
I can't confirm.
He's brilliant.
He used to be my boss.
Yeah.
Nice.
Yeah.
So, yeah, we work on some interest, but great projects.
We work.
So, for example, the project we worked on about two weeks ago, we estimated the value of a
property.
So any properties in the UK.
So we sort of started a model to get the estimated value of any property in the UK, which I think
is great.
And the one we're working on the moment is the propensity to sell.
So that's the project we're working on the moment.
I think at the moment, well, I've got some features that we both agree to use.
So it is great.
I'm enjoying working as a data scientist, especially within the company, because, you know, there
are some amazing people and I get along with them.
So it just makes life easier.
That's really great.
It's interesting.
I hear you all talking about all these people that you're working with, how great it is.
And people who are coming in, maybe they're in academics or something, but they're not in
the programming side.
Maybe they think of the programming world as, you know, you sit in a dark room by yourself
and write code.
And it doesn't sound like that's your experience.
No.
That would be great.
I wouldn't be against that.
There's a lot of people management and not like managing a team, but talking to stakeholders,
managing expectations.
Yeah.
A lot of times you're the person between the people who want the software and maybe the
customers or who's using it.
And you've kind of got to juggle all that, like that project management sort of side, right?
Like, what are we going to focus on?
What are we going to build for them?
And so on.
Yeah.
Well, I mean, in our team anyway, we've got a project manager and he's brilliant at helping
to protect us from that and making sure our priorities are aligned with that other business.
I sort of code maybe half the time.
And the rest of that's sort of planning and coordinating and meeting and discussing discussions
with people, trying to figure out what they need, what we can do to make things better.
And, you know, all that kind of thing.
It's good.
Yeah.
That's cool.
How about you others?
Same time balance about it?
Or how is it?
I think at the moment I do more coding.
So I was team lead until last summer.
So I had like a lot of time just spent like managing a team and setting them tasks, making
sure everyone like was okay with the customer work and sort of was that sort of in between
sort of firefighting type role.
But then I took a decision that I actually wanted to spend more time learning and developing.
That's where sort of my Python development came up.
So I was like, I just want to take a bit of time away from the team lead role and actually
spend time coding.
I think from doing that, I've actually managed to carve quite a lot of my time just to focus
on the coding side of things and actually developing the models and doing analytics.
And then we have a customer success team that's sort of the link between our customers and the
data scientists.
So they can do a lot more of the like customer engagement and making sure that the customers
are happy and sort of communicating what we're doing for them.
And we get a bit more time to actually focus on doing data science, which is really cool.
Yeah, that's great.
Esleen?
I think at the moment I'm doing more research just for the more, because we want to build
a model for the propensity to sell.
I'm doing more research, but at the same time, but doing research gives me sort of an opportunity
to learn new techniques and new tools.
So yeah, I'm not coding at the moment, but I am missing it.
Yeah.
Well, one thing I think is worth talking about maybe is all of your backgrounds were not officially
in computer science from the beginning, you know, freshman year and university.
And neither was mine.
And that means when you get into the industry, it's a lot of like what you described.
It's a lot of research, a lot of figuring out like, oh, I know this, but now they're asking
me to do this other thing.
I have no experience.
Like I have no idea how to talk to a database, but apparently this is what I'm figuring out
soon.
Right.
Like that kind of path.
Do you think that your academic backgrounds and just the whole sort of advanced study side
of things just kept going when you got into the tech industry?
And was it helpful?
Yeah, Jennifer?
Yeah, absolutely.
I think, so I don't know what people imagine on the whole when they think about what's
different between academia and industry, but there's a lot of transferable skills between
PhD and industry.
So planning experiments or planning projects, that's the same research.
So like when you go into a new project, you've got to do the research to figure out like what's
been done before, what hasn't been done yet.
So how could you go about testing something or learning something?
That's the same, which as in just described.
And yeah, you come up with, you're giving you tasks and like, well, I've not actually
done that before.
Like I've not done recommendation systems before.
I've got to figure that out.
You're still mentoring.
So the higher up you go in academia, you start mentoring students.
You do the same in an industry.
Maybe you're a TA teaching a class while you're doing your research or something like that.
Yeah, exactly.
Yeah.
And you're mentoring junior or foundation level data scientists or whoever, when you get
into industry, you're always learning using your reasoning skills really to get through
any of the work that you do.
It's the same in academia as it is industry.
There's a lot of overlap between the two.
I think definitely my sort of academic background has taught me to be sort of curious and to
keep asking questions when I'm doing tech, especially it's quite easy as a scientist, I think, to
sort of get your data, run it through some sort of like psychic learn type model, get the
result.
Here's the answer.
And then just go with it.
And I think it's, I think what a PhD has sort of developed is that sort of deeper thinking
of, but why is that the answer?
Can I explain that?
What happens if I change something?
And I think I've like interviewed quite a lot of data scientists over the past couple of years.
And you notice more sort of the ones that have come more from a junior level that have maybe
just come out of a master's haven't quite developed that sort of questioning because it's sort of
what comes from doing a PhD and diving into much more deeper analysis on things that they're
sort of just to get the results.
But then when you sort of dig into like the question and say, why is that the answer?
How did the model perform?
How does it work?
They're sort of as unsure about that.
And I think that's something that I've sort of been trained to sort of keep questioning during
my PhD, which I then brought to tech.
Oh yeah, super.
Esley?
Yeah, I think it's the same for me.
I think my PhD has really helped me to think things through and to question myself and to
check, to double check and check again and to test my work really.
Whether I implement it in Python or even doing some analysis, I just, it's, I think it's,
it's really helped me in that way.
Because I think when I did my PhD, one thing I learned was just because you have some, you've
done some mathematical analysis doesn't mean it's totally right.
It's good to prove it by doing some programming as well.
You know, it's much more, you know, it just makes it a lot more fun.
And also you're able to show what you've proved mathematically.
You're able to show it as a graph, for example, you can, so people can actually visualize the
maths behind it.
Even if they don't quite understand the maths, as soon as they see graphs that they get a
bit happy because yeah, that's something I'm really grateful to have learned whilst doing
a PhD.
Yeah, absolutely.
It does really make it much more concrete because maths can be so abstract and out there.
And once you either make a program that can say, do a recommendation or it can generate a picture,
all of a sudden it's like, oh, look, it's, it's kind of real.
It, it sort of exists at least visually.
Yeah.
So, all right.
So here's like one of the big questions I think of this conversation.
You all talked about your, your background and academics and studying and so on.
And you're all working in the tech industry, not doing biology or statistics or complex analysis
or whatever, you know, it is in the math world that you thought you might've been doing.
So what was the thinking there?
I could tell you when I sort of dropped out of my PhD program, which I did halfway through,
it was a big decision and it like kind of weighed on me for a while.
It doesn't anymore.
I'm, I'm all great with it, but best decision ever.
But you know, what was your thinking?
You know, whoever wants to jump in first, go for it.
I think I always sort of knew that I would probably end up in industry, particularly
like sort of halfway through my PhD.
So because the way our doctorate training center was sort of set up, it was set up to work with
industrial partners.
So we had a lot of our PhD projects were with industrial funding.
So I did mine with a company, DSTL, which is the Fern Science and Technology Laboratory.
And like within my PhD cohort, there was 10 people in my year and everyone sort of had a different industrial partner.
And within that, they would, the industrial partners would come and present to us.
We would have like regular problem solving days where they would come in in the morning.
It's sort of like a mini hackathon where we'd get to work on practical problems.
That sounds so cool.
I think really useful, right?
Not just like you're going to study the theory and then you're on your own.
That's great.
Yeah.
So it was like a really applied PhD.
So even though we were spending time researching and doing our own work, the doctorate training center also put on all these other events and sort of things like conferences where there'd be half academic speakers and half industrial speakers.
And I think I was always more interested to go to the industrial talks so that people like using tech in the industry because they're more applied.
I could see the reasons behind doing them.
I think academia, you spend a long time sort of trying to develop novel areas and trying to prove them like asymptotically or whatever.
Whereas I think I was much more of a, I know the tool set or I'd like to go and research what tool set to use and then apply it.
So I think it was always sort of more than the applied sort of side.
Yeah.
That's a big difference, right?
You don't get credit for just necessarily finding interesting uses of existing technology and academics.
It's always about what new problem are you, what new question are you answering, right?
So that's a big difference.
Yeah.
And I feel like it's always very small incremental changes that you might make and someone's done it that like there's like years and years of research and you might be changing.
I'm not doing a parameter or doing something quite small.
I also think that academia is a bit sort of outdated in a sense.
So it's a bit slow.
So like the time you publish papers and then it goes through like all your reviewers and then you do all your edits and then you eventually get your publication like submitted and published.
That could have taken like two years.
Whereas in the industry, you could have your project in sort of developed like productionized thing ready in a few months and then value generated from it.
Yeah.
It's a huge difference.
Yeah, for sure.
Esaleen.
Well, when I finished my PhD, I didn't.
Well, just before I finished, I didn't know what to do because I enjoyed working in academia.
I was working whilst doing my PhD.
I was also helping undergrad students with their maths problems.
So I enjoyed talking to people, explaining and just interacting with students.
I really enjoyed it.
So when I finished my PhD, I applied in academia and I also applied in the tech industry.
So I just said to myself, the first job that comes along is the one that I'll go for.
So I first worked as a Python developer.
I think that was really good.
I worked for a year and that was a great opportunity for me to really improve my Python skills because I was using Python on a daily basis.
But I was missing my maths because it was made.
Yeah, sure.
So I really missed it.
So, yeah, that's why I started applying for data scientist roles and I did get one.
But I think for me, I enjoyed, I mean, I am in tech industry and I really enjoy what I'm doing.
I feel like I'm doing everything I've always wanted to do because I do enjoy programming and I also enjoy maths.
So having both just makes me happy.
At the same time, in terms of interaction, I do interact with my peers.
So I'm not really missing out.
But yeah, before I finished my PhD, I didn't know what I wanted to do.
Mainly because I was wondering whether the level of interaction in an industry will be different to actually lecturing.
But now that I do know, it's not really different.
Yeah.
One of the things I thought I would miss the most, that I thought I was giving up, was that teaching side of things.
Because I was teaching, in grad school, I was teaching all the levels of calculus and linear algebra and those types of things.
And I really enjoyed that probably more than actually studying math directly.
And I thought, okay, well, if I'm going to programming, I'm definitely giving that up.
And that seemed like a huge thing.
And then it turned out that I ended up doing professional development training stuff later on.
And I kind of got it back anyway.
It's not as big of a trade-off, I guess.
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Jennifer, how did you...
It sounded like you were working a little more close to where your degree was,
you know, when you said working in Baltimore and so on,
before you moved over to what you're doing now.
Yeah.
So I did six years postdoc after my PhD, and that was just more MRI.
It was brilliant.
That's how I got into coding.
And I liked...
I really liked...
So yeah, it's still academia, but in, I guess, the government research space.
Yeah.
So it's still not industry.
But yeah, the analysis was great.
Experiments, brilliant.
Like, loved all that stuff.
But I'd always be attracted to the sparkly new technology.
Like, there's this new technique.
And I'd think, oh, I want to do that.
But it's like, no, no, no, that doesn't answer this.
You got to...
It's the question first.
And then you pick the right approach.
And that's still true, even in data science industry.
But it's a little bit easier to handle now.
Also, I'm aware of it now.
So I know when to stop myself.
But yeah, projects were really long.
So one experiment from beginning to end of potentially having finished the paper would
be like 18 months, really long experiments.
So I don't have a very long attention span.
So that's not great for me.
Also, once I've done the experiment, I've got the answer that I was looking for.
But then you've got to write out the paper.
But I'm interested in something else now.
You're kind of done with it.
But there's all this work you've got to go through and peer review and revise and resubmit,
all that.
You've got to write introductions and stuff and justify what I did.
And yeah, it's a really long process.
And yeah, I want to do the next thing by then.
Also, there's not really much feedback.
You know, you submit the paper and it's so niche.
And then you get nothing back.
Like what did what I do change anything?
Did it progress anything?
You might get some references or citations later.
But it'll be, you know, a year or two, you might get five citations.
And I would just, did that mean anything?
And those citations might not even be positive about your paper.
They might be saying this paper, this experiment was bad.
And we're going to do it differently.
We're trying to just prove it.
Yeah, yeah, exactly.
So it's very unhappy all around.
So I didn't know going into industry would solve these problems at the time.
But I feel like, at least in the companies I've worked for so far, they've both been very
small companies.
The projects are much more shorter.
They're more defined.
They worked in smaller increments.
You get feedback quicker and directly with the people that you're doing it for.
So that's really cool.
And there's no papers to write.
Yeah.
I do think there's the opportunity to iterate.
Y'all have touched on this, that you iterate on something.
You can go from idea to making a difference or making an impact in a month or two instead
of, did you even make a difference?
The thing that really drove me crazy about the academic side was once you really get
to the point where you're doing official academic stuff, so much, so often it's extremely limited.
You know, there's 20 people in the world who potentially could care and half of them don't.
You know, it's pretty, it depends on the area, but it can be very, very small.
I remember.
And you can write software that isn't used by millions of people or makes huge differences.
And it's a big contrast.
And also like in industry or like in programming, there's a lot more like open source.
So you can get your function out there and people can use your function.
Whereas it can be quite difficult to actually access papers.
So even if you then publish a paper in the journal, unless you also work in academia, you
can't really access papers unless you pay lots of money or find someone that can access it
for you.
Does that seem crazy to you that so much of this research is government funded, publicly funded,
but is locked up behind these extremely expensive journals?
Yes.
Drives me nuts.
They get paid twice because you've got to pay to submit your paper and they've got to
pay to read it.
And it's peer reviewed by academics who are doing it for free.
Yes, exactly.
It's ridiculous.
People who work on it.
I know some people in the journal get paid and do work, but the reviewers, the, yeah, it's
like part of their commitment to being an academic.
Yeah.
I have feelings about that.
Yeah.
That's another episode.
Interesting.
So that's really interesting.
You know, when I made the switch, it just seemed like a lot of what you were all saying really
was part of it.
I felt like I wasn't going to make that big of an impact.
I felt like what I was doing was actually much more interesting.
I also thought the employment story was really challenging in the academic space for me.
You know, just talking to the people who were graduating a year or two ahead of me, how many
applications they had.
Like I applied for 105 positions.
I got five responses, two interviews, and one postdoc offer for one year that I can reapply
for.
Like, whoa, whoa, whoa.
This is like a good university.
You got your PhD.
There should be more than one offer in the entire realm of what you can apply for.
So I'm just like, wait a minute, wait a minute, wait.
This is a little bit crazy.
So yeah, there's definitely a lot of benefits.
One thing I do want to ask you all is what advice would you give back to people who are,
say, in academics now?
Not necessarily like, oh, you should move on because whatever, because there's a lot of good
stuff happening in academics as well.
But what are some of the data science techniques or programming tips or career advice would you
give to people who are maybe getting their PhD or master's degree or something like that?
I think I'd say to try, if possible, to get a little bit of experience in working in industry.
So whether that's trying to find like an internship or fellowship, I know some sort of PhD courses
or master's courses actually offer that, where you can take like three months, maybe sabbatical
from your PhD and go try doing something in industry.
Or there's, I think in the UK, there's like KTPs, which are knowledge transfer partnerships.
I guess there might be similar things over in the States, just to get that sort of experience
of trying both academia and research and industry, but also to speak to people in industry.
Because I think there's like data science is such a wide spectrum of a career that there's
different parts to data science that might interest different people.
And someone that's sort of spent a long time in academia and has possibly gone through and
done that few postdocs might find themselves wanting to go into more of a research post
in industry where they could get sort of that hybrid of still doing research for a bit, but
doing it in industry, which would then give them sort of like a foot in the door.
And then they might realize then that either they want to stay sort of more research or they
might think actually the apply time is quite fun.
Yeah, that's a good point.
There's a lot of companies that have little research projects going on inside them.
And you could definitely find yourself there.
Esleet?
Yeah, I mean, I would say just be open to new opportunities.
So like, for example, if you're doing your PhD at the time, just make sure you take part
into teaching, for example, just to see whether you'd be comfortable doing it as a long term
career.
And also, again, like Kayla said, you know, just go for at least just work in an industry,
maybe for one month or two months, just to see if you're comfortable.
And yeah, and also just don't worry too much, I would say about the money when you finish
your PhD.
Just make sure you get the right experience and just make sure you also learn new techniques,
new tools, because yeah, I'm sure you get plenty of money.
Yeah, that's a great point.
Because I think the hardest part about getting a job in industry, data science programming
side of things, the hardest part is the first job.
Go from zero to one.
And once you've had that first job for a year or two, all of a sudden, many opportunities
open up to reposition yourself or to get a big raise or move up or whatever.
But getting that right experience is important.
I agree.
Yeah.
Jennifer?
Yeah, I think I most don't have much to add.
So that is really good advice.
But sometimes it can be hard to find people in industry to even talk to.
So I definitely recommend going to meetups.
You can meet all kinds of people who work for all different kinds of companies.
I think a lot of my concerns about working in industry are ones that would be true in big
companies, like really, really big companies that are not so that don't exist or aren't
so apparent in smaller ones.
For example, being siloed into small, very specific kinds of work that might happen in a much bigger
company or stringency to software engineering processes.
Or even just knowing that some data scientists' jobs are more R&D, which might be more similar
to your academic style of working.
And others might be more about data engineering or productionizing data science models, machine
learning models.
And so that's very different kinds of working that might, one of them might suit you better
than the other.
So just knowing that those are out there and what benefits different kinds of work environments
might have would be good to know.
Yeah, there's a really big difference between taking an ML model and then writing an API
to make it go versus researching what parameters you use to predict the likelihood of selling
a home.
Yeah, I mean, working on toy data to get the most accurate model is one thing, but then optimizing
it to work for masses of streaming data on some kind of cloud platform is a completely different
problem.
And you might have to make sacrifices of accuracy for speed or, you know, any kinds of things.
And there are all different kinds of problems.
So yeah, you can meet or even, you might not even want to go to industry, you might want
to work for public service or government.
Right.
That's not necessarily industry per se, but also not academia.
There's a lot out there.
And you can meet all these people at meetups.
I recommend.
I think to add on to that is like meeting people at meetups and trying to get an understanding
of like what the data teams are like within companies, especially if this is going to be
your first job out of academia.
It's probably best that you find a company that already have a data science team of sorts
in the company.
You don't really want to be the first data scientist because then you're not going to
learn from anyone.
And I think as well, like if you've been in academia for years and years and if you've
gone like post-op, maybe some fellowships, and then you decide you want to make that change,
you sort of have to understand that you're no longer the expert.
I think you build up quite a deep expertise in academia that you could all of a sudden become
the expert in this sort of really, really niche field that only other like 20 other people
are part of.
But as soon as you go into industry, you realize that actually there's now a broad spectrum
of data science skills and you're no longer the expert, even if you've got 20 years in
academia.
So you sort of have to take that step back and say, right, I now need to learn this whole
range of other skills.
Yeah.
Unless you identify where you really are interested in, right?
Yeah.
Well, that's all really good advice.
And I want to second the idea of going to meetups and those kinds of events because
the earlier you are in that transition, the harder and the more alone it feels and like
the less guidance I think you feel like you have and you can go meet other people who are
in the same situation or more importantly, maybe they're one year farther down the path,
right?
They've made that step and like, this is what worked for me.
I also want to encourage people to offer to speak at these meetups because I think that
makes a huge, huge difference.
I know when I started speaking at things like code camps and meetups and whatnot, it
gave me a much greater interaction with the community.
I started getting job offers that I didn't know that I wanted, but I'm like, wait a minute,
really?
I could go do this?
It's not nearly as intimidating as it sounds.
And coming from academics, you're used to giving presentations anyway, right?
So if you can find something focused enough, meetups would love to have a 30 minute talk on
something that you've been studying and then now you're doing computationally, right?
Yeah.
I'm sure Kaylee can agree that it's always having Kaylee used to organize our ladies,
right?
Our ladies.
Yeah.
Our ladies, Manchester.
Yeah.
And I currently organize Pi Day in Manchester and yeah, looking for speakers.
We would love for people to just offer us to talk on any of our meetups and be, yeah,
always open.
Yeah.
And I don't want to speak for YouTube, but in general, I think if you were to say, go to an
organizer and say, I would love to talk about this, but could you give me just a little
bit of advice and guidance and feedback just to kind of mentor me of like, what is expected?
Would this resonate?
You know, just a little bit of help for your first presentation.
I'm sure you could get that help, right?
Oh yeah, definitely.
Yeah.
Actually, we were thinking about having a meetup event.
I don't think we've nailed down the details yet, but we were thinking of having a session
just like that to try and encourage people to give, to talk because especially depending
on where they are in academia and what kind of, what they're studying, they might not have
as many opportunities to give presentations.
So they might not be as experienced.
Sure.
Yeah.
No, I think that's great.
I think in data science, it can be very hard, like very easy to feel a bit like an imposter
because there's so much going on that you think, I don't know enough to speak at these
events.
And what I found recently is actually finding what I did in my, so during my PhD and I did
change points section, I've now done quite a lot of talks on that because I found that
when I do do talks, it's something that I know about, but it's also something that other
people don't know about.
So then they like to come and ask questions.
So I think even though you might think your topic is only small or isn't as broad as some
of the other data science topics, once you start to like present it and get more comfortable
presenting something, you know, it then makes it easier to present on something you don't
know as much about, but you're sort of trying to learn about.
And it's easy to think everyone knows more than me.
When I go on the internet, everyone seems so smart.
I have no idea.
But then in practice, you know, you go and you give one of these presentations and you
might ask like, all right, who has a lot of experience doing this thing I'm about to talk
about?
And you're thinking everyone's going to put their hand up and like two hands out of 20
go up.
You're like, oh, wait, these people don't have a lot of experience.
I'm going to be able to teach them something.
And I think that's actually really surprising and great.
Yeah.
I actually think it works the other way because even if you presented something that there was
an expert in the room, that's probably a good thing as well, because they can then give you
the feedback that you might not have got from anyone else.
And then actually like mentor you or train you or welcome you into their team and say,
we're actually doing something like this.
Come and join us.
Yeah.
But anyone else, but I find giving a presentation helps me learn more about what I'm talking about
as well.
Like I might, I might think I understand, but it's not until you try to explain it that
you, you realize there's holes in your understanding and then you have to go and sort of fill
in those holes.
Oh, absolutely.
I think that getting some concept put together so you can present it clearly, you've got
to figure out what is the essence of this thing.
You've got to distill it down.
And also you have to just have a different way of thinking about it.
And I think the PhD research side of the world works really well here because if you're going
to use some technology in a program, you just have to get some way to work.
There might be three ways you could solve this problem.
If one of them works, you're done, right?
Like the problem solved.
But if you're going to talk about it, you have to say, well, there's these three ways
and in this situation, you use this one, but that situation is that one.
And here's the trade-offs.
And like that kind of deeper level of research, I think it really matches well with those kinds
of presentations.
I think one of my goals this year was to actually do a talk, but obviously coronavirus had to
talk.
Yeah.
So that is so unfortunate.
Yeah.
So what were you going to talk on?
What were you thinking?
Well, actually I hadn't decided, but I just wanted to pick anything, even if I wasn't good
at it, just to pick one topic and just do some research, learn about it and then do a presentation
and then talk to, for example, Jennifer, because she has more experience.
She's done so many talks at those meetups and, you know, talk to someone who's more experienced
and show her, for example, my presentation and just, yeah, to get some feedback.
I love feedback.
Yeah.
And then to go out there and give a talk.
Yeah.
So that was one of my goals.
I don't know if it's going to happen, but fingers crossed.
Let's hope all this just, yeah.
Yeah, I hope so.
There are some virtual meetups happening.
So I'm talking at a virtual meetup on Thursday.
So Her Plus Data, which is the event that inspired this podcast, actually got cancelled a few
weeks ago because of coronavirus, but they've moved it to a virtual meeting on Thursday.
I don't know if that makes it better or worse, like speaking to a screen where you can't
get feedback from anyone.
But if they're going to do another virtual one, you could like, if that interested you,
you can try and get on the speaker lineup for the one after.
Oh, thank you.
Yeah.
Cheers.
Yeah, absolutely.
I don't know if it makes it easier or better.
I've done a lot of in-person presentations, but also virtual ones.
And they both have their own challenges, right?
Like if you stand in front of a crowd of, you know, 500 people, that's intimidating.
But also if you just stare at your screen and you have no feedback and you don't even know
for sure if your audio and video is cutting out and they're even hearing you, like that's
also really, it could be distracting and hard.
So yeah, it's different challenges, but I'm glad you're still getting to speak.
That's great.
Yeah.
I mean, Pi Data Manchester started, we had our first streamed, live streamed panel event a
couple of weeks ago.
I think we had everyone on a hangout that there were three panelists and the person asking
them the questions on a hangout.
And then that was streamed to YouTube live.
And I think that might've been odd for the panelists as well, because they didn't have,
as you said, they didn't have any feedback.
They could only see the other panelists.
They could only see who they're directly talking to.
They didn't, they didn't have YouTube open and have access to like how many viewers it currently
had or what comments were going down the side or whatnot.
So yeah, it must be quite odd.
Yeah.
It's a challenge.
You want to give a quick shout out to Pi Data Manchester.
You all seem like you're involved in that.
And it's, if you're doing virtual stuff, people could attend from all over even.
Yes.
Let us know.
Eslyn as well, of course.
Maybe you'll get two or more speaking opportunities this year, which would be fabulous.
Yeah.
Pi Data.
That would be great.
Yeah.
We got monthly meetups and a monthly code night where you can bring a project and get help
and give help.
And we also do monthly podcasts.
Super.
Yeah.
You'll have to give me the links to put in the show notes so people can check it out.
Cool.
All right.
Well, ladies, we're getting close on time.
So I think we'll have to leave it there.
But it's really interesting to get a look at how you went through your academic side of
things and then moved over and what you're doing now and that perspective.
It's been really, really good.
But before you get out of here, you've got to answer the two quick questions at the end,
which are if you're going to do some data science, write some Python code, what editor
do you use?
Jennifer, go first.
I jump around between three depending on what I'm working on or how I'm working.
Atom, Spider, and Jupyter.
Notebooks.
Okay.
JupyterLab.
Yeah, right on.
Yeah, sure.
That's the next one, right?
Latest.
Esleen?
Yeah, I use Atom, Spider, and Jupyter as well.
Yeah, those are great.
Kaylee?
So I mostly use R, so I'm an RStudio person because I think that's an editor for R.
But when I'm trying to do Python, I'm using VS Code at the moment.
Cool.
Yeah.
We're doing neat stuff there.
I've heard good things about VS Code, though.
Yeah.
I really like it.
I tried using Jupyter Notebooks, but I'm not a huge fan of Notebooks.
So it seems to work well.
Yeah.
They've got some really good plugins.
Yeah.
I don't think I've fully experienced everything it can do, but I think I've probably set it
up to run too much like RStudio, so I'm probably not doing things up Python-y way.
There's so many options in all those extensions there.
You can go, everyone can have a different experience with the same thing.
Yeah.
Pretty much.
Yeah, for sure.
All right.
And then maybe one of you could throw out a notable Python package that, or a couple of
you, whoever wants to, notable PyPI package that you've
recently used that was awesome or worth telling people about.
I'd say Vader Sentiment.
Vader Sentiment, which is an analysis tool specifically for Twitter tweets.
So it's NLP, but takes into account how people tweet, which is not like how people officially
write.
Right.
Because you couldn't feed it like an article.
Train it on an article and then feed it a tweet of like, well, this person's really frustrated
because they are really terse.
No, that's the limit.
If it's a word written in all caps, it takes that into account to weight the score or how
many exclamation points are used or I think also emoticons or emojis.
So it takes into account, it sort of understands tweet speak as it were.
It's Vader Sentiment, huh?
Yeah.
Vader, like Darth Vader.
Vader Sentiment.
Oh, Vader.
Yeah.
Vader Sentiment.
It's a GitHub project.
I'm not sure if it's PIPA.
Yeah.
I see.
Yeah.
It's on GitHub.
I'll put it on.
I'll put it in the show notes.
Very cool.
Yeah.
I was going to mention Sublime Text.
So that's more like a sophisticated text editor for code, markup and prose.
And also DataGrip.
So DataGrip is, that provides context sensitive code completion.
So it's good for SQL queries.
And yeah.
So it's...
Yeah.
DataGrip is super cool.
It blows me away.
You can do a query and it just understands all the different structures.
And then it's also built into PyCharm.
And in PyCharm, if you write a string in a Python program, it'll autocomplete inside the
string, the stuff out of the database, which is less like crazy.
So anyway, it's a good time to be programming.
The tools these days and the libraries are so awesome.
Yeah.
All right.
Well, that's it for our show, ladies.
Thank you for being here.
It's really been fun to talk to you.
Thank you so much.
Yeah.
Thank you.
It's fun really, right?
Yeah.
Yep.
Cheers.
Bye.
Cheers.
This has been another episode of Talk Python to Me.
Our guests on this episode were Jennifer Stark, Kaylee Haynes, and Asleen Bikamu.
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