SDS PODCAST

EPISODE 227:

ENHANCING YOUR MOBILE GAMING

EXPERIENCE

WITH DATA SCIENCE

Kirill Eremenko: This is episode number 227 with Data Science

Influencer Sarah Nooravi.

Kirill Eremenko: Welcome to the SuperDataScience Podcast. My name

is Kirill Eremenko, Data Science Coach and Lifestyle

Entrepreneur and each week we bring inspiring people

and ideas to help you build your successful career in

Data Science. Thanks for being here today and now

let's make the complex simple.

Kirill Eremenko: Welcome back to the SuperDataScience Podcast, ladies

and gentlemen, and today I've got a very exciting and

fun and positive episode prepared for you. I just got off

the phone with Sarah Nooravi and I definitely don't

think that I've had this many laughs on an episode of

this podcast before. It was lots of fun and be prepared

for a very, very energetic and positive episode.

Kirill Eremenko: What you need to know about Sarah is that she's a

Data Science Influencer with tens of thousands of

followers on Linkedin and Sarah inspires the Data

Science community through her articles, webinars,

mentorship meetups and many other ways that Sarah

engages in the community. She inspires data scientists

to constantly learn and grow in their careers.

Kirill Eremenko: In this podcast, we talked about three main things.

First of all, Sarah's background and how she got into

the space of Data Science in the first place. Be

prepared for some very peculiar detours here starting

from the world of culinary and becoming a chef and

going all the way to to the world of nuclear fusion.

Then after that, we talked about a specific case study

or a specific use case of Data Science in Sarah's

current role and you'll find out how Data Science can

and is used for marketing of mobile applications. Very

interesting case study and I'm very excited for you

guys to check it out and find out, get a glimpse into

this world.

Kirill Eremenko: Finally in the third part of this podcast we talked

about diversity in Data Science and what we as a

community can do to help inspire everybody regardless

of their gender or ethnicity to be successful data

scientists. There we go, we've got a very exciting

podcast coming up ahead. Can't wait for you to check

it out and without further ado, I bring to you Data

Science influencer Sarah Nooravi.

Kirill Eremenko: Welcome back to the SuperDataScience Podcast, ladies

and gentlemen. Super excited to have you on the show

today and we've got a very special guest joining us

from Irvine, California, Sarah Nooravi. Sarah, how are

you going today?

Sarah Nooravi: Very good, Kirill. Thank you so much for having me.

I'm excited.

Kirill Eremenko: I'm super excited and it was really cool meeting you at

DataScienceGO. We were just chatting about this

before, how we were, I think the first time we bumped

into each other when we were putting those stickers

under the chairs, completely not expecting, I wasn't

even expecting to do that, but yeah. Thanks a lot for

helping out, I think it was a fun night we had, with all

those stickers under the chairs to facilitate the

conference, it was really appreciated.

Sarah Nooravi: Oh, it was a lot of fun.

Kirill Eremenko: Yeah. In a brief recap, I know we chatted about this

just now but just for the sake of our audience, what do

you think of DataScienceGO?

Sarah Nooravi: I think it was a very, very, it was a very well put

together event. I think my initial thoughts were like,

"Wow," like the map that we had in the very, very front

when you come, it's a super impactful moment when

you realize that you're bringing people from all around

the world to come to this event to meet each other, to

network, to be a part of something really big. I think

overall, I want to give it to you for putting together

such a great event.

Kirill Eremenko: Thank you.

Sarah Nooravi: For bringing of the energy, for basically getting people

excited to get into a space that is not that easy, right?

It's not that easy to break into it and so having that

supportive community that's going to help you

whether it's through networking and jobs or whether

it's through resources or support or mentorship,

having that community to lean on is super important.

Props to you for bringing people from all around the

world and creating such a successful event.

Kirill Eremenko: Thank you, thank you and definitely right back at you

because I couldn't have done it without you guys. Like

we had quite a few influencers there and as you

pointed out correctly just before the podcast that we

really leveraged this community that already exists on

Linkedin on data scientists and thank you all so much

for your shout outs and Tarry, also Eric, Randy, Favio,

your shout outs on Linkedin to get everybody excited

about the event. I think in overall it was really cool,

and the diversity, right? That is a part that we're very

proud of, that we had an abnormal for this industry

percentage of women or minorities represent at the

conference. I think that is also to do with the

committee that you guys have built up as influencers

in this space. Once again, thank you for supporting

the event and making it all possible and making it all

happen.

Sarah Nooravi: Of course, of course, thank you.

Kirill Eremenko: All right. Well today we're talking about your journey

in the space of Data Science and your career and what

you've done. I want to start off with something I

noticed on your Linkedin which is really cool and it

sounds to me like it's your personal motto, "Question

everything, answer with data." That is such a powerful

statement. How did that come to be?

Sarah Nooravi: Yeah, actually so I was thinking about it because Eric

had a good one. Shoot, I forget what his was now.

Kirill Eremenko: Eric Weber, right?

Sarah Nooravi: Yeah, Eric Weber had a good one and he kept getting

called out for it. I was like, "Oh, okay, I need to come

up with a good one too."

Kirill Eremenko: I think his is, "I learn everyday."

Sarah Nooravi: "I learn everyday," yes, that's what it is. I was like,

"Okay, so what is it that I do on a daily basis," right?

He learns everyday and I was like, "Wow, that

resonates with me too."

Kirill Eremenko: Just copy it. You should have just copied it.

Sarah Nooravi: Yeah, I was like, "Should I just take it?" I was like,

"What do I do?" I was kind of thinking about it and I

was like, "You know, I question everything. Everything

has to come down to a logical question and answer.

Okay, but why are you doing this? Why is that

happening? Let's get to the root of the problem," right?

At the end of the day, especially in businesses and

even in personal relationships, it comes down to,

"Okay, well historically what has been going on? How

can we answer this with data?"

Sarah Nooravi: I feel like it falls right into my personality in my day to

day and what I love to do is just be inquisitive, be

curious and then don't let people's gut or their

instincts lead what strategy ends up happening or

what decisions get made but let that be based on

something tangible. Data is tangible, actions are

tangible. Yeah, I think it fell right into place and I like

it.

Kirill Eremenko: Gotcha, gotcha. That's a very apt way of putting it and

totally agree, you got to use data to answer all those

questions. Sometimes though, interestingly, I was

speaking to Vitaly, my mentor, and sometimes he says

that even as a consultant he sometimes uses, he relies

on his heart as a separate entity for answering

questions. Sometimes, you can call it gut feel, you can

call it like following your heart, but sometimes even if

the data doesn't align with what his heart is saying,

sometimes he'll follow his heart. What are your

thoughts on that? That's a bit of a controversial

comment there.

Sarah Nooravi: Yeah, that is, especially when you deal with

stakeholders who want the data to only mimic what

they want, what their heart is telling them or what

their gut is telling them. They only feel good or it's like

a reassurance of like, "Oh, well when the data matches

what my gut is saying then, okay, I'm good but when it

doesn't then I'm going to basically argue with you until

it matches what I want." It's a little controversial. I

think that especially on the, let's say from the

analysts' side you have to have a hypothesis of what

you think the data's going to tell you, right? Because

that's how you're going to approach the problem.

Sarah Nooravi: From the stakeholders' standpoint, they're going to

question everything you do and everything you say

until it kind of aligns with what they want it to say,

which is good depending on who you're working with.

It just depends on the scenario, but that one's a hard

one, I think.

Kirill Eremenko: Yeah, yeah.

Sarah Nooravi: Hopefully data is the most objective way that you

answer any question, right, so you would hope that if

the data is vetted and you know where it's coming

from, it's cleaned properly, the way it's being collected

is vetted and then your approach is sound then really,

you should be trusting the data. Or at least you can

modify a little bit of what your gut is telling you to

align.

Sarah Nooravi: You know, it's funny though. We as humans can

convince ourselves of anything, right? Have you heard

of this where the data could be, you could come up

with research or data that tells you one story and

maybe initially you don't agree with it but then you

can rationalize it. "Oh, okay, yes, it's saying this

because of X, Y, Z," but then later discover something

wrong with the data and it tells you, you come to a

180 degree different conclusion. Then you're like, "Oh,

okay, but I also see how that can [inaudible

00:10:39]."

Kirill Eremenko: Yeah, yeah, yeah. I totally know what you mean.

Sarah Nooravi: It's very interesting how us as humans, we can take

what the data's telling us and come up with a story as

to why it is this way or the other way.

Kirill Eremenko: Yeah. I've had that in my life. Kind of similar to the

placebo effect when you're given medicine and you're

told that it will help you with your high blood pressure

or whatever else and in reality it's actually not real

medicine. It's just an empty capsule but your brain

creates a story for itself and convinces itself on a

physiological level even to lower the blood pressure

and what not. Interesting, interesting.

Kirill Eremenko: Okay, well Sarah, tell us for the benefit of our listeners

who don't know you yet, which is probably, I would

say there's a lot of our listeners who do know you.

You're a major influencer in the space of Data Science

with tens of thousands of followers on Linkedin, but

for those of our listeners who haven't met you yet, can

you give us a quick overview? What is it that you do

and how did you get into the space of Data Science?

Sarah Nooravi: Sure. I never know how to answer this question. I like

to start from the very beginning which is maybe too far

back.

Kirill Eremenko: When you were born.

Sarah Nooravi: Because it's interesting. You talk to people and their

journeys into how they ended up where, especially into

this field of Data Sciences, so vastly different. Mine

started actually without even a desire to be in

anything technical. I actually really, really aspired to

be a chef growing up.

Kirill Eremenko: Really? Wow.

Sarah Nooravi: Yeah, I really wanted to go to culinary school.

Kirill Eremenko: You couldn't be further away from being a chef by

being in Data Science.

Sarah Nooravi: It was a really big passion of mine at the time when I

was younger. Once I realized that that was not going to

be the direction I would go I really fell into my love of

mathematics and just logic in general.

Kirill Eremenko: Hold on, hold on, you just skipped a whole, I don't

know, massive part of your life story. When did you

realize that it's not the path you're going to go down?

Sarah Nooravi: Okay, do you want to know the truth?

Kirill Eremenko: Absolutely, always, of course.

Sarah Nooravi: Because there were a few colleges around me that

offered culinary programs and the one, when I realized

what the curriculum had, and this is actually very

interesting when you start talking about your passion

for anything in life, are you willing to do the dirty stuff

before you get to the most exciting stuff? The first

classes that they wanted me to take were about

sanitation. I was like, "What?" I was like, "No, I just

want to start learning how to cook cool things and the

creative side and the artistic side and the different

flavors and this and that." That part of it just

completely turned me off and I was like, "No." I guess I

didn't have anyone around me that was going to push

me in that direction anyway. It was going to be 100%

my own motivation into it and I fell off the cliff right

there.

Kirill Eremenko: Wow, that's crazy. Actually I heard that about chefs. I

read an article once, I think it was about Jiro Ono who

is the top sushi chef in Japan. Basically he or whoever

this article was about, one of the top chefs there about

sushi, when they went to learn to do sushi and they

have this master who is teaching them how to do it,

they weren't actually allowed to touch the rice for, I kid

you not, for 20 years he was not allowed to actually

touch the rice. He had to watch, clean the place, do,

feel and sense everything and now he's the best chef in

Japan with dozens of restaurants and super highly

rated.

Sarah Nooravi: That's when you think about whether someone really,

truly wants to do that, pursue that career or pursue a

certain hobby. You have to really enjoy every aspect of

that job or of that hobby. Even the practicing, even

just sitting around and watching other people do it,

learning from other people's techniques, doing every

aspect of that career or of that job. Yeah, for me that

was the point that I was like, "Okay, moving [inaudible

00:15:22]."

Kirill Eremenko: I love it, I love it. You just had to look at the

curriculum to realize, "Nope, moving on."

Sarah Nooravi: Then at that point, then I started college and I was

like, "What's the common thread of what I enjoy

doing?" It really and honestly when I think about how I

got into really enjoying math, it was through an

English class. In English they teach you how to

logically put together an argument. It's very structured

and it's a logical flow of ideas. Through that logic, A

then B then C, I realized that it's really this underlying

logic that was a passion for me and then I found it

through mathematics. Then I studied math, econ, I

minored in statistics. I mean I'm jumping ahead. I took

a detour into mechanical engineering thinking I was

going to go into the renewable, into the energy sector.

Kirill Eremenko: Oh yeah, as you do, just a casual detour into

mechanical engineering. Wow. This is interesting. All

right, well what made you take the detour into

mechanical engineering?

Sarah Nooravi: When I graduated, I really only saw myself pursuing

one job and it was really odd that I stuck to this one

particular job that I wanted upon graduation, which

was for a company called J-PAL. I was very excited

about their mission. I was excited about what I would

be doing with them. At that point, Data Science wasn't

really hyped up at that point. Maybe a little bit but it

was barely trending upward and so I was looking for

more of like a statistician's job, designing experiments

and helping the world in general. I wanted to have an

impact. When I realized that that job was on the East

Coast, so geographical limitations, I was like, "I'm not

going to move to the East Coast and deal with snow."

Kirill Eremenko: Makes sense. You were always in California, correct?

Sarah Nooravi: California, yeah. Then I decided, "Well, what's the next

best thing?" Because I moved back home and

something about me, I always enjoyed teaching and

tutoring. I took on a tutoring job. I took on a tutoring

job, I moved back home, took on a tutoring job. I was

getting paid almost nothing and I was like, "What am I

doing with my life? What is the next best move for

me?" I found through a class that I happened to take

that I really enjoyed thermodynamics and I loved

physics and I loved, like maybe my way of contributing

would be through something like nuclear fusion just

completely blew my mind.

Kirill Eremenko: Wow, so you went from being a chef to nuclear fusion.

You're a person of extremes, aren't you?

Sarah Nooravi: I mean when you get excited about something it really,

it's that type of excitement that you can have. Like,

"Oh my God, I want to have an impact and I want it to

be in this," right?

Kirill Eremenko: Yeah, yeah. I totally agree. I get excited about nuclear

fusion every morning. I wake up and I'm like, "Nuclear

fusion today, yeah, tomorrow laser physics." I

completely get your point. It's just like the topics you

pick are so out of the blue. Very interesting. Keep

going, I'm having so much fun. This is really cool.

Sarah Nooravi: I mean I'm being totally transparent and honest right

now.

Kirill Eremenko: Thank you.

Sarah Nooravi: I was amazed at the idea of creating a mini sun in your

home, right. Like that was the future of nuclear fusion

and I was like, "Well, how do you make that a

possibility?" I started applying to master's programs in

mechanical, well there's a whole story about how I

ended up finally deciding on mechanical that is just

hilarious but then I wanted to marry it with public

policy. Just because I realized that in engineering, I

didn't end up actually setting public policy but I feel

like someone who has those two skillsets can actually

make a difference because you'll realize that the way

budgets get split for different research projects

especially in government have to do a lot with

understanding public policy and relations. I realized

nuclear fusion stopped getting funded at some point

and I was like, "Well, you have to have both skillsets."

Sarah Nooravi: Anyways, I ended up finishing up my master's.

Kirill Eremenko: Wait, just hold on. Sorry, so you're not going to tell us

that hilarious story about how you chose mechanical

engineering? We're not letting you off the hook here.

Sarah Nooravi: I mean because look, when you get into, when you

realize you want to study engineering, that's part of

the battle. Like, "Okay, now I know I want to study

engineering." Then you realize, so I went to UCLA

campus and I was like, "I want to study engineering!"

They were like, "That's cool, what engineering?" I was

like, "What do you mean? How many engineerings are

there?" They're like, "Well ... "

Kirill Eremenko: I know, right? I didn't know there's like civil,

mechanical.

Sarah Nooravi: Electrical, mechanical.

Kirill Eremenko: Chemical, any kind of thing engineering. Just like put

a noun and then engineering after it, it exists. It's

crazy.

Sarah Nooravi: Yes, exactly. I was like, "Oh, okay, yeah, let me go

back and think about it." Then I was like, "Okay,

maybe it was civil." I didn't know how I decided on

civil, I was like, "Civil." Then I went to the civil

department and I was like, "Oh, so I want to apply to

the master's program here." They were like, "Oh that's

great, what specialization?" I was like, "Excuse me?

What are you talking about?"

Kirill Eremenko: It keeps going.

Sarah Nooravi: At that point I realized I don't need to go the top down

approach, I need to go the bottom up approach. I need

to figure out what exactly am I trying to specialize in,

whose research am I excited about and then I can

decide, I can back out. "Okay, well oh, that was

mechanical the whole time," you know? Because I

found a professor that, I loved her research. It was on

solar powered power plants and renewable energy

storage and I was like, "Okay, this is exciting, I want to

do this." I met her in a parking lot. I talked to her

when she had a flat tire. I was like [crosstalk 00:22:14]

annoying.

Kirill Eremenko: Tell us, Sarah, how did she get a flat tire? Did you

happen to do anything, have to do anything with that

flat tire?

Sarah Nooravi: No, right? At just the right moment. [inaudible

00:22:26], no, but that's the thing, right? If you're

excited and passionate about something, and think

about me. I never had any experience in engineering at

all. Just from my story you can tell how junior I was.

You see this type of, the same thing going on with

people trying to get into Data Science. It's this desire of

like, "Oh my God, I see what I want and then how do I

get there?" You have to be kind of scrappy. Like who

are the right people that you need to connect with and

talk to and show them that you're passionate and meet

them in a parking lot when they have a flat tire and

just go out of your way to make things happen for

yourself. You have to really be ready to put in that type

of effort and be gritty to go after it.

Kirill Eremenko: Gotcha, yeah. Totally agree, totally agree. The best

part is what I love about the way the world works is

when you really like that and you really, truly want

something, things will happen to align in your favor.

Flat tires will happen just at the right time when

you're walking past the car park. Things like that.

Sarah Nooravi: Yeah, yeah.

Kirill Eremenko: Okay, cool. You picked a professor whose research you

liked. I just didn't realize that solar was part of

mechanical engineering.

Sarah Nooravi: It is, yeah.

Kirill Eremenko: Interesting.

Sarah Nooravi: All of the renewable energy type projects fell under

mechanical and so I specialized in heat and mass

transfer which is essentially what all the

thermodynamics is doing.

Kirill Eremenko: Okay, gotcha. You became a researcher at UCLA in the

mechanical engineering space. Is that correct?

Sarah Nooravi: After that, I mean so then here we get to the point of

so many people, of the job market. I now am

graduating and I'm approaching the job market and

I'm like, "Okay, so I have an undergrad that's focused

in economics and math and then I have a graduate

degree in mechanical engineering." I was like, "You

know what I'm going to do, I'm just going to create a

resume for both and the job that I get first will be the

direction I end up going."

Kirill Eremenko: Interesting.

Sarah Nooravi: I left it up to chance and the job market to dictate

where I ended up and fell in love with the culture at a

startup in Hollywood. I just loved the culture, I saw

myself fitting in there, I liked my manager, I liked the

projects that they were working on, the direction the

company was moving. It was very inviting to someone

like me. I don't want to say that it happened by

accident but I didn't go out searching for it. It just

kind of was like leveraging whatever skillsets I had and

then from there, yeah, I don't know.

Kirill Eremenko: Wow, you did such a good job at keeping it, not telling

us. I'm sitting here dying to know which one was it,

was it the mechanical engineer or the mathematics?

Which one did chance pick for you? What was the

startup involved in?

Sarah Nooravi: Yeah, yeah, oh sorry. No, I ended up doing analytics.

Kirill Eremenko: Okay.

Sarah Nooravi: Yeah. I actually never worked a day in my life as a

mechanical engineer. I studied it and I thought, "You

know, maybe eventually it will come in handy." I know

there's a lot of companies right now in energy that are

going towards IOT, all the smart grid and stuff like

that. I think that's what I would have liked to do but I

think emphasizing more on the data side right now

could actually be leveraged eventually into that

industry anyway.

Sarah Nooravi: Yeah, I started working as an analyst, data scientist,

picked up and filled the gaps of all of my knowledge

with the Machine Learning stuff and the Data

Visualization and et cetera, et cetera, and then we get

to where we are now.

Kirill Eremenko: Was it hard to pick up all that knowledge, the Data

Visualization and Machine Learning? How long did

that take you and was it a chore or was it more of an

exciting path?

Sarah Nooravi: That's an interesting question. For me, graduating

with a minor in stats and then I studied economics as

well, they went over a lot of the fundamentals. Your

linear regression, your logistic regression, dealing with

literally every, I took at least two years of really

understanding that stuff very well. Then you go into a

company now that's focused on predictions and

predictive analytics and you realize, "Oh wow, I just

was not prepped for this at all." We didn't learn

Machine Learning as a part of our curriculum.

Sarah Nooravi: Seeing that the company was gearing itself in that

direction, I was like, "Wow, I really have a lot to learn."

My way of learning, and people who know me or

interact with me locally, they know that the way to

learn, at least for me, is to teach. I started teaching

myself all, filling the gaps of all the things I needed to

know and then hosting monthly Machine Learning

meetups in LA where I would just basically talk about

what I learned that month or like a project that I was

working on that maybe people would be interested in

hearing about. I just took that on myself to make

basically, and we go back to Rico, oh Rico. You will

forever be remembered for commit, fail, improve.

Sarah Nooravi: I just by committing myself every month to a meetup

that I had to get in front of people and talk to them

about something Machine Learning related was my

way of just holding myself accountable for learning

and then also integrating my learnings and my

conversations into the projects that I was doing. It

definitely wasn't overnight and I'm still learning, so

what's great about being in this space as well is that

you'll never learn everything.

Kirill Eremenko: Yeah.

Sarah Nooravi: You can get proficient, you can be very good at several

things and then know of many things but you'll never

know everything.

Kirill Eremenko: Yeah. I agree. The way that, I love that approach. As

you said, Rico with his reckless commitment, that is

so cool. To learn something, you commit to hosting or

explaining it at a meetup and that forces you to learn.

Tell us, did that go well every time or were there times

when you found that the challenge was too complex

and you just couldn't possibly learn it on time?

Sarah Nooravi: That's a good question. For me, I always went with

topics that were aligned with projects I was working

on. In the months that I knew I couldn't pull it off, I

delegated. I chose a victim from my company to do the

meetups.

Kirill Eremenko: Nice.

Sarah Nooravi: Then it works out, right? Because I think that

consistency, because what I was trying to do was at

the same time as learn myself was develop a

community of people who could rely on each other and

feel like that they were being supported. I feel like

there was such a demand for it in LA every time that I

held a meetup. I mean the first one I did was in a

coffee shop. It was at Coffee Bean, there was like

maybe 30 people that showed up and I didn't know

anything at that time. I was like, "Oh, let's just pull

up," Sklearn has their nice diagram of all the different

models. I was like, "All right guys, let's just pull up a

model and then go learn it for 30 minutes and then

come back and explain it to everyone." That was my

first meetup.

Kirill Eremenko: Wow. Wow, that's crazy. 30 people sitting there staring

and you're like, "Okay." That's so interesting, wow. You

do this through Meetup.com?

Sarah Nooravi: Yes I do.

Kirill Eremenko: Okay, wow, and you still do it to this day?

Sarah Nooravi: Til this day, yeah. I moved out of LA, and to answer

your question, yeah, I did feel like sometimes it was a

struggle but I think having that commitment, like,

"Oh, 100 or maybe 100 people, 60 to 100 people are

relying on me to follow through on this [inaudible

00:31:39]. I'd better have something good for them."

Kirill Eremenko: Yeah, yeah.

Sarah Nooravi: Once I moved out of LA and I moved to Irvine,

MobilityWare has been very, very gracious with

allowing me to kind of keep that going and providing

us with pizza and a space and just all the

accommodations. It's been very, I've been very

fortunate with not having to worry about a venue in

order to host these and keep them consistent and

build a good community. Next year, I think I might

have help from one of our fellow Data Science

influencers to help me keep the LA chapter and the

Irvine chapter open and expand the meetup and keep

it going.

Kirill Eremenko: Nice, nice. Who's that? Who's the influencer if you

don't mind disclosing?

Sarah Nooravi: It's Randy.

Kirill Eremenko: Randy. Randy's going to, oh, that's awesome. Randy's

great, that's so cool. People love him. That's awesome.

That's so exciting for you. MobilityWare is the

company where you work currently, is that correct?

Sarah Nooravi: Yeah.

Kirill Eremenko: Awesome. Tell us a bit about, what do you do there?

What's your role? Because there's so many different

ways companies use Data Science these days.

Sarah Nooravi: Right now I'm the sole dedicated marketing analyst. I

do everything for our marketing team. My job is a little

bit, it's more than a full time job I would say because I

work across all of our games and we have three

different suites of games. Our card suite, our casino

suite, social casino, and then we have puzzle. Each of

them, it's very interesting, they're all in different stages

of their life cycles so some of them are just starting out

and we're trying to prove whether or not we need to

continue to sustain them and do UA for them or

they're pretty much stable. Like our solitaire game, it's

been basically there for a very long time and so the

marketing strategies around our different games are

very different.

Kirill Eremenko: Sorry, I missed that. These are games for mobile

phones, right?

Sarah Nooravi: Yeah.

Kirill Eremenko: Okay, gotcha.

Sarah Nooravi: My job entails really surfacing data to our marketing

team because before I was here, I think that was a part

of the struggle. I touch every database that we own

and consolidate and really surface that to our teams

so that they can make better decisions, but then aside

from that I'm building out a lot of tools for them. Like

competitive benchmarking tools, creative optimization

tools, different campaign optimization tools which will

all be initiatives that I'll be running next year but I'm

also working on now. Sometimes I get pulled into

things to do with product, so understanding user

behavior, developing user segmentation models. I kind

of get to touch everything which is nice about my role.

Kirill Eremenko: That's cool. You mentioned you're the sole marketing

analyst in the company.

Sarah Nooravi: [crosstalk 00:34:51].

Kirill Eremenko: Yeah, you were mentioning that as well before the

podcast, that you're thinking of expanding the team as

well. Tell us a bit about that. Like when, because I also

went through a similar situation where I, after Deloitte

I joined a company and I was the only data scientist

for a while. I'd be interested to hear your experience.

At what point do you realize that this or the company

realizes that this is beneficial, that there is value in

having a data scientist on board, let's start growing the

team? What are your thoughts on that?

Sarah Nooravi: I really think that it depends on the company and

who's at the top and whether or not they see, the

reason why I say that is because I'm thinking about

two different scenarios. In one scenario, you build out

tools and you basically prove your value through those

tools. It's like, "I can show you that revenue is going

up because of these models that I'm building and the

campaigns that we're doing, the experiments that we're

doing and through basically having data scientists

analyzing the data, building models, et cetera." If it's

very clear, like, "Oh, I can see the revenue lift as a

result of having a data scientist on board," then you

don't really have much argument there.

Sarah Nooravi: In other companies, maybe it's a little bit harder to

justify when there's no real, you can't point to revenue

and say, "Hey, our revenue's increasing because I

exist." From there maybe it's a little bit of a harder

discussion to have but whether you can prove that

through automation or optimizing what domain

experts are doing and helping them do their jobs

better, that's a way to do it. I think on my end, I'll

speak to my current job, the tools that I've build out

for marketing have just been amazing. Their words.

Kirill Eremenko: That's awesome.

Sarah Nooravi: They've really appreciated having someone dedicated

to their needs and especially since we have a lot of

budget allocated towards UA and marketing in general,

the initiatives that I want to run next year are just too

much for me to handle alone. I've kind of pushed for

maybe having someone on my team or having a few

people on my team that we can all work towards

driving better decision making on that side.

Kirill Eremenko: Okay, okay, gotcha. Interesting. It's ultimately up to

the data scientist to show the business value, to make

the case, to make it a no brainer decision for the

business to go ahead, right?

Sarah Nooravi: Yep, yep.

Kirill Eremenko: Okay, okay, makes sense. Cool. Can you give us an

example? It's a very interesting industry, I don't think

anyone on the podcast has been before talking about

games and mobile phones and it's a massive, massive

industry. There's a lot of games popping up all the

time for mobile phones. What is like a recent project

that you are proud of and that you're able to share

with us some details, maybe some tool that you used

or some approach or some, kind of like more industry

specific use case of Data Science that you can tell us

about? Is there anything that comes to mind?

Sarah Nooravi: Sure. I have two in mind but maybe I could speak to

the one that just got productionalized recently, but it

doesn't deal with marketing. It's more on the product

side.

Kirill Eremenko: Sounds good. Give us a little insight into this world.

That would be very cool.

Sarah Nooravi: On the product side we have a lot of users who come

into our game and have some certain user behaviors.

For us, what we can do or what we're aiming towards

is as much personalization within the game as

possible because on our side we want to create a good

user experience and eventually some sort of purchase

or some sort of engagement so it's a win win. For us, I

think one thing that we were hoping to do was really

understand our users in terms of different segments. I

mean most of our users or listeners might know of K-

means, so doing a clustering model on our user

segments. Even though K-means isn't hard, really

understanding what, so the upfront on this is really

understanding what features really needed to play into

differentiating these different segments in order to

create really good, well defined segments that we can

now create campaigns off of and develop these

personalized store configurations or messaging in

order to create a better user experience.

Sarah Nooravi: The reason why I'm proud of that is because we just

closed the loop in our data pipeline, so not that this

model doesn't just exist on its own. This is something

that I talked about in a recent article that I wrote,

which is that most businesses are suffering from the

cold start of AI. They don't have that closed loop of,

whether it's the data infrastructure or whether it's

taking the model output and actually using it. What

I'm excited about is the productionalization of my

model which is now taking the output and it's pushing

to a live environment where we can actually build

these campaigns and do something with the model

output rather than it sitting in a PowerPoint or sitting

on Jupyter Notebooks or in a Python script

somewhere.

Kirill Eremenko: That's really cool. You're right. Productization of Data

Science outputs, it's a whole new world. We often

think, "Okay, I've got the insights, I've done the

modeling, I've got the insights, here's the presentation,

done." No, that needs to go to the IT department or

whoever else and that needs to be implemented, like it

might be actually you might have to reprogram it in a

different language. You might have to create some sort

of protocols for it to talk to the existing servers and

infrastructure and it has to somehow be integrated. It

has to have its own window during the night when it

will be running. How often does it have to refresh?

How do you maintain it? Who looks at the results?

How do they get integrated in the company? That's like

a whole new project on its own.

Kirill Eremenko: Tell us a bit about, first of all, congratulations. That's

a massive win, but it would be really cool to hear like

were you involved in, how did you hand over this part

from, like you created this K-means cluster algorithm

which I think actually a pretty cool approach to

creating a better user experience. Let's cluster our

users and find out what kind of groups do we have,

but then how were you involved and what is the

process like of taking what you create and handing it

over to the people responsible for productization of

your development?

Sarah Nooravi: Yeah. I had to work really closely with our engineering

team who were specifically building out this process

for us to essentially schedule the output. My script

runs every day and I had to work with them to figure

out, okay, so they came up with a wrapper that will

essentially take the output that I, the script that I'm

running and it'll wrap it within the activation process

that they have. Then it'll push to a live environment

and so I had to work with them to understand a lot of

what GitFlow is.

Sarah Nooravi: I know how to use Git and I know very basics of

committing but GitFlow is a whole new world of taking

you through different environments. From dev to test

to stage to prod and really having them walk me

through that and working really closely with them so

that, when you're pushing to a live environment you

don't want to break anything. You want to make sure

that you're testing every step of the way and you're

doing QA on your output every step of the way.

Learning that process, working really closely with the

engineers to help document that process so that we

can, the next time that we want to work on a project

like this or a productionalized output, that it's stable

and that it's easy to follow.

Sarah Nooravi: What else? I think mostly the hardest part was really

getting, because I was one of the first people to help

productionalize output through that process, so it's

really like understanding how it is so that I can

eventually teach our team. Then working, so that was

the engineering side of just getting it in team with

stakeholders. The person who, the PM, the product

manager for that particular game, I had to work with

them on developing, "Okay, well what's the attribute

going to be? How do you want it labeled? When you

call it in your live environment," all the nitty gritty of

what they need from their side.

Kirill Eremenko: Yeah, wow. That sounds like an involved job. How long

did the project take you and how long did the

productization take you?

Sarah Nooravi: The project took me, I would say from scoping out the

project to actually implementing and being done with

the model, maybe a couple of weeks. Then the

productionalization of it, also probably a couple of

weeks. In total probably around a month or so.

Kirill Eremenko: Interesting. The productization takes as much as the

project itself.

Sarah Nooravi: I think it was a little bit slower only because I was

learning the GitFlow process and really on the, it was

very engineering heavy. Being the first one doing it,

obviously there's hurdles. I think second, third time

around, that process will be much quicker.

Kirill Eremenko: If you don't mind sharing, why did you pick K-means

clustering out of all the available algorithms?

Sarah Nooravi: That's also a good question. I was familiar with it and

it's simple, there's really nothing too complicated

about it. I think because I was familiar with it and

because I needed to have such quick turnaround it

was a project that I, it didn't necessarily, it wasn't my

highest priority but it was a priority. I was like, "Okay,

can I get good results using K-means?" When I saw

that it was performing pretty well and I was getting

results that seemed reasonable and that I could put

into effect pretty quickly, I was like, "We're just going

to run with it."

Kirill Eremenko: Gotcha. That's the way to go sometimes, right?

Sarah Nooravi: Yeah.

Kirill Eremenko: It's fast, you get results, the 80/20 rule. Why would

you spend, you already spent in total like a month on

this project with the productization, why would you

spend six months on it if you can already implement

something and get the results? That's very cool, that's

very cool.

Kirill Eremenko: Okay, well thank you very much. That's a very

interesting case study. I'm sure a lot of our listeners

got a great insight into this world and especially this

whole productization approach. I want to switch gears

a little bit and talk about something that I think we're

both passionate about and that is diversity. When you

were at DataScienceGO, you spoke on the panel of

Women in Data Science and we had an interesting

chat about in general how to enable, empower more

women to get into this space just before the podcast. I

would love for you to share your thoughts on this with

our listeners if you don't mind.

Sarah Nooravi: On the importance of diversity?

Kirill Eremenko: Yes, please.

Sarah Nooravi: Sure.

Kirill Eremenko: Importance of diversity and what can we do as a

community of data scientists to help anybody

regardless of their agenda or ethnicity, background, to

be able to get into this space and really benefit not just

like an individual company but the community in

general and bring those new ideas, fresh perspectives,

insights into this community that we're building of

data scientists.

Sarah Nooravi: Sure. In terms of the importance, I think every

company that wants to maximize the production that

it's making within its business and get the best ideas

to come out and the best solutions to the problems

that they're trying to solve would think about diversity

as one of the key factors that it would need to try and

incorporate. This has been proven time and time

again, where diverse teams will outperform non-

diverse teams on different approaches and solutions to

problems. Especially when we're dealing with like

challenging and complicated problems that involve the

entire world at this point, we're trying to make

solutions that affect everyone, having a team that

looks representative of who they expect their

consumers to be would be important.

Sarah Nooravi: The one example that sticks in my mind and it's like

forever since I heard it was a woman who worked at

Google X when they were testing out their Google

Glasses. She was like, "You know, I'm on the panel to

essentially test out the product and then come back a

week later with feedback." She was like, "Yeah, so I

took the Glasses, I wore them for a week and then I

came back to talk to the team about my feedback,"

and her feedback was essentially like, "When I take the

Glasses off, it sticks to my hair, so hair pulls out when

I take it off." The guys in the room were like, "Well,

why don't you put your hair up?"

Sarah Nooravi: It's like, wait, hold on, do we really think that that's

the solution?

Kirill Eremenko: That's so funny, that's so funny.

Sarah Nooravi: When you think about putting together a team and

really creating products and solutions for the masses,

you have to have a team of people and be open minded

and hear that feedback but be actually willing to do

something about it.

Sarah Nooravi: The importance part, I don't know that I need to argue

too much. I know that we can all agree that diversity is

important, right?

Kirill Eremenko: Yeah.

Sarah Nooravi: Why it's challenging is that it's not very common yet.

Whether it's the minorities or the women or even

minorities of educational background. Maybe someone

who studied humanities who wants to get into Data

Science or someone just doing something totally

random that you wouldn't expect who has an

analytical focus and they want to get into it and this

imposter syndrome that we talk about. I think

everyone can share in this idea of that we're trying to

figure out where exactly we fit in but by embracing our

differences and by being okay with, "Hey, you have a

different perspective than I do and that's okay."

Sarah Nooravi: The reason why diversity helps is because when you

think about it, when you get into a room and you see

everyone that looks like you, you don't think that you

need to press your point too much. You assume that

everyone thinks like you, but when you enter a room

and people don't look like you and you're like, "Wait,"

or you know that they come from different

backgrounds, you're like, "Okay, I need to convince

people of my point." That's why the diverse teams

work, is because everyone's now talking about and

actually expressing their perspectives and now a

discussion gets made about it and then you arrive at

the best solution.

Sarah Nooravi: Within our community, I think what we can do is

understand that that's our goal. Our goal is always the

same, right? We're always aiming towards the same

goal, that we want to achieve the best product, we

want to build an inclusive community and a lot of that

is just embracing someone else's differences. Being

like, I know this is going to be hard. Diversity doesn't

just happen overnight and it doesn't take no effort. It's

hard to accept someone who looks like you thinks

differently from you, et cetera, et cetera.

Sarah Nooravi: Things that I've done that I think that other people can

do is just start these conversations. Tell someone that

they did well. Reassurance like, "Hey, you did really

well on that, that was really great." Make people feel

validated in what they're doing, like as managers or as

colleagues or as friends. "Hey, you did really well on

that, that was really impressive." Positive affirmations

could go a long way. Mentorship can go a long way.

Standing up for someone when you feel like they have

no voice. Like sometimes depending on who's speaking

in a room, you may listen to them differently and so

giving someone who otherwise maybe wouldn't step up

and defend themselves, "Hey so and so, you had a very

good point about X, Y, Z, do you want to talk about

it?" Like helping support each other within teams and

within the community could go a long ways.

Sarah Nooravi: I did want to mention, something that I'm doing is I

started a mentorship program called GLAD. It stands

for something hilarious. My creative team, some guy

on my creative team came up with the name. It stands

for Glamorous Ladies and Data.

Kirill Eremenko: That's nice. That's really smart.

Sarah Nooravi: You know, it's funny but I don't want it to be geared

just towards women, right?

Kirill Eremenko: Yeah.

Sarah Nooravi: I want it to be a very inclusive environment where we

can just work together and develop a supportive

community. It's essentially just that. It's bringing

people together and just building up confidence and

reassuring people and helping them through a lot of,

sorry, I'm going to go on a tangent but a lot of Data

Science is, it's the technical side but it's also a very

emotional journey.

Kirill Eremenko: Yeah, yeah, yeah, totally.

Sarah Nooravi: When you realize you're helping someone through

their journey into Data Science, it's not just, "Hey, let

me help you find the best models or let me help you

with resources." It's also a lot of reassurance. It's that

emotional side of, "Oh, the imposter syndrome, do I

feel like I belong here? Maybe I'm not qualified, maybe

this isn't what I should be doing?" It's really helping

people build that confidence regardless of who they

are, right?

Kirill Eremenko: Yeah, yeah, totally. Totally agree with you. Thank you

very much for that very inspiring talk and also good

suggestions. Positive affirmations, mentorships,

standing up for someone, even just saying, "Hey, what

do you think about this?" I want to add to that what

we talked about just before the podcast were, role

models. Role models are super important and the

whole, it's really hard for somebody from like, for

instance, a woman to get into Data Science when they

don't see that many data scientists.

Sarah Nooravi: Yeah.

Kirill Eremenko: When they see, women see that there's only 10% on

average of data scientists that are female, then that's

what you will get in terms of people entering the field.

We want to improve that and so the best way to do this

is to show that there are actually lots of successful

women who are enjoying being in the field of Data

Science. That doesn't mean you have to be like the

best data scientist in the world. All you have to do is

just show up. Go to a meetup and be present and that

will show people or show women who want to get into

Data Science, will that you are there, you're a

successful woman in Data Science. Or like maybe

invite somebody to a talk or try to present at a talk.

Things like that. Just more publicity in that space for

women will attract other women into this space. That's

kind of my thoughts on how we can help in the sense

of role models.

Sarah Nooravi: 100% agree, yeah.

Kirill Eremenko: Awesome. Okay, well Sarah, thank you so much.

We've come to our time limit on this show. Thank you

so much for coming and sharing all these insights,

totally loved the chat. It was lots of fun exploring your

background. Before I let you go, what would you say

are the best places for our listeners to get in touch

with you, contact you and follow you and your

interesting career, see what you get into in the years to

come?

Sarah Nooravi: I think LinkedIn has definitely been that one platform

where we're developing all of our network in Data

Science. Linkedin is probably the best way. I'm not

really active on Twitter yet.

Kirill Eremenko: Gotcha.

Sarah Nooravi: [inaudible 00:59:54] when I do.

Kirill Eremenko: Gotcha. Awesome, awesome. Yeah, you guys, so

listeners on the podcast, Sarah has 23,000 followers

so make sure to join all the people benefiting from the

things you're sharing. By the way Sarah, I had a look

at your recent article, Creativity in Data Science, very,

very interesting. I also like the talk by Sir Ken

Robinson on TED and I like how you incorporated his

ideas into Data Science and the whole notion about

creativity. I highly recommend for others to check that

out as well.

Sarah Nooravi: Thank you.

Kirill Eremenko: Okay, and I have one final question for you. What is a

book that you can recommend to our listeners to help

empower their careers?

Sarah Nooravi: Okay, so I am reading this book currently. It's called,

which you've probably read it, How to Win Friends and

Influence People by Dale Carnegie. You've read it,

right?

Kirill Eremenko: Yeah, amazing book.

Sarah Nooravi: I think that after reading that book, and I just put up

a post not that long ago, maybe last week, talking

about the importance of human relations. Especially

me who's thinking about the future and where things

are headed, I think understanding how to deal with

people and especially when you get up in the ranks as

a manager or a director and you're dealing more with

the human side and less on the very technical nitty

gritty side and especially with things getting

automated the way they are, I think that people should

be looking towards improving their communication

skills and looking towards how do they improve the

relations with people and their human skills.

Sarah Nooravi: That's one that I would say, if you ask me next week

maybe I'll have a different book but I think if you're

thinking holistically about the Data Science space and

of different skills that you should have and you think

about a well rounded data scientist, I think this is

definitely a book that everyone should read.

Kirill Eremenko: Fantastic, and that book can help you not just in Data

Science but in all other aspects of life as well.

Sarah Nooravi: Exactly.

Kirill Eremenko: Awesome. Thank you so much, Sarah. Amazing having

you on the show today and really appreciate you

coming on and sharing all those wonderful insights.

Sarah Nooravi: Thanks, I had such a great time.

Kirill Eremenko: There you have it. That was Sarah Nooravi. I hope you

enjoyed this episode as much as I did and my personal

favorite part was how open Sarah was, how positive

this episode turned out and how many laughs we had.

That was very exciting, very fun, and you can tell right

away that most likely Sarah is extremely successful in

presentation skills and communication. No wonder

Sarah recommended the book How to Win Friends and

Influence People by Dale Carnegie. I think we all as

humans can pick up some interesting tips and ideas

from that.

Kirill Eremenko: As always, you can find all of the show notes for this

episode at www.SuperDataScience.com/227. That's

SuperDataScience.com/227. There you'll find all of the

materials that we've mentioned on the show, including

the URL to Sarah's LinkedIn. Make sure to connect,

make sure to follow Sarah and get all these interesting

updates and insights that she'll be sharing in the near

future. Make sure to forward this episode to somebody

you care about and somebody you want to inspire. On

that note, I look forward to seeing you here next time.

Until then, happy analyzing.