SDS PODCAST

EPISODE 297:

FORTITUDE &

PASSION

IN THE DATA

SCIENCE

JOURNEY

Kirill Eremenko: This is episode number 297 with Data Scientist

Ayodele Odubela.

Kirill Eremenko: Welcome to the SuperDataScience podcast. My name

is Kirill Eremenko, Data Science Coach and Lifestyle

Entrepreneur. And each week we bring you inspiring

people and ideas to help you build your successful

career in data science. Thanks for being here today

and now let's make the complex simple.

Kirill Eremenko: This episode is brought to you by our very own data

science conference, DataScienceGO 2019. There are

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there.

Kirill Eremenko: Welcome back to the SuperDataScience podcast.

Ladies and gentlemen, what an episode. What an

episode I have prepared for you today. Ayodele

Odubela is one of our speakers for DataScienceGO this

year in San Diego and I literally just got off the phone

with her from recording this podcast and this episode

is going to blow your mind. Ayodele came into data

science just over two years ago from a nontechnical

background and the amount of success, the amount of

projects that she's done, the amount of things that

she's learned and already given back to the community

is going to be super inspiring, for me it was super

inspiring to hear about.

Kirill Eremenko: You're going to hear about things like how and why

Ayodele chose to do a Masters in Data Science and, a

full time masters for two years and how she's

supplemented that with online education and why.

Finding messy data on purpose in order to learn how

to deal with messy data. We'll talk about self-

discovery, fortitude and passion. You'll hear about

some of the projects that Ayodele has worked on such

as using SVM to support vector machines for detecting

poisonous versus edible mushrooms, using random

forests and decision trees for ranking wines based on

the chemical contents, using the Naive Bayes to detect

spam and real world project that she's actually worked

on, written a conference paper on, bullet stopping

flying drones.

Kirill Eremenko: Yes you heard that right. Bullet stopping flying drones,

and you will find out what role machine learning

played in that. What Ayodele did with that and how

they're going to be applied in society once they get

rolled out. Also you will learn how she got one of her

data science jobs through Twitter and how you can

replicate the same success, how you can expose

yourself on different platforms to get hired basically. In

fact, you will also learn that they're currently hiring at

MINDBODY, the company where she works and you'll

learn more details about that role. And many, many

more things from soft skills to her presentation at

DataScienceGO and lots and lots of other things. So a

very inspiring podcast. I can’t wait for you to check it

out. So without further ado, I bring to you data

scientist, Ayodele Odubela.

Kirill Eremenko: Welcome back to this SuperDataScience podcast.

Today I've got a very special guest joining us for the

show. Ayodele Odubela. Ayodele how are you going?

Ayodele Odubela: I'm doing well. How are you Kirill?

Kirill Eremenko: I'm very well too and very excited as well to have you

on the show. You are local to San Diego, which I was

very surprised to learn just now.

Ayodele Odubela: Yes. So I've actually only been in San Diego the past

six months, but it's been a really nice change from

living in pretty cold Denver, so it’s beautiful.

Kirill Eremenko: Wow, that's good. I love San Diego. It’s the weather, it’s

amazing there all the time. That's so nice. How long

did you spend in Denver?

Ayodele Odubela: I was there for three years, so I was just starting to get

used to the cold and then I got spoiled a little bit.

Kirill Eremenko: What happened? Why did you move?

Ayodele Odubela: I actually landed in a new position, so I started with

MINDBODY in March and that's when we moved out to

San Diego.

Kirill Eremenko: Oh, congrats that's really exciting. I must say your

LinkedIn is super interesting with the different projects

and different roles that you've been in and you

currently are in. When I read it, I was really pumped to

see what will come out of this podcast. So it's going to

be really fun I think.

Ayodele Odubela: Yeah, I think so too.

Kirill Eremenko: To get started, tell me a bit more about this ... We

chatted about this just now before the podcast, but I'd

love to learn more. So you have a podcast of your own

and it's about hockey. Out of all things, you chose to

do a podcast on hockey. Tell us about that. How did

you become a hockey fan?

Ayodele Odubela: Yeah, so I actually got dragged to a hockey game my

very first time without really knowing anything about

it previously and of all places, I actually got into it in

Texas. And to a minor league game and fell in love

once I figured out where the puck was moving. It was

just so much faster sport than football, which I had

gotten really accustomed to. So after a couple of years

of being really interested in this sport, I started talking

to a lot of other people, meeting other hockey fans and

my boyfriend actually is the cohost of the podcast with

me and we end up talking a little bit stats, a little bit

about trades and contracts. And a lot of the other fun

drama that's really involved in national hockey in the

US. So it came out of nowhere, but we definitely tend

to just talk about, make a couple of jokes about

[inaudible 00:08:02], analyze a little bit about what's

going on.

Kirill Eremenko: That's so cool. That's such an exciting thing. So what's

the podcast called in case we have some hockey fans

in the audience as well?

Ayodele Odubela: Yeah, it's called the Offensive Zone podcast.

Kirill Eremenko: The Offensive Zone podcast. Okay. Very cool. And how

long have you been doing that for?

Ayodele Odubela: That's actually about two years. We are about to start

our third season, which is very crazy to me because we

started it on a whim.

Kirill Eremenko: Wow, that's cool. Exciting time. Well congrats. Hope

that keeps going really well. That sounds like a cool

passion project and completely unrelated to data

science then.

Ayodele Odubela: Yup. I have done a couple of hockey projects doing

some goal prediction. I'm really interested in looking at

computer vision for trying to predict goals. Maybe the

previous three seconds of the video or however long

trying to get an understanding there of what leads up

to a goal or what might lead up to a miss.

Kirill Eremenko: Okay. Very interesting project. Maybe eventually you'll

be able to predict the game in advance, the scores or

something like that. That'd be valuable. I guess. I don't

know much about hockey, but it sounds like a really

cool area to be in. Okay. So let's talk about data

science then. You mentioned that you moved from a

nontechnical background into data science, right? It

sounds like you love these things where there's some

uncertainty. Oh, hockey. Okay. I'll go into that. Oh,

data science. I'll go into that. So what's the story

there? What were you in and how and why did you get

into data science?

Ayodele Odubela: Yeah, so I was actually working in marketing and

specifically social media marketing. In my undergrad I

studied media professional communications and was

working for a marketing agency when they had a little

bit of an opening on the PPC side, so understanding

paid media campaigns, that introduced me to the

world of impressions and click through rates just as

digital media was really starting to rise. And a couple

of years after doing that, I ended up working for an

app company, and I was putting on a lot of AB testing,

so their push notifications, this was one we had just

found out that using emojis would get people to click

into those. I was doing AB testing on in-app

notifications as well.

Ayodele Odubela: And as soon as that startup really started to go under,

I actually decided to go back to school for my Masters

in Data Science. This was 2016 when that was just

starting to get hot and I started to notice an overlap in

my skills and what I was really starting to do at work

and how my digital role seemed to be more about

analytics. The more I wanted to progress in the roles.

They were looking for people who are able to analyze

the results more so than create the content. So that's

when I switched into data science.

Kirill Eremenko: Okay. Well, so you looked around for roles and saw

where the market is moving and decided to follow the

market.

Ayodele Odubela: Mm-hmm (affirmative). Pretty much.

Kirill Eremenko: Okay. That's very cool. You said you started your data

science in 2016, is that right?

Ayodele Odubela: Mm-hmm (affirmative)

Kirill Eremenko: And also you told me you finished it like just this year,

is that correct?

Ayodele Odubela: I finished it last December.

Kirill Eremenko: Last year, last December. So what is it like a two years

master, oh no? Yeah, two year masters?

Ayodele Odubela: Two year program.

Kirill Eremenko: Okay. Full time?

Ayodele Odubela: Yes. So I probably would not recommend this to other

people, but I was working full time and going to school

full time in data science and it was tough but I

survived.

Kirill Eremenko: Wow. Yeah, that's the drive to be successful to learn

new things. That's really cool. Usually people take part

time if they have work. What made you take the

decision to do full time data science?

Ayodele Odubela: I wanted to get it done quick, so at least with a degree,

but with work, I had the opportunity to work for a

startup and I was putting in about 60 hours a week,

but I felt like I had a really big impact. I was the only

person that was data knowledgeable on the team. So it

was on my shoulders a lot for fulfilling requests, doing

a lot of the predictive modeling. Anything data related I

felt was on my shoulders. So I had the benefit of

having a lot of impact and I sacrificed sleep.

Kirill Eremenko: Okay. Yeah. Well, all right. And so was it worth it?

Was a Masters in Data Science ... This is a really good

question actually. It's quite a controversial question

now because there's so much online you can learn, so

many things you can learn online. So what were the

advantages of doing an in person ... You said you did it

at Regis University in San Diego, what were the

advantages of doing it in person?

Ayodele Odubela: Yeah, so Regis is actually a really small school in

Denver, Colorado and so some of my classes were in

person, but some I actually was able to take online

and just work on after work. I feel the biggest

advantage for me was having these conversations in

person with people about data science. There's a lot of

really difficult to understand concepts that when you

hear it described in multiple ways from multiple people

and talking through those problems that you run into,

I found that to be the most helpful. I also think for me

getting a Masters degree was really that foot in the

door in a lot of companies. I think that is definitely in

part because I have a nontechnical background. So if

someone were to look at my resume and just look at a

nontechnical bachelor's degree and a marketing and

analyst role, they may not necessarily think I'm

qualified for a data science position. So I think that

really helped get me to the next level. And when I was

applying to jobs, I think that definitely made a big

difference.

Kirill Eremenko: Okay. Just out of curiosity, what is your background

in terms of bachelor's?

Ayodele Odubela: Oh, it's in digital media and communications. So it's

interesting actually. In my undergrad I was computer

science for a year and a half, and I was not in love with

the program, and I ended up switching over to a more

general digital media role, but ended up switching over

to a more general digital media and communications

degree, but that included some courses in web design,

and critical media theory, and a lot of things that I

actually found really helpful to my role now, and since

I work with a lot of marketing and advertising data and

that's in my wheelhouse previously. My undergrad

actually was fairly important to my role and that

communications aspect, and public speaking is

actually a large part of presenting at my body right

now.

Kirill Eremenko: Well those soft skills in data science, right?

Ayodele Odubela: Absolutely.

Kirill Eremenko: That's super important. You know what this is, I've

never encountered this before. I love this about you,

that you are so open to just like, take, pick your things

up and change and change and change. Starting from

you said it was an IT degree at the very start, right?

Ayodele Odubela: It was actually just kind of general studies. So it was

more of that communications in digital media. And I

didn't like the computer science things.

Kirill Eremenko: So you basically got out of computer science, went into

digital media and then digital media communications

doing that in those areas and then back into data

science, which a lot of this actually really related to

computer science. So like I like this about you that

you can very easily, very adaptable, very agile in the

sense of how you think about your career and your

future and psychologically, how does that feel? Does it

stress you out or does it like on the flip side, does it

liberate you in some way?

Ayodele Odubela: I think there's definitely a little bit of both. I have to

give some credit to my past and having to adapt when

moving from city to cities though. Growing up I was

what I like to call a military brat. I went to a couple

different schools for middle school picking up and

moving was just part of life. So I think I learned to be

really adaptable on that side. But even as far as my

career, I think that's definitely been somewhat due to

necessity. When I was working in marketing, I was at a

couple startups that ended up just running out of

runway and having to try and find another role with

short term notice, not really any severance package,

you really start to think, where can I best leverage my

skills that will put me in a place that's a little bit more

financially stable.

Kirill Eremenko: Yeah. Well, fantastic. No, that's a great example. I

think for the people who might be a little bit hesitant,

they might not completely like what they're doing and

things like that. Just being open and to this

uncertainty and seeing where life takes you.

Ayodele Odubela: Absolutely.

Kirill Eremenko: Cool. Okay. So, and with this Masters in Data Science,

another thing you mentioned before the podcast,

which I'd love to touch on, is you supplemented that

with online education. Tell us a bit about that. So how

did you do that and like what ... How does that put

you ahead or why? What was lacking in the actual

data science degree that you were doing?

Ayodele Odubela: I think one of the things that was lacking, my program

was very project based. So each week's homework

essentially would be one part of a larger project that

was built over the course of the semester. I think what

was lacking was that real connection to what real data

might look like. So we had a lot of very packaged,

pretty pre-cleaned data to work with. And one of the

things when I started out, I actually took the like Data

Science A to Z course and I really understood data.

That wasn't something that was really part of the

program that even now, obviously those surveys, that

data scientists spend so much time cleaning data but

there's little things I run into like Excel formatting and

things that aren't necessarily taught to you, but can be

such a time waster when you get to your role if you

don't have experience working with it.

Ayodele Odubela: So that was definitely one of the reasons that I wanted

to supplement my degree just working with things that

are messy and finding Kaggle datasets that were

imperfect and running into errors and trying to work

through those.

Kirill Eremenko: That's really cool. In the Data Science A to Z course,

you're talking about section three, right? Where we

talk specifically about cleaning the data. Did you, like

that was, the way I put that together was I took

everything, all my experience I had back from Deloitte

in terms of, alright, what messy data have I ever

encountered and how ... let's make it as difficult as

possible. So this is the biggest section in the course.

And I knew I'm going to make it brutal. I think actually

I mentioned that at the start of the section. Did you

find it manageable? How did you get through that

part?

Ayodele Odubela: I found it manageable over time but I really enjoyed

the, I loved that it was difficult because I wish

something that people would have told me more before

even getting into data science was how hard it really

is. And when I say that I don't necessarily just mean

technically, but explaining to stake holders that

correlation isn't causation, and you can't say arise in

this one aspect is related to one thing. I think early on,

it's easy to get disillusioned and say I'm going to have

impacts, and I'm going to predict these really cool

things without understanding how hard working with

the actual data is.

Kirill Eremenko: Yeah, no, totally agree. That's really cool. Okay. So

finding messy data and learning how to work with

messy data because projects can be pre-packaged too

neatly in a real master's. Okay. Any other reasons?

Ayodele Odubela: I just wanted to learn as much as I could. I felt like my

program kind of left out some of the really interesting

things. We didn't go very deep in natural language

processing and that was one that I really enjoyed just

researching on my own and doing some of these

supplemental classes. I really enjoy working with text

data.

Kirill Eremenko: Okay. Really cool. Well, that's yeah, text data is quite a

powerful thing. It's great that you're finding these

things that you do. What I keep like reverting to in my,

like thinking about this is that all of this happened in

what, two years that you've been in data science for

two years. Is that right? Or two and a half?

Ayodele Odubela: [inaudible 00:21:56]

Kirill Eremenko: That's crazy. Like you've already accomplished so

much in such a short span of time. Can anybody do

this? Is this available to anyone to become a data

scientist from a nontechnical background in two and a

half years and achieve this level of success?

Ayodele Odubela: I think so. So I don't want to say, oh, there's nothing

special about me, but I think it was a little bit of just

hard work and a genuine interest. So data science may

not be the right thing for everyone. I thought that

software engineering was going to be my thing and I

realized it was not. I like the analysis and I don't so

much like the debugging and building a product and

really thinking about things on that aspect. I think

data science was the right intersection of what I was

doing.

Ayodele Odubela: I was able to use some of my marketing experience

and have a company take a chance on me because I

had marketing experience, and I was analytical

enough. But I don't think it's something that can't be

learned in two and a half years. For someone to get a

job in this field I really think it takes that just

highlighting where you're unique and where your

passions actually lie. And I think it's that self-

discovery, I don't want to say, oh, if you're not built for

this then you shouldn't do it. Even realizing now in my

job, I'm a people pleaser and it's difficult to be in data

science and try to please everyone. So it just takes a

little bit of fortitude and some passion, I think.

Kirill Eremenko: Yes, that's really good. Fortitude and passion, that

should be in the title of this podcast or something in

the notes. Fortitude and passion. Okay. Very cool. So

let's talk a bit about some sample projects. You have

some very exciting things mentioned on LinkedIn. Is

that okay if we jump to that now?

Ayodele Odubela: Yeah, absolutely.

Kirill Eremenko: Okay. So first one is this one I found really interesting,

I got to know how you did this. You said you built an

SVM, support vector machine, that resulted in a 97%

accuracy in determining poisonous and edible

mushrooms. Why and how, the questions I have, why

this project and how did you do it?

Ayodele Odubela: Absolutely. So that project actually came about, that

was one from my master's degree. So we were given a

dataset. One of our instructors was really, really

interested in hiking and had an odd fascination with

hunter gatherer lifestyles. So we were looking up

different kinds of edible plants, edible flowers. I landed

on edible mushrooms and essentially the data that I

was able to find had a couple of the different aspects of

the mushrooms, the size, the chemical components as

well as a couple other measurements. How many, I

forget the name of it, the part on the bottom of the cap

of the mushroom, and essentially what I wanted to end

up productionizing was a tool that would use all of

these components from our past knowledge and

combine that in some way with the computer vision

project that will let you scan a mushroom and tell you

if it's edible or not.

Ayodele Odubela: So this was that first step in that project not really

going the computer vision route yet, but trying to

understand what our likelihood is of getting sick if it's

happening to eat a mushroom on our hike. So well

what I found really interesting about this one is that it

didn't take a lot of tuning for those SVM to actually

perform pretty well. And I had a relatively balanced

dataset, so I didn't have to deal with a lot of those

issues. But it was cool because this project was the

first to really make me think about our evaluation

metrics and accuracy obviously being the most

popular, despite the fact that it's a very accurate

model. I don't know if I would trust it. It's something

[inaudible 00:26:34] talking about me [inaudible

00:26:35] and I'm like, “I'm just going to pass on all.”

But I can also see how that same methodology is

applied to medicine and predicting disease.

Ayodele Odubela: And it really got me starting to think about the ethical

aspects of data science and how we are also in the

control of choosing what metrics we want to use,

which is probably uncommon for most people in data

science but if you want to use accuracy or precision or

recall, that's in the hands of the protect practitioners.

So it got me thinking early on about the implications of

that.

Kirill Eremenko: And it also puts into perspective that 97% might

sound good for a business project, but when it comes

to, is this mushroom poisonous or not, I wouldn't like,

3% chance of you're getting super sick or even having

a lethal outcome doesn't sound appealing to me at all.

I would require a 99.97% accuracy at least. Very cool.

Sorry?

Ayodele Odubela: Oh, I'm with you on that. I have not trust that 3%

chance.

Kirill Eremenko: Very interesting. Okay. All right, so next one. This one

I'm a wine fan, not fanatic. I like wine and this one

sounds really exciting. So you created an algorithm to

rank wine quality based on its chemical compounds

using random forest. Tell us a bit about that.

Ayodele Odubela: Yeah, this is definitely one of my favorite projects. I am

also a [wino 00:28:20]. So I had never really given a lot

of thought to what's in wine. I'd never given a lot of

thought to the chemical components. And so this

project came up just out of interest. I was looking at, I

think at the University of California Irvine's data

repository and they have this really nice wine quality

dataset. And essentially red wines are ranked, I know

this sounds weird, but from three to eight based on

how good they are. You look at the sulfates, citric acid

I think is another one of the features, the alcohol

contents another one of the features. So I just wanted

to understand, what makes a wine actually good? We

hear a lot about being a wine connoisseur some time

may be fake or there's a lot of controversy I think

around that.

Ayodele Odubela: So it was cool to quantify it and so I ended up creating

these random forests, but I wanted to have a better

understanding of why certain decisions were made. So

I ended up looking at just a single tree. And within one

tree, what I was able to find that pretty much the root

node or the top definition of what makes the wine good

or bad is actually the alcohol content. So this one

surprised me because I didn't think it would matter as

much in wine maybe as compared to like hard liquor

or something. But essentially if the alcohol content is

over 6.5, it ends up being on that seven or eight scale

versus being in one of the lower sections for quality. So

after wine, I think it ended up being folic acid and

sulfates.

Ayodele Odubela: So a lot of chemistry things I haven't really thought

about in a while, if I get the chance to see the alcohol

percentage in a wine bottle now, I'll try and compare it

to one that I like and I'll choose the higher one.

Kirill Eremenko: Okay. That's interesting. Tell me this like normally I

thought normally wine is over 12% anyways, like 12.5,

14 or is this some special wines that go below 6%?

Ayodele Odubela: So I think there are enough that go below 6% that it

was actually that ended up being the root node. So

that leads me to believe in the ones that are higher

than that it's probably the folic acid and the sulfates

that are pretty big features.

Kirill Eremenko: Okay. Wow. Okay, cool projects. So you use that in

your daily life. Have you used the Vivino, the app that

you take a photo of a bottle and it tells you the public

rate or the crowd resource rating for it?

Ayodele Odubela: I actually have not.

Kirill Eremenko: Check it out. It's called Vivino. It's free and you just

take a bottle, or you can go to a shop. What I do is I go

to a shop and I take, you can change the mode from

taking one photo to taking multiple and take five or 10

photos in a row and then just like on the fly, I think it

uses ... It does use computer vision to recognize the

label and then it brings up the ratings. And they're not

like ratings from wine connoisseurs, they're ratings

from normal people like you and me who have drank

the wine and just said, okay, what's the score? What I

think the score is.

Ayodele Odubela: That's awesome. I'll check in that.

Kirill Eremenko: Okay, cool. Okay, so that was project number two and

project number three, you created a spam filter with

98% accuracy using the U phase in R. Was that

another project in your university?

Ayodele Odubela: Yeah, that was actually another university project.

That one kind of was the first foray I had into really

imbalanced datasets. So as you can understand with

spam, it's usually less than 1% of your email may be

spam. And that definitely relates to a lot of the work

that I'm doing now where we're trying to understand

maybe what customer segments, someone might go

into, but 0.002% of customers move. So that got me

understanding what techniques might work within

balance sets and what might not.

Kirill Eremenko: Okay. Well I've heard that Naive Bayes is really

powerful for spam prediction, what would your

comments be there? Like, why is Naive Bayes a good

choice for projects like that or applications like that?

Ayodele Odubela: Yeah. I think part of that is the naive assumption

there because it's a little bit difficult when you're

working with those really, really sparse sets and not

really relying technically too much on prior data. I feel

like Naive Bayes just tends to be a better predictor

when you're looking at imbalanced data.

Kirill Eremenko: Okay. Got you. All right. So those three examples of

projects, there's plenty more that I'm sure you're

working on and different things. But what I'd like to

know is, this would be interesting. So since you got

into data scientist and you've been in a few different

roles tell us a bit about like what was, what's been

your most exciting one so far? Well, of course. Okay.

Let's say this. What do you do at MINDBODY and

before that, what was your most exciting role?

Ayodele Odubela: Sure. So right now I work with a lot of marketing, sales

and customer service data. We're really trying to

understand our consumers better. So I'm on a project

that's about consumer segmentation and how they

move between different segments. I also do a lot of

work just fulfilling data requests for other

departments. A lot of our customers are internal, so

we do a lot of presentations and using some of the soft

skills there. As far as pastorals, I would have to give it

to my job at Astral AR. So there I worked heavily on

machine learning. They are 'drones for good' company.

So the drones they're creating are resistant to firearms

and being shot at or heavy or extreme kinds of

weather. So essentially the drones are trained to

understand what a weapon is on a sensor level.

Ayodele Odubela: So I did a lot of machine learning and understanding

where metallic objects are including firearms, fire

magazines, testing on different kinds of knives and the

drones are supposed to be used in disaster relief

situations or in law enforcement situations where

they're trying to get a better understanding of suspects

and they're able to deploy a drone, understand from a

couple meters away if someone's armed. And we as the

public have the data on whether an officer knows if a

person is armed or not in that moment.

Kirill Eremenko: If you're listening to this podcast and you're thinking it

just took a whole new twist, then you're sitting there

like "Wow, what was just said? What is happening?"

You are not alone. The first time Ayodele told me about

this I also had my jaw dropped and yes, indeed the

company's called Astral AR, they have nothing to do

with astrophysics. I'm not sure how they came up with

name, but indeed you are hearing about bullet

stopping drones and how they detect weapons. Are

these flying drones?

Ayodele Odubela: Yeah. So they are drones capable of-

Kirill Eremenko: Flying bullets stopping drones. And you wrote a

research paper about this. Is that correct? The

conference paper?

Ayodele Odubela: Yeah. So I'm one of the co-writers on the paper. It's

called the Edna Bullet Stopping Drone. So we actually

presented that at the IEEE conference last year for the

global humanitarian. It was the IEEE Global

Humanitarian Technology conference.

Kirill Eremenko: Wow. And you've already worked with law

enforcement, is police going to maybe start deploying

these things sometime soon?

Ayodele Odubela: Yeah, so when I was with Astral, we were working with

the Austin police department and partly in getting

officers trained to know how to use these, know how to

pilot these. They're very specialized systems that you

actually are able to fly with just your thoughts.

Kirill Eremenko: Augmented reality, right?

Ayodele Odubela: Yes. So, and then the other aspect of that was really

doing testing on the drone being able to withstand

firearms and withstand bullets.

Kirill Eremenko: Wow. What is your life? This is crazy.

Ayodele Odubela: I know.

Kirill Eremenko: Okay. And so you used the word computer vision to

detect firearms and stuff like that?

Ayodele Odubela: So the computer vision part was actually a really small

piece. That's actually more to detect anomalies and

threats. So the computer vision behind the drones

essentially is looking for someone in a threatening

stance. So if they may have a weapon in their hand,

but if you're holding your hand outwardly from your

body, there's a couple of different stances that are

picked up and might be anomalies. And a lot of the

work I was doing was using radio frequency sensors.

So similar to the TSA body scanners. And those are

what's actually determining this is a 45 millimeter

weapon versus a Swiss army knife, or this is

someone's credit card versus a rifle.

Kirill Eremenko: Okay. Wow. Oh, that's really exciting. Are you able to

disclose or what's in the paper in terms of what

algorithm did you use for this?

Ayodele Odubela: Yeah, so what's actually in the paper is it covers a lot

of the different components of the drones, so exactly

what it's made of. There's a metallic foam that they

use to actually make it bulletproof. The algorithms, I

can't disclose completely what we use, but we ended

up on using ... I tested a lot of different algorithms

against each other and ended up using a combination

of decision trees and random forests to really get our

final results. But our neural networks actually ended

up performing the best, but in the context if these

drones are actually sold to schools and police

departments, a lot of that data is going to end up being

public knowledge. So we decided that the trade-off in

quote unquote accuracy for an explainable model was

actually better in this case.

Kirill Eremenko: Does it better to do the explainability versus the

accuracy?

Ayodele Odubela: We think about it in the very practical context lest

there are going to be false positives and false negatives

in the context that the drone thinks someone is armed

when they are not actually armed. We want to be able

to say why we don't want to say the chemical

component of item, whatever you're holding is so

much similar to this ... So we really value

explainability.

Kirill Eremenko: Yeah, yeah. Like, so neural network doesn't allow that.

It's the decision trees that have that. Okay. Yeah. This

concern is rising more and more, explainable AI and

what do we do with neural networks, especially in an

application like this where you've got a drone making

decisions whether something is dangerous or not. I

can understand where this decision comes from.

Despite that you were able to achieve a 91% accuracy

rate. Is that right?

Ayodele Odubela: Yeah. So that was really exciting, but I think it's a

really hard problem. It goes back to our poisonous

mushrooms. Do I think 91% and 9% of the time it's

going to say someone isn't armed when they are, it

doesn't cut it. And I feel like that's the hardest part

about solving this kind of problem is as a society

deciding what is good enough. Do we say even though

this isn't perfect, it's better than human judgment

alone? So there's a lot of implications and a lot of

ethical concerns around that.

Kirill Eremenko: And it's an example where humans can work with

these drones, right? The concern here is of course the

mostly in this situation, the false negatives that you

have ... let's say if the drone has 91% accuracy and it

does say that somebody has a weapon, then they

don't. Okay. Double check. No problem. It's better safe

than sorry. But the real problem is out of those 9%

where it says if that person doesn't have a weapon,

you want to make sure to catch the false negatives

when the person does have a weapon. Well you just

have the human double checking those false negatives

and double checking when the cases or looking at, I

don't know how few people would check that, but

when the drone says there's no weapon, okay, does the

human confirm or not?

Ayodele Odubela: Exactly. [crosstalk 00:41:53] that going forward that's

going to be so much more part of any kind of AI

system is we're going to need to have human in the

loop.

Kirill Eremenko: Yeah. Okay. How do you get a job like that? Ayodele

really how, I don't even know where to start. How did

someone get a job like that?

Ayodele Odubela: You would not believe me if I told you I got this job on

Twitter.

Kirill Eremenko: No way. You're joking.

Ayodele Odubela: Yes. So the CEO of the company actually just saw me

tweeting about data science and pretty much a couple

tweets about data science and ethics and she reached

out and we opened up the conversation about a lot of

the machine learning I was doing in school and she

was like "Do you want to work with sensor data?" And

it just happened like that.

Kirill Eremenko: Wow. Congratulations. That is so cool. From now I'm

going to use this as a case study. This is the best.

Because I always tell people, make yourself visible,

don't just sit there and do your projects and like hang

them on your wall. Put them out there, tweet, put

them on LinkedIn, put them on, I don't know, Medium.

Put them on all these places where you can show your

work, expose what you're learning. Doesn't even have

to be ground breaking new stuff. Just like, okay, this

is what I learned. This is the new thing I did. This is

what I'm excited about. Somebody will eventually find

... Companies are looking for good data scientists. That

is inevitable. That's going to just keep growing because

data is growing.

Kirill Eremenko: The problem is that there's an ocean of wannabe data

scientists, of people who say they want to be a data

science. It's just because it pays well, it's a cool trendy

word and things like that. So it's really hard for

companies to pick out of this ocean, the right people.

All you have to do is put your hand up. You only have

to shine a beacon into the sky and somebody will come

and get you.

Ayodele Odubela: Exactly. And I think that really just highlights the

importance of being able to market yourself. I think

that's where a lot of my marketing skills came in and

I'm like "Oh, use these hashtags, this'll be fine." But I

think for a lot of people who are really analytical or

come from academia or a lot of the hard sciences,

being able to brag about your work and brag in a way

that shows how passionate you are about the impact

you get to have with your work is such a key that so

many people unfortunately miss out on.

Kirill Eremenko: I'm reading a book now by Susan Cain called Quiet.

It's about how introverts get along in this world that

currently looks so extroverted. And one of the things

that she mentions is in the start of the 20th century

we moved from a character, like a focus on character,

how you are within your household, within your

community, what values and traits you have. We

moved to a kind of like what you could even call like a

cult of personality that starting from this whole

industrial revolution, not really industrialist, but

basically when sales became more important, people

like Dale Carnegie came along and things like that. It

became more important the personality that you

exhibit and how you get others to perceive you and in

specific, particularly introverts and or more people

that are more closed that are more to them, hold to

themselves, they struggle with that because it's not

normal as you say, to brag about your work or it's not

a natural thing that happens for us.

Kirill Eremenko: Unfortunately that's the world we live in and you got to

get into it. You've got to learn how to show yourself

and show your results so that people find you because

they're not going to find you just through knowing

your brands or through the local community that

you've always grown up in. We don't live in that world

anymore. So it's just something that I think we all

need to get accustomed to.

Ayodele Odubela: Absolutely. And I think one thing that might be helpful

for some of those introverts is to think about it as you

are being as efficient as possible. You can get to the

same place and work so much harder without making

those connections, networking, doing speaking events

and just know that a lot of people who you see that

might be extroverts are probably feeling a lot of those

same fears. I definitely am one of those people where

it's easy to talk one on one, but speaking events, I can

be vulnerable and say that's difficult and I get scared

and I get nervous. But think about it as you can be

more efficient and get over some of those fears or you

can do it the hard way and not really have to challenge

yourself personally.

Kirill Eremenko: Yeah, yeah, totally agree. And speaking of speaking

events, you're coming to DataScienceGO and you're

going to be one of our speakers there. Congratulations.

Ayodele Odubela: Yeah. Thank you so much. I am super, super excited.

I'll be talking about fighting bias in AI and trying to

highlight some solutions that help us analyze our

models better and just be more critical about bias that

we don't really think about.

Kirill Eremenko: That's very exciting. So fighting bias. It sounds like it's

... Because we talked about this, a bit about this

before the podcast. It sounds like a passion of yours.

Tell us about that, how... Bias and diversity and

machine learning. How are you working on that? What

is this project?

Ayodele Odubela: Yeah, so one of the aspects there comes from past

examples. I think a lot of people might be familiar with

the Tesla accident that happened I think a year or two

ago in Arizona. I think it was Arizona. Yes. So one

example that I had that shed some light about who

this really impacts is I was giving a talk at the national

society of black engineers last week. And at the

personal development conference I had everyone raise

their hands if they had issues with taking selfies at

night and surrounded by a group of black engineers,

almost everybody raised their hands. And I asked

everyone in the room if they had heard about this

accident and pretty much everyone raised their hands.

And I asked them to think in 25 years if we're going to

see a statistically significant difference in who actually

gets hit at night.

Ayodele Odubela: We are kind of on the understanding right now that

self-driving cars are going to be more and more

frequent. We are going to have to deal with them being

on the roads. And that's one of the things that I

consistently think about that despite the fact that

there are sensors, we know that sensors can fail. We

also know cameras can fail. And I think that's one of

those measurement biases where the actual camera

lens is the source of the hardware in almost every

camera in modern day America or across the world is

based off of the lens that was tuned specifically for

lighter skin. So when the camera was being developed,

they would do paper bag tests, they would take photos

of slaves and usually their child takers with the

children and actually tune the camera lens to the

children's skin color.

Ayodele Odubela: And what we end up with is a flawed hardware

product that is the basis for more flawed hardware

products. So I think in that sense, I do worry and I'm

very concerned that we'll see more people with darker

skin are getting hit at night because we just can't see

them but we didn't intervene early enough to really say

we need to do more research and we need to make

sure that this hardware that has the ability to detect

people doesn't make the wrong decision when it comes

to people of different skin colors and how we differ.

Kirill Eremenko: Okay. And so you're planning on using machine

learning to fix that hardware problem?

Ayodele Odubela: I'm actually thinking about making it a hardware

problem to fix. So part of this, I've actually started

working on this project is collecting a lot more diverse

data. So I'm actually working almost backwards, so

collecting data sets of individual races, but being able

to tune our camera to each of those. So if we have

presets or if we have the things that our hardware aids

for cameras to be able to recognize darker skin people

better I think going forward, these need to be used so

that we don't end up with some of those problems.

Kirill Eremenko: Okay. Okay. Got you. And what will you share in your

talk at DataScienceGO about this problem?

Ayodele Odubela: Yeah. So I'll talk a little bit about what we can do now

and then also a lot about explainable artificial

intelligence and how there are practical solutions that

right now we're able to use to just understand our

models better.

Kirill Eremenko: Okay. All right, very exciting. So you haven't been to

DataScienceGO before, in the past two years. Okay. So

what have you heard about DataScienceGO and what

are you looking forward to?

Ayodele Odubela: I've heard that it's a lot of fun. So I'm really excited to

network with a lot of the other data scientists there.

Just looking at some of the other speakers there, some

people I'm really excited to meet, but I want to talk

with a lot of job seekers. I can imagine that we might

still be hiring at MINDBODY, so really trying to also

get a good idea of the job market in San Diego as well.

Kirill Eremenko: Awesome. Awesome. Very cool. And one thing I think

you will like is that I didn't realize this, but Pablos

Holman who was our keynote speaker last year, he

and this is a person that's got 20 million views on his

Ted talks and he's worked with Bill Gates and we have

quite a reputable person in the space of AI data

science engineering. We were having lunch together

and he pointed this out to me and I looked around, he

was totally right, that the level of diversity at

DataScienceGO is astonishing. It's not something we

were expecting that it just somehow naturally

happened through our audience, through people who

are coming there. But he said that this was one of like

the top conference in the space of data science AI with

like the highest diversity.

Kirill Eremenko: We had people from like 23 different countries come

and from all sorts of nationalities. It's like you, I don’t

know, you find yourself meeting absolutely random

people from all walks of life, from all colors of skin,

genders, from whatever preferences and minorities

from everywhere. And that is so exciting. I think with

your talk will be very actual and relevant to this

audience. So I'm very excited about that.

Ayodele Odubela: Yes, I'm super excited to hear that as well.

Kirill Eremenko: Okay, fantastic. And so let's talk a bit about

MINDBODY because you did mention just now, we

talked about this, or you mentioned this before the

podcast, you are hiring right now. You are hiring a

coworker? So I warned you that because 10,000 people

listen to this podcast as soon as we say something like

you're hiring a coworker, you might get a very large

number of applications. So let's make it easy for

everybody who's listening to understand if this is the

right position for them and if they should indeed send

you their CV. Tell us a bit about what kind of work is

going to be involved and also what kind of, what are

the requirements for the person that you're looking

for?

Ayodele Odubela: Absolutely. So we are looking for someone who is local

or willing to live in San Diego, California. That is

because this data scientist will work very closely with

our consumer marketing team. So that means the

MINDBODY app, our front facing application is the

team that you'd be working with and working on that

consumer product. Some of the skills required for the

role are very basic level of sequel. Being able to pull

data for customers is still really big for us. Also we're

looking for someone who has some marketing

experience. So that's either an understanding of a SAS

company, metrics like lifetime value or click through

rates. Any kind of marketing analytics knowledge

would be really, really helpful for this role.

Ayodele Odubela: And we're really agnostic about programming

languages. If you're an R Python user, we have more

than enough tools to accommodate, but we're looking

for someone who's really passionate any kind of

interest in marketing is really helpful.

Kirill Eremenko: Got you. Okay. Very clear description. And how do

people apply?

Ayodele Odubela: Anybody who is ready to apply can go to

company.mindbodyonline.com/careers and you'll be

able to search for the data scientist role.

Kirill Eremenko: This is it. Let me check that again. So company.mind

body dot what?

Ayodele Odubela: MINDBODY online

Kirill Eremenko: mindbodyonline.com/careers. Okay, let me see. If it

loads for me just to the, okay. So everybody listening.

Company.mindbodyonline - one word - .com/careers is

where you can apply. Okay. Very cool. You even have a

description of your perks. Very cool. Awesome. Okay,

great. And so just to sum up, what is the mission of

MINDBODY so that people can see if they relate to this

mission?

Ayodele Odubela: Yeah. MINDBODY is really about connecting the world

to wellness. So we have a combination of our apps and

tools for fitness studios, hair salons, and masseuses.

They're able to interact with their customers better

and we make it really easy to book an appointment

especially if you're traveling.

Kirill Eremenko: Very cool. Are you the first data scientist there or is it

a big team already?

Ayodele Odubela: No, it's actually a fairly large team. So we actually have

about 40 people right now. But that's broken up

between business intelligence, business insights, data

warehousing and a couple other departments.

Kirill Eremenko: Oh, so it's quite a big company already.

Ayodele Odubela: Yes. So think we're about 1900 people for reference.

Kirill Eremenko: Okay. Awesome. All right, well there we go. If anybody

is looking for a job or knows somebody who is looking

for a job in San Diego, then please refer them. Okay. I

think we've covered on so many topics. Is there

anything that we missed? Like I had a whole huge list

of things to talk about, but things, it looks like we've

gone through almost anything. Is there anything that

you'd like to add to our discussion?

Ayodele Odubela: Yeah. Last bit is just that I am mentoring people right

now who are trying to find roles in data science. So I

want people to feel free to reach out to me on Twitter

or on Instagram as well as I also mentor via the

Sharpest Minds. It's a very specific data science

mentoring platform, but please feel free to reach out. I

knew that something I kind of wish I had early on in

my career.

Kirill Eremenko: That's so nice of you. You're only two and a half years

into data science, but you're already giving back to the

community. That is amazing. I love that. Sometimes

takes people decades to realize that it's all about giving

back. So huge kudos to you and I do hope people

reach out and sounds like you do have a lot things to

share from detecting poisonous mushrooms to bullet

proof flying drones, it's crazy. All right that's awesome.

Ayodele Odubela: That could be an episode on its own.

Kirill Eremenko: Yeah, I can imagine. All right. And so you mentioned

Twitter, Instagram, LinkedIn. I see you have over

almost 4,000 followers. Let's say everybody's that

3,954 followers. Let's tip it over 4,000. Yeah, I like

LinkedIn. You get ... That's one of the best ways I

think to connect because it's got the professional-

Ayodele Odubela: I like LinkedIn and Twitter one and two for sure.

Kirill Eremenko: Yeah, definitely. And you tweet quite often, I am

guessing.

Ayodele Odubela: Yeah, I'm pretty active. I'm probably most active in

tech on Twitter. Some of those things can get reposted

to LinkedIn, but fairly active in those conversations.

Kirill Eremenko: Fantastic. Okay. All right. Well there we go. That's

where people can find you and I guess one question

I'm curious about is what's a book that you've been

reading or you've read that's impacted your life or

career? You have such an interesting story. There has

to be something that's impacted you and I'd love to

know what it is.

Ayodele Odubela: Absolutely. I'm kind of going to use a very technical

career one. it's called Heard In Data Science

Interviews. It's by Kal Mishra and it came out just last

year. So this has I think over 600 different interview

questions with like really detailed answers. So when I

was going through my job hunt this was my Holy grail

and feeling really comfortable in how I answered some

of those data science questions.

Kirill Eremenko: Wow. Okay. Heard In Data Science Interviews, right. I

think I found it on Amazon already. 600, over 650,

most commonly asked interview questions and

answers by Kal Mishra. Okay, fantastic.

Ayodele Odubela: It's great. It goes into some of those really specific

questions too, like an NLP and other subject areas.

Kirill Eremenko: Okay. Very, very cool. So you basically provided a job

opportunity and the solution to how to get it from

podcast. Apply for the job, read the book. Very cool.

Very cool. Sounds like you should write a book of your

own. Like an autobiography or something. This is

crazy.

Ayodele Odubela: It's funny you mentioned that. Not plugging anything I

promise, but I've been thinking about the idea mostly

just geared towards people transitioning into technical

roles because I feel it's important for me to talk about

these things and give back now while it's still fresh in

my mind, the struggles I'm still dealing with. So if

that's helpful to anyone, let me know.

Kirill Eremenko: Please, please do write it and when it's done, come

back on the podcast and we'll talk about it and we'll

tell the world that it's out. And yeah, that is such a

common thing people transitioning from nontech to

the world of data science, I wish more people did that

because it's possible. And examples like yours show

that not just possible you can achieve great success in

a short period of time.

Ayodele Odubela: Thank you for that.

Kirill Eremenko: Thank you. Thank you for coming on the show. So

we've covered so many things. Super excited for your

talk at DataScienceGO for everybody who's going to be

there is going to hear it. It could be a great

continuation of the things that we talked about on this

podcast and I personally look forward to catching up

with you there in real life.

Ayodele Odubela: I do too. Thank you so much. I know that I've used a

lot of your content to help me get ahead and it's

awesome to be able to chat and I'm so honored to be

able to talk at DataScienceGO.

Kirill Eremenko: Fantastic. Thanks Ayodele. Hope you have a great day.

Ayodele Odubela: You too. Thank you.

Kirill Eremenko: Thank you ladies and gentlemen for being part of the

SuperDataScience podcast today. Super pumped for

you to have joined us for this conversation with

Ayodele. I hope you feel as inspired as I am. My

personal favorite takeaway from here has to be the

bullet stopping flying drones. But in terms of valuable

career takeaways, I think the whole notion of just

being flexible and adaptable to changing all the time,

to being accustomed to change. How many times did

Ayodele change in her Masters? How many times did

Ayodele change in her career trajectory? And that is

what has helped her, not just get success but get

success rapidly and something to consider. Where is a

place in your life where you are maybe stuck or you

have fear because you're afraid of change and you

don't know what will happen?

Kirill Eremenko: Well just think of Ayodele’s story and that should

inspire you to embrace change and jump into it, take

the leap and see what happens. So that can be very,

very rewarding sometimes. And on that note, as usual,

you can get all the notes for this episodes at

superdatascience.com/297. That's

superdatascience.com/297. There you'll find the

transcript for this episode and any materials we

mentioned. Also the links and URLs for Ayodele’s

profiles, make sure to connect with her on LinkedIn.

Her Twitter handle is data_bayes. D-A-T-A underscore

B-A-Y-E-S. You can also find it on the show notes, so

make sure to connect with her and her great offer for

mentoring. If you're looking for a mentor then Ayodele

might be a great person to connect with.

Kirill Eremenko: By the way, if you're looking for a mentor, I highly

recommend listening to what Tim Ferris, the famous

writer and podcaster has to say about it so you don't

overwhelm your mentor and that's the best way to

build a relationship with your mentor. Just saying, if

you're going to reach out to someone about

mentorship, first check those things out so that you

can build a good well-grounded, fundamental mentor-

mentee relationship. And finally as mentioned Ayodele

is going to be a speaker at DataScienceGO and we're

going to have lots of exciting speakers like Ayodele. I

would love to personally meet you there. We have

hundreds of people already signed up already coming

for DataScienceGO.

Kirill Eremenko: So if you haven't gotten your tickets yet for

DataScienceGO and you're considering coming or

you'd like to come or you'd like to hear more about

Ayodele, this is one of those times to embrace

uncertainty, take the leap and see what happens.

Jump into it. So the website is

www.datasciencego.com. Even if you have to fly from a

different country, it is worth it. We had people from 23

different countries come last year and we're expecting

even more this year. So come see us in San Diego,

27th, 28th, 29th September. That's already coming up

very, very soon. That is what, when this podcast goes

live, that's two weeks away or a week away, just over a

week away from when this podcast goes live.

Kirill Eremenko: So make sure to get your tickets if you haven't gotten

them yet. This is the last chance to transform your

career and then DataScienceGO is not going to be

available for another year. You'll have to wait until

2020. So don't miss out. Make sure you're there. Make

sure you skyrocket your career and take it to the next

level. Once again, website is datasciencego.com. Get

your tickets. And I'll see you next time, until then,

happy analyzing.