SDS PODCAST EPISODE 177 WITH ZACH LOERTSCHER › uploads › … · does a lot of data warehousing...

Show Notes: http://www.superdatascience.com/177 1

SDS PODCAST

EPISODE 177

WITH

ZACH LOERTSCHER

http://www.superdatascience.com/177


Kirill Eremenko: This is episode number 177 with aspiring Data

Scientist, Zach Loertscher. Welcome to the Super Data

Science 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.

Welcome back to the Super Data Science Podcast,

ladies and gentlemen. Super excited to have you on

board and today we had quite an interesting episode

with an interesting guest, Zach Loertscher. Zach is an

aspiring Data Scientist who has made huge progress

in terms of building his own career and building his

online presence in the space of data science. And

interestingly enough, the way I actually found out

about Zach was through a blog post that he made with

a list of Data Scientist mentors to connect with to

follow. And I was so inspired by that idea, I thought it

was a great idea and a great way to help others, that I

had to invite Zach onto the show and find out about

how he actually thinks of data science, of the

community, and of building your presence here and

also playing your part, your role in helping others.

That's what we talked about quite a lot on this podcast

and you'll get some very valuable insights in how you

can better help the data science community and build

your own online portfolio. Also, we talked about his

thoughts on data science education, and in this

podcast we actually flipped it around and Zach asked

me a couple of questions, which I was totally not



expecting. And I got to answer a few questions on the

show as well, so you'll get to know a few of my

thoughts, especially on the situation with data science

education, with universities, with online courses and

things like that. All in all, quite a fun episode, a bit of

a different style this time. I hope you enjoy it. I can't

wait for you to get straight into it. Without further

adieu, I bring you Zach Loertscher, an aspiring Data

Scientist.

Welcome back to the Super Data Science Podcast,

ladies and gentlemen. Today I've a very exciting guest

on the show, Zach Loertscher. Zach, welcome to the

show. How are you doing today?

Zach Loertscher: Doing really well, I'm excited to be here.

Kirill Eremenko: Excited to have you here. Tell us, where is your

surname from?

Zach Loertscher: It is from Germany. Swiss, Germany.

Kirill Eremenko: Swiss, Germany. I was in Switzerland in the Swiss-

German part for a whole month in march this year.

Very nice place actually, it's very neat and very clean.

Have you been back since?

Zach Loertscher: I haven't, I've never been there. You know more about

it than I do.

Kirill Eremenko: Oh, I think you will love it when you go.

Zach Loertscher: Yeah.

Kirill Eremenko: Well Zach, first of all I wanted to say a huge thank

you. The reason why I reached out to invite you to this

show is because I saw a list that you put together and



somebody else represented in image format, which is a

list of top mentors in data science. I was very humbled

to see myself on the list, that was very exciting for me

but that's not the reason of course, why ... it's not, "Oh

just because of me." The thing is, I found it very

inspiring that you went through the trouble of actually

putting together a list of mentors in data science,

people who you can learn from and who inspire you to

grow in this space. So, thank you so much for putting

that together and for sharing it with all the aspiring

Data Scientists. Today, I just wanted to talk more

about that and your journey in data science, how does

that sound to you?

Zach Loertscher: That sounds awesome.

Kirill Eremenko: All right. Well, tell us a bit about yourself. Where do

you live? And what are you currently doing in your

life?

Zach Loertscher: Currently I live in Idaho, in Rexburg. Not many people

know about this place but it exists. I'm going to BYU

Idaho, pursuing a degree in business analytics and I

am about to graduate in a few weeks. Yeah, I'm really

excited about data science. I've been studying it since I

did my internship down in Utah, and I was working for

their business intelligence team and I found this really

cool tool called Tableau. That was just really, really

cool to me. Then I was also having to learn how to

program with Python 'cause we were doing a lot of data

transformation and I was going through one of those

long days of going through tutorials and trying to

finally find what you actually are looking for. I



stumbled on Kaggle and I was like, "Oh this is cool.

What are these people doing?"

Kirill Eremenko: Yeah.

Zach Loertscher: That's how I discovered machine learning and data

science and since then it's just been a really fun ride.

That's a little bit about me and my stage in life. Yeah.

Kirill Eremenko: Fantastic. So you were doing a bachelor's in business

analytics?


Kirill Eremenko: Okay. Was it a four year bachelor?

Zach Loertscher: Uh huh.

Kirill Eremenko: And what was the university?

Zach Loertscher: BYU Idaho.

Kirill Eremenko: Oh okay, okay. I didn't catch that. Okay, cool. I was

surprised to learn that, it was a very apt degree for

getting to the profession of data science. You don't

hear many degrees in business analytics, is that a

recent degree that they've come up with or has that

been around for a while?

Zach Loertscher: Yeah. Well, it's been here for a few years. They actually

just created a data science degree but I'm already so

far into it, I'm like, "Well, I'll just finish with business

analytics and pick up all the other stuff along the

way."

Kirill Eremenko: Might as well, might as well.




Kirill Eremenko: Okay, cool. Why did you get into business analytics in

the first place? Because that was what, four years ago?

Is it because of what you heard about data science and

what's going on in the world? Or was there another

reason?

Zach Loertscher: Actually my dad, he works in business intelligence. He

does a lot of data warehousing and he started in

reporting and I was like, "You know, this looks

interesting to me. I know there's high demand for it."

And I didn't actually really know about data science

when I started out on this degree. I was like, "I'll just

go along this path that my dad has gone along." It

wasn't really until my internship when I really

discovered data science and what it is and I found out,

"Hey, business analytics actually fits into this a little

bit."

Kirill Eremenko: Yep.

Zach Loertscher: And it just was a really happy accident, to be honest.

Kirill Eremenko: Yeah, yeah. That's pretty cool. Okay. So you're on this

journey, but tell me this, many people who are

studying are just happy studying. Yet, you go above

and beyond to actually find online courses, like you

mentioned before the podcast, I think you did one of

our courses or at least a couple courses already, then

you finding resources and you put a list of mentors

together who inspire you in data science. What pushes

you to do that? Why did you decide to go that extra

mile?

Zach Loertscher: I think it's mostly been knowing that LinkedIn is such

a powerful networking tool and I've been really trying



to push for a stronger network as I'm approaching

graduation. That was really the first place I went to

because for a very long time, for I don't know, four or

five months, I would just look at LinkedIn and look at

what other Data Scientists were saying and just follow

them and say, "Oh, that's cool, that's what they're

doing." But I wasn't really posting anything. I noticed

that a lot of people on LinkedIn talked about, "It's such

a great networking tool, it brings people together, it

helps you find a job." I was like, "I'm gonna try this out

and I'm ready to contribute. I've been looking at this

for a while, I've been studying it for a while and I want

to contribute, I want to make a difference."

That's really what prompted me to put it together. I

remember when I was just starting at data science,

someone else had posted a list of a bunch of Data

Scientists I should follow and that's when I really

started learning about it. So I was like, "Let's pay it

forward a little bit." And I had no idea that, that post

would be so popular, but it was really exciting, it was

really cool and that's really my motivation. One of the

best parts about it is getting messages from people

just thanking me. I've heard other people say the same

thing 'cause I haven't done too much, but just

thanking me for contributing what I have, saying,

"Hey, you helped me with this." And that's just one of

the most satisfying parts about it all. Yeah.

Kirill Eremenko: Yeah, yeah. Gosh, and you actually helped me as well,

apart from finding YouTube and watch this podcast.

The way I found your list, I think Ben Taylor, you

know Ben Taylor?




Kirill Eremenko: He recommended me to look at your list for guests to

invite to Data Science GO 2018 and-

Zach Loertscher: Oh cool.

Kirill Eremenko: Yeah, and I was going through your list, I was like,

"Oh, I haven't met this person, I haven't met this

person." And I just went through every single one and

invited everyone to connect on LinkedIn. It was a great

opportunity for me to connect with the top influences

in the field of data science, so thank you. It really

helped me out as well.

Zach Loertscher: Yeah, you're welcome.

Kirill Eremenko: In fact, we're probably gonna share this on the show

notes for this episode, for all listeners, I highly

recommend going to check it out and then just

connecting with every one of those influencers there

and following them, and learning from them just as

Zach did, and I'm learning and others are learning as

well. Yeah, that can be a good addition to peoples

LinkedIn networks. Tell us a bit about, if you don't

mind, since you made the decision that you want to

build this network and you want to contribute back to

the community, how have you felt the impact? You

mentioned that people have said thank you, but has

anything else happened for you in this period of time?

Zach Loertscher: I feel like by posting, you develop these ... they're not

real life relationships but there are certain people who

will always comment and you develop these

friendships almost on LinkedIn, which is awesome.

But also, if you think about LinkedIn and the platform



and what it's designed to do, it's designed to keep you

on the site as long as possible. They get their revenue

through ads or through hooking you up with a

company or someone else. So if you have a lot of posts

and you're putting a lot of content out there that

people are enjoying and engaging with, when a

recruiter searches for you, it will boost you in their

search results. 'Cause they want recruiters to find

people who are active on the site so that the recruiters

will stay on the site longer. At least, that's my theory.

That's basically what happened for me, is as I was

posting more and engaging more with the community,

I was getting contacted by a lot more recruiters. It

really wasn't until I started posting so much that, that

happened. So that was just an unseen benefit for me

and one of the best ones for me because it's led to a lot

of opportunities and a lot of connections.

Kirill Eremenko: Yep, yep. That's definitely what LinkedIn is all about,

and putting yourself out there and building your

brand and portfolio and helping others is gonna get a

lot of attention going your way. Yeah, that's a good

start, you're definitely on the right track with your

career. You haven't even graduated yet and you

already built up this portfolio, how does that make you

feel?

Zach Loertscher: Just really excited to continue to engage with the

community.


Zach Loertscher: I just feel really privileged and blessed to have that

opportunity, that there is a good group of people that



see my posts, that's exciting for me 'cause I get to

share my journey with other people and hopefully

inspire other people who come from a similar

background or maybe are just beginning. And maybe

share some encouragement or motivation, share some

of my discouragement to help them feel encouraged

'cause it's a hard journey for everyone. That's the

exciting part for me.

Kirill Eremenko: Gotcha. How much time on average do you think you

spend per day on LinkedIn?

Zach Loertscher: Too much time. Probably an hour, maybe an hour and

a half.

Kirill Eremenko: An hour and a half.

Zach Loertscher: It depends on the day, just how busy I am, but yeah

probably around there.

Kirill Eremenko: Does it feel like work to you? Or does it feel more like

you having fun?

Zach Loertscher: When I'm posting, it feels more like ... I don't know, I

put a lot of thought into it and it takes a lot more

thought than when you're just scrolling, right?

Kirill Eremenko: Right, right. Yeah, yeah.

Zach Loertscher: But no, it's really exciting 'cause I've been following a

lot of these people for a long time and you get to see

their journey as well. You also discover a lot of new

trends in the field, new things that people are

discovering, new cool projects that someone else did.

I'm always seeing these cool new innovations that

people created with technology or deep learning, things

like that. That's really exciting for me too.



Kirill Eremenko: Who would you say is your favorite Data Scientist to

follow on LinkedIn?

Zach Loertscher: My favorite to follow, I'll think about this for a second.

Kirill Eremenko: I see you have commented on quite a few posts by

Randy Law recently.

Zach Loertscher: Yes, yes, yeah.

Kirill Eremenko: Would you say Randy might be that candidate?

Zach Loertscher: Yeah, he's probably one of my favorites. He's always

posting something really positive and motivational,

and also posts these awesome lists of resources. And if

someone else is asking me for help, I usually just send

them one of those lists of resources 'cause they're so

awesome. Yeah.

Kirill Eremenko: Yeah. Yeah, okay. That's very cool. All right. That's a

great way of getting this knowledge, by following the

people that are at the top of data science or the cutting

edge of data science and they're trying to break it

down in simple and complex terms and then you can

just learn from them and see what resources they use.

You get ahead of the game, you skip all that searching

that they are doing and they save you a bit of time that

way, I guess. That's very cool. What about your future

career? You're graduating in a couple weeks. That's

very exciting and congrats on that.

Zach Loertscher: Thank you.

Kirill Eremenko: Where do you think you're gonna go from there? Have

you already lined up a job? Or is there an industry

that you're interested in?



Zach Loertscher: Yeah, actually I have. I don't know if I'm supposed to

keep it on the down low or not, I'm not sure.

Kirill Eremenko: Probably stay on the safe side but maybe just ... all

right, congratulations. If you've lined up a job that's

fantastic. But yeah, tell us once you have a job, are

you going to continue learning? What kind of data

science are you going to continue learning? In what

area are you doing to direct your education?

Zach Loertscher: Yeah, I'm really fascinated by the healthcare industry,

actually, and a lot of the innovations that are

happening there because one of the projects that I did,

there's a really cool data set on Kaggle about breast

cancer. Actually, it was the first data set that I ran a

logistic regression on and it was really fascinating to

be able to see I can get 95% accuracy at predicting

breast cancer. Something that nobody likes and

something that everyone's researching to try to get rid

of, you can use and harness the power of machine

learning to help with this issue. So that's the field that

I'm hoping to end up in, really. Is in that field of using

machine learning within healthcare, because the

traditional healthcare system has just been, you go in,

you see a doctor, someone who's been to school for

several years and they're very wise, they're very

practiced. But there's no way that you could ever

harness all the data that the medical professionals use

in one day without using Data Scientists. And that

just makes me so excited.

I saw a video the other day of ... I don't remember

where it was but they were using deep learning to spot

cancer cells in realtime, draw a little circle around it



through a microscope in realtime, and it was all based

off of these deep learning algorithms that they had run

on these images. It's just fascinating to me, so that's

where I really want to end up is at the healthcare

industry, 'cause I think there's so much potential there

and a lot of potential to do good as well.

Kirill Eremenko: That's a very noble cause. Have you heard of the

conference called HIMSS?

Zach Loertscher: Uh uh.

Kirill Eremenko: It's spelled H-I-M-S-S, and I just recently found out

about it myself, just a few days ago. I think this one is

held in February and it's massive, it's like 40,000

people attend and it's all about healthcare and

technology, the intersection and ... it's not all just data

science but there's a ... I haven't been to it but I heard

about it from a data science podcast, I'm assuming a

large portion of it is dedicated to data science. Why I'm

mentioning this is because first of all, it stands to

show that this is a very rapidly growing industry, or

intersection of industry of healthcare and data. Also,

maybe you and others who are interested in healthcare

can check it out. It's HIMSSconference.org, might be a

good thing to attend maybe.

Zach Loertscher: Yeah, absolutely. I'll definitely look into this.

Kirill Eremenko: Okay. I understand it's a noble cause to help people

with healthcare and apply to data science in that area,

is there any ... there's also other areas of where you

can apply data science. Finance, you can apply data

science in safety, in full detection and other areas.

How did you single out this one? We already



understand why, that you want to help people, but

what thinking process did you go through? Did you

just meet someone who is in healthcare and you got

inspired? Or did you go through lots of industries and

you picked this one? Or was it just because you were

able to get a job in this space and then you learn more

about the company and you were inspired by what

they do? Can you walk us through the thinking

process of somebody's who's studying, how do they

pick an industry that they're gonna go into?

Zach Loertscher: Yeah, absolutely. I think one of the most powerful

things that someone can do before they decide on an

industry, like, "I'm gonna go in finance or I'm gonna go

into healthcare.", or whatever it might be, is reach out

to people who are currently working in that industry.

Better yet, people who have recently been hired in that

industry. For me, as I was pondering about going into

the healthcare industry, I did reach out to a few people

who do work with data from the industry, and they

both spoke very highly of it. They said it's fascinating

the things that you learn. So for me, that was a really

good motivator. Also, just thinking about where ...

data science has come a long way for business but I

think there's a lot of potential for growth still in the

healthcare industry. I think business has been on the

bleeding edge, obviously there's research, but business

has been on the bleeding edge of using data science.

And I think healthcare is coming to that point where

it's going to be using it a lot more.

So, just spotting that opportunity gets me really

excited, like, "There's gonna be a lot of jobs in this



field, there's gonna be a lot of opportunities." So I

think just being aware of what's happening in the field

and thinking, "Which industry could really explode

next?" But probably even more importantly is talking

to people who are in that industry so that you get a

good idea of where it's at and is it going to explode

next?

Kirill Eremenko: Yeah. No, that makes it very clear. So you are

identifying an opportunity for the situation to grow,

and you're right, in business, competitive pressure

makes companies adopt newer and newer technologies

all the time. And that might not really be the case in

healthcare, it might take a bit longer for that to

happen. Okay. That's very good. The other thing I

wanted to ask you is actually what you asked me

before the podcast, you mentioned that you are

interested to find out about degrees. Is a formal

education required in data science? Or are online

degrees an exception? Can you repeat that question?

What is it exactly on your mind when you're pondering

that?

Zach Loertscher: Absolutely. As a new graduate, and I've found that

position ... I'm thinking a lot about grad school and

traditionally, in the field it has been, "You need a

graduate degree." But the cost of education everywhere

is skyrocketing, but also the availability of open source

or very low cost education online is also skyrocketing.

So I'm wondering what your take is on these online

certificates, maybe if people go to Corsair, or Udacity,

or Udemy and learn about these things, do you think

that companies will begin to value those certificates as



much as they value an advanced degree? Or do you

feel like it'll keep on the same trend that it's been

following?

Kirill Eremenko: Okay, that's a good question. What I would say is that,

I think those certificates ... and this might be a bit of a

controversial answer but I think those certificates,

those online certificates are gonna be valued less, and

I think university certificates are gonna be equally

valued less. The reason I say that is because ...

especially once you already have a degree, which I

personally think if somebody's looking to starting a

degree, at the very beginning, you can go without it.

But especially if you already have a degree and you

already have a job, from here what counts is your

experience, is your ability to demonstrate that you

have industry or industry level experience in the field.

And that's all that matters because people don't really

care about, or employers don't really care about

another paper.

At the end of the day, whether you have a paper or

not, what they care about is, can you solve their

problem, can you add value to their business, can you

add value to their bottom line of their profit and loss

statement. And having a degree might indicate the

possibility of you being able to do that, but there's so

many other ways right now online that you can

demonstrate that much more efficiently and much

more succinctly. Whether it's by going, like you said,

to Kaggle and doing projects there and adding them to

your portfolio, or using, again, Tableau and building

an online Tableau public portfolio and showcasing



things there. Whether you're on a website or blog and

sharing information there, you don't even need to blog

these days. As you've noticed, you just share stuff on

LinkedIn and ...

Well, you can't really share if you have a job and

you're working there and you have made some

breakthroughs, you can't really share those things

because that's sensitive information. But you can

share the techniques that you use, again, if they're

publicly available techniques, or ideas that you came

up with if you're not violating any intellectual property.

Or just to share thoughts on other data sets and how

you would approach other challenges. Ultimately, if

somebody's signing up for a degree such as a master;s

degree, there has to be an intention in mind. You

cannot just sign up for a degree and say, "Okay, I'm

signing up for this degree just for the sake of doing it,

just for the sake of having a paper." That's definitely,

in my view, a waste of time.


Kirill Eremenko: If you have an intention in mind, for instance, I want

to have, not just a data science job but like in

healthcare, which is really, really great stuff, but I

want to have a Senior Data Scientist, Senior Machine

Learning Expert in the field of healthcare. Or maybe,

let's say Senior Machine Learning Expert in cancer

prevention. You have a very specific goal in mind and

that's hwy you would pursue a master's degree in

machine learning or something like that. Well, as long

as you have this intention in mind, all you have to do

now is replace the word degree, which I find is like a



safe bet, or a person that ... like if somebody who

would take a degree is ... maybe not always the case,

but in my mind, it might be somebody who just lacks

the proactive approach to be creative and come up

with an alternative solution that might be faster, but it

might be a bit more difficult.

Let's say this one, you go and you create a portfolio of

projects and you share them through LinkedIn

consistently for six months, that are specifically either

about machine learning or cancer prevention using

data science, or and machine learning. And if you keep

doing that for six months and you share your work on

one project every four weeks, let's say in six months

you share six massive projects that you did, you

described them, you spend a week or two just writing

that blog post out, in addition to the two weeks that

you spent on the project. So in six months, you have

six big projects that you shared with the world that

added value to businesses, and people, and

professionals, and aspiring Data Scientists, and that is

going to give you so much more visibility than just the

paper that you can share on your LinkedIn or in your

resume. The process is gonna be faster.

A master's degree is gonna take you at least two years,

or at least one and a half years. This can be done in

six months and it also is gonna be more current

because those degrees that you see in universities,

unfortunately, they are usually outdated by the time

they are released. Because to go through the formal

approval process in the university, they have to put

the curriculum together, get it approved by the Dean,



by the faculty, by the university, et cetera, et cetera, et

cetera. By the time it rolls out, it's already been at

least 6 to 8 to 12 months and the world's moved on

from those case studies, from those methodologies.

Not that far but there might've been a breakthrough or

some other discovery that is not included in the degree

and therefore you're like, what you're studying or what

you're paying money for, there's already something

newer out there. Whereas if you're doing it yourself,

you can always adapt. All those projects take a month

at the most. That's my take on it, especially having

completed a degree and having a job, you're not

pressed for time, you have a way to sustain yourself

and you have this hopefully free time to work on

projects, I would just go do it my own way.

Zach Loertscher: Yeah, yeah. Absolutely, cool. Wow, that's a very

powerful answer, I really appreciate that. I got some

gold nuggets of wisdom in there.

Kirill Eremenko: No worries, man. Sometimes it's sad to see people

losing time, time is the most precious thing we have. I

get the appeal of a degree, whether it's a bachelor or a

master's degree because it's what our parents did,

what our grandparents did, what everybody's

expecting you to do. It's a big scary to go without a

degree, or without a master's degree, or without a PhD

because still, it has some way, this feeling that it's an

accomplishment, it's like a check, "I checked this box

off." But ultimately, if you'll even look at the most

successful people in the world, most of them are drop

outs, there's Mark Zuckerberg, Steve Jobs, they never

completed universities, as far as I know. I might be



mistaken somewhere here, but most of these people,

they realize that, "Hey, no, there's something else I can

do." It's about making your own rules and playing by

your own rules.

University and all those degrees, they are rules that

have been created over time and society has imposed

upon itself and accepted. And by following them, you

follow a safe path that is guaranteed to get you

somewhere. And even though, it's just the perception

of those guarantees. Those guarantees are actually

fading, dwindling away exponentially as we move

through the years, as we move into the world of

internet and technology and more people are actually

coming online. This guarantee is actually dwindling

away, it stays in our mind through this upbringing,

through our cultural education and things like that,

but in reality, this is the best time probably in the

history of human kind to break rules and play by your

own rules. I'm not talking about legal rules, I'm talking

about cultural rules of the way that we are used to

building our careers and education and so on. The

people that break the rules are the people that create

their own rules and play by their own rules, those are

the people that get ahead the fastest and succeed the

most.

Let's say for instance in your case, you've created your

own rules by saying, "Hey look, I want to give value

back to the community on LinkedIn." That's already

not what most people do, 99% of people don't ... I don't

know how many people have or don't have LinkedIn

but let's say out of the people that do have LinkedIn,



99% of people don't share valuable blog posts of their

own creation. But then you take it even further, you're

like, "Well hey, how about not sharing just a blog post,

how about sharing a list of mentors, people who have

influenced me. Let me collate that information." That's

like you creating your own rules, nobody else had

thought of that, especially since they said, "Oh maybe

a few people have." But you were like, "Let me do that

from my perspective."

Look what that's gotten you, so many people have

contacted you, so many people have gotten value out

of it. And right away, as soon as you do something

that doesn't conform with the rules, you ... there's a

saying, "If everybody" ... I don't remember exactly how

it goes, but, "If everybody around you thinks you have

a stupid idea, it's either they're true, they're right, you

have a stupid idea, or you're on the verge of a

breakthrough." Right?


Kirill Eremenko: I'm not saying it was a stupid idea, but I'm saying if

you're not conforming with everybody else, you might

not do anything, you might be wrong, but on the other

hand you might be right and therefore you will have

this exponential leap all of a sudden. And "Bam!", in

one blog post you have thousands of followers, people

are contacting you and you added tremendous amount

of value to others. So yeah, I guess the same goes for

education and stuff like that.

Zach Loertscher: Yeah, that's awesome.



Kirill Eremenko: Yeah. That's my take on these things. Any other

questions? I like this approach. Do you have any other

questions for me that I might be able to help you out

with?

Zach Loertscher: I'm curious a little bit about your own journey. You're

a successful entrepreneur, you've started your own

business, you're telling me you've got people from all

around the world in your company and I also share

this same philosophy. I know I'm finishing a degree

but as I come along in understanding the power of

teaching yourself and seeking resources on your own,

I've changed my mindset a little bit there as well. One

of the parts that I've struggled with and I know a lot of

other people struggle with is the discipline that is

required. You go to school and it's very structured and

you have accountability in place with professors, or I

don't know, maybe you're facing pressure from

someone else, and it is a very new idea to take your

own path and take charge of your own education. So,

I'm wondering, on your own path, what has helped you

the most to, I don't know, keep that drive, keep that

motivation, and keep that structure in place? 'Cause

that structure is very important.

I've definitely experienced just traveling down the

rabbit hole on 15 different web pages, all in one day

and not really learning anything at all. I'm curious,

what tips or techniques have you found to be the most

helpful in disciplining yourself and creating an

organized learning environment for yourself?

Kirill Eremenko: Oh, that's a good question. Oh okay. All right. Well, I

guess one thing is I don't like wasting time, that's



number one, because I don't like setting myself back

or putting myself at a disadvantage because I'm being

lazy and knowing that, that will cost me time later on

because I know I probably won't get that time back.

Realizing that, that you only get every hour, or day, or

even I think in more terms of years. Like if this is when

I was doing my degree or when I was working at

Deloitte, I knew that if I fail at something, failure's

okay, but you learn. But if I fail because I'm lazy

during [inaudible 00:36:13] then that's gonna set me

back in terms of my being able to progress through my

degree, or through my promotions, and so on. So I was

like, "I can't afford to do that because I'm responsible

to the future version of myself. Future Kirill, he's

gonna be upset with me or I'm gonna put him at a

disadvantage, that's not cool." That realization is

important, I guess.


Kirill Eremenko: The other thing is, have a vision. What do you want? If

you don't have ... they have a saying that, "Without a

target, you're gonna miss every single time." Right?

Have a target, have a goal. Where are you going?

What's the purpose of what you're doing? In terms of

organization of your work, there's a great methodology

by Tony Robbins called the RPM, called the Rapid

Planning Method. He talks about identifying the result

that you want, then setting a purpose behind that

result, which is the emotional driver behind what you

want. Let's say you want to learn R, in your degree you

might know Python but you want to learn R by the end

of the year. So that's your result, "I want to be able to



code a random forced algorithm in R by the end of the

year." That's gonna be your result but then what's the

purpose? It has to be emotional, it has to be like, "So I

can actually help prevent cancer and save lives of

people, lives of people in Idaho, or in other places."

Zach Loertscher: Sure.

Kirill Eremenko: Yeah. And so the M is the Massive Action Plan, that's

something to look up the Rapid Planning Method by

Tony Robbins, it can help you with the organizational

side of things. The reason why I was bringing this up

is because, "have a goal in mind", that's the first part

of the RPM, have a vision where you're going because

unless you have that, it's really gonna be hard to be

disciplined. Discipline is a micro thing, it's within a

day, within an hour you have to be disciplined, but

you can't achieve micro effectiveness without a macro

vision, without a macro goal in mind because where

are you going, right? You might be disciplined for an

hour, for a day, for a week, or a month but then you're

gonna be like, "What's the point of all this? Where am I

going? What's the purpose?" And you're gonna lose

motivation. So that's another thing.

The first image that pops to mind when you ask about

discipline is, when I was working at Deloitte and also

at the same time building these first courses I was

grading, just every 15 minutes I had a timer going off

when I had to write down, did I spend those 15

minutes effectively and were they good 15 minutes or

were they bad 15 minutes? Did I waste them looking at

Facebook or YouTube or whatever? And then I was

tallying them up for the whole day.



Zach Loertscher: Oh wow.

Kirill Eremenko: Yeah, it's pretty intense. There's a timer, Pomodoro

Timer it's called, you can get a version.


Kirill Eremenko: You know that one?

Zach Loertscher: Yeah, I've heard of it.

Kirill Eremenko: We have some people on the team that actually use it

and it works exactly like that, every 10 or 15 minutes,

whatever, it goes off and you have to write down what

you did. You just have to be very strict with yourself.

Another good saying I heard is, if you want to be an

entrepreneur, a successful entrepreneur, you have to

be the harshest slave driver for yourself. It's not about

bossing around other people, it's about bossing

around yourself and not letting yourself rest. Like all

right, you rest when you can, but yeah it's important

to be very strict with yourself. That's what I was-

Zach Loertscher: Yeah, awesome. More golden nuggets. I hope everyone

who's listening is taking notes 'cause this is awesome.

Kirill Eremenko: This is a fun podcast, it's like all of a sudden a

reversed situation. All right, well let me ask you a

question then.

Zach Loertscher: Sure.

Kirill Eremenko: You mentioned entrepreneurship, and you're in

healthcare and data science, do you think that data

science is a good space for entrepreneurship? Or is it a

space where you solely should focus on building a

career and progressing up the career ladder?



Zach Loertscher: That's an awesome question. I haven't been in the "real

world" yet, so I'm still finishing up my degree and

everything. So take my response with a grain of salt,

but I think that the data science field is probably the

best field to be entrepreneurial in because it has

everything set up for it on the internet. Everything that

is popular is opensource, everything that you'd ever

want to share can be shared and is being shared and

people are consuming it. If you're wanting to build

your brand or your business or whatever it is, data

science is probably one of the best fields for it right

now because if you think about it, for other fields, not

a lot come to mind at this moment. I don't know what

it is about data science but it's really taking off online.

Since that's the future of our society, is this

collaborative, cohesive societies, I think it's probably

one of the best fields.

Kirill Eremenko: Yeah. Yeah, no. That's very cool. You definitely can

connect with the right people apart from the resources

Amazon Web services, or [inaudible 00:42:23] SQL,

and all these tools, Python, I think they're a [inaudible

00:42:27] tool, all these online tools and datasets. The

other thing is that you can connect with the right

people to build this team, or even international team of

Data Scientists and make things happen. That's what

Kaggle's all about, right? They have some projects

where you can participate as an individual Data

Scientist but there's some projects where you're just

not allowed to participate, as far as I remember. You're

not allowed to participate as an individual, you have to

be part of a team, right?




Kirill Eremenko: And I think that's a really cool concept.

Zach Loertscher: Yeah, it's awesome. And something else to note that

I've noticed, I've been pretty heavily in the job search

for the past few months and something that might

make the entrepreneurial route more appealing is it

takes a very long time to set up the data infrastructure

required to really have a good functioning data science

team. And you can correct me if I'm wrong, but to have

your data warehouses all set up and normalized and to

have your data capturing processes all automated, if a

company just jumps on the data science train and

hasn't done any of that prep work and you were hired

by that company as a Data Scientist, you might find

yourself in a situation where, "I'm just not doing what I

was expecting." Right?


Zach Loertscher: So, that might make it more appealing as well 'cause a

lot of companies are still making that transition right

now. I put a post out a couple months ago talking

about this and it got really good response as well.

Talking about this, how every company is at this

different stage of their data evolution. Some companies

are towards the backside of things, they're doing

everything in excel spreadsheets and email and other

companies, they have all their data is all distributed,

it's all in the cloud, they've got realtime reports going,

they have realtime models running and making

decisions. So yeah, when you're comparing the

entrepreneurial side of things to the company side of

things, the entrepreneurial can be a little bit more



appealing in that sense. You get a little bit more

control over what you're doing and maybe you don't

have to wait so long for a lot of those things to happen

right now.

Kirill Eremenko: No, that's definitely a good point. The difference is or

can be, it depends on how you set up the

entrepreneurial side of things but let's say if you're a

Data Science Consultant, difference is you only need

the tools that you actually need, that you are used to. I

hear you use Python and Excel, or Python and SQL, or

the combination that you like, Python/Tableau/Excel

and you set them up for yourself and then in any

business you go and you're like, "All right guys, I'm

here. I can help you out with this specific type of

problem, here's my rate and give me your data." They

give you their data, you take it back, you upload it or

even if you use it through their tools, you can make

sure that they've set ... if they don't have it set up and

if it's going to take a long time, you just move on to the

next line.

Either way you do it, you have those tools in your

arsenal and you perform the analytics and that's it,

that's all you're worried about. You're not worried

about all the red tape, you're not worried about

waiting for approvals and so on and so on. You have a

plethora of choices of companies that could be your

potential clients because ultimately any business has

data these days, you just need to show them that you

can add more value than you're gonna cost them,

that's it.

Zach Loertscher: Right, right. Absolutely.




Zach Loertscher: Absolutely.

Kirill Eremenko: We had a guest on the podcast almost a year ago now,

and I still really like the approach that he takes and

it's about not charging the client until they see value.

So as a consultant or a Data Scientist ... and that's a

starting point for a data science entrepreneur, you

being a consultant, you can go into other spaces later

on, make great products and stuff. But there, to get

started as a consultant, you say, "Hey, I can add

value, I'm not gonna charge you anything, I'm gonna

do this project on my weekend." You do the project

and then you say, "Hey, if you like it, you pay me. If

you don't like it, that's okay, you keep the results and

no problem." At the end of the day, if your project

added ... I don't know, let's say it's a business that

makes $100 thousand dollars a month and you just

added 10% to their bottom line, you added $10

thousand dollars every single month, from now on

they're gonna save $10 thousand dollars.

It's pretty obvious that they're gonna be okay with

paying a consultant like that, a certain amount that is

around the $30 thousand dollar mark or $50 or

whatever.

Zach Loertscher: Oh yeah. Yeah, absolutely.

Kirill Eremenko: Yeah, make it a no brainer for them if you were a

consultant.




Kirill Eremenko: Okay, all right. Let me ask you another question. What

is your favorite part about data science?

Zach Loertscher: My favorite part of data science. I think I'll go back to

when I really discovered it, I think the most exciting

part for it, of data science for me, and it still is, it's just

the idea that you can give power to a system to make

decisions. Does that make sense?

Kirill Eremenko: Mm-hmm (affirmative).

Zach Loertscher: In my mind, I mean, maybe a lot of people don't think

that's exciting but for me it's really exciting. I think

that, that's just an amazing thing because I've worked

at places where decisions are made by gut feelings or

people maybe don't use the data to the fullest capacity

that it could be used to. But even taking it a step

further and saying, "Look, the data, we can use this

and harness this to tell us things that we could never

find out otherwise.", is really exciting for me. The other

part that I think is really exciting about data science,

is I love the data visualization part of it. I love being

able to take something that's hard and coarse and

rough as math and numbers and put it to a visual that

people can understand and digest and really gain

insights from. Those would be probably my two

favorite parts. Sorry, I know you asked for one and I

gave you two.

Kirill Eremenko: No, no, that's cool. No, that's good. Yeah, no, I like

that. I like that you have at least a couple of things

that you're super excited about in data science and

definitely a very diverse field where anybody can pick

what they're most interested in. Like somebody

listening to this podcast might disagree with you in the



sense that they have their own preference, they might

be excited about the data preparation part, or

identifying the challenge or the problem, or talking to

clients and things like that. Totally normal, totally

agree with that.

Zach Loertscher: Absolutely. What about you?

Kirill Eremenko: For me?


Kirill Eremenko: What's my favorite part of this, nobody's ever asked me

that question.

Zach Loertscher: It's hard to choose.

Kirill Eremenko: Yeah, I know, it's hard to choose. I think for me it

would be breaking down the complex into simple, it's

that part where I know the insights, I know what I

found. Now, how do I explain it? How do I make these

faces of my audience light up and see in their eyes that

they have passed through a threshold concept, for

instance. A threshold concept is once you learn

something, that's a threshold concept, you never see

the world the same way again, right?


Kirill Eremenko: Like, "How do I make sure that they understand this

and they can apply it and they can make their

businesses better?" And so, that's probably ... again,

just like you, I probably have a couple. That one and

the other one would be the whole investigation

process, the whole digging to find the insights. Once

you're in the project, you really with your heart and

soul in the project, it's really fun. You can get lost in



the project that time will fly by, you know that feeling,

right?

Zach Loertscher: Yeah, yeah, totally. Yeah. Yeah, you can spend a lot of

time in the exploration phase but it's just 'cause it's so

exciting and fun and new and you never know what

you'll find.

Kirill Eremenko: Yeah, for sure. What's your least favorite?

Zach Loertscher: My least favorite part.


Zach Loertscher: I'm gonna go out on a limb here, I know a lot of people

get into Data Scientist from a developer background so

maybe I'll get a lot of flack for this but probably the

programming, that's gotta be my least favorite part.

And maybe it's also because I'm coming from a more

business analytics side of things where I want my tools

to be on AGUI and nice and easy to use. I don't like to

worry about the syntax or spend forever on google

finding things. That being said, I realize that there's so

much power in being able to have control, exact

control over your data, over your project and your

work flow, but you really can't have inside of AGUI, so

something like, I don't know, Tableau. It's an

incredible tool but at some point it has limitations, or

excel, it can be hard to automate sometimes. That's

probably my least favorite part, but I recognize that,

that's also one of the most powerful parts too.

Kirill Eremenko: Yeah, gotcha. I agree, I like programming but I don't

like the getting lost in the specifics of certain

algorithms.




Kirill Eremenko: Sometimes you gotta remember to add this line, you

gotta remember this part of the algorithm, and this

hyper parameter, the tweaking of these statements.

So, I like creative programming when you can just

come up with stuff and make things, like even you're

writing your own algorithm. That's really fun, you're

writing something that's ... but sometimes when it gets

too mechanical and you forget something, the

debugging of the code, that can be quite tedious.

Especially if you forget or you don't notice that there's

an error in the code, not because you're approached,

but just because you forgot something that's part of an

algorithm. Definitely then that can be a bit tedious.


Kirill Eremenko: Yeah, that's what I'd say.

Zach Loertscher: And to anybody who's listening who's maybe at the

initial point of this learning curve, for me, that

learning curve's been more of a brick wall that I

haven't really climbed up it, I've more smashed into it

as much as I could until I finally have broken through,

at least a little bit I think. If it's hard for you, it's been

hard for me too, so don't worry.

Kirill Eremenko: Yeah, yeah. Well, yeah exactly. At the end of the day, if

somebody really doesn't enjoy programming, there's so

many other ways you can be a Data Scientist, even

without programming. Like you said, there's so many

great tools like Tableau for instance, where it can add

so much value programming a single line of code.

Zach Loertscher: Yeah, absolutely.



Kirill Eremenko: Yeah. Okay, well let me ask you one more question

before we wrap up. What career aspirations do you

have? I know you're in the health industry now and

you're starting out as a Data Scientist, is there

anything you're aspiring towards? You want to be a

Chief Data Officer, or Chief Data Scientist? I don't

know, do you want to maybe have your own business

one day? Or do you want to be an executive, like a

Chief Executive Officer that uses data science? I don't

know, I've mentioned a few executive positions, I don't

know why, maybe there's other roles that you're more

interested in, but what's on your list of aspirations?

Zach Loertscher: I would say I'm pretty easy to please, I don't really

aspire to super, super high level positions because I

want to always maintain a good work life balance. I

never want my work to become more important than

my family or taking care of my wife, whatever it might

be. So I'd say the biggest aspiration for me is just to

find a position in which I get to do data science and

also maintain that all in balance. And maybe that'll

involve some give and take with things like pay and

salary, but that's ultimately my goal because I feel

there are a lot of things in life that bring happiness

besides prestige or position or money. For me, the title

isn't super important as much as the work life

balance. And also, is really important, is the people I'm

working with. Are they collaborative? Are they excited

to be at work? Or is it a team that just shows up and

just does what their told and then goes home, you

know what I mean?




Zach Loertscher: Those are probably the two most important parts for

me, the title can come later I think. That would be my

highest aspiration.

Kirill Eremenko: Gotcha, gotcha. Fair enough, that's a very fair answer

and it's good to hear you got your priorities sorted out

in a nice way. Okay, well Zach, thank you so much,

enjoyed the new kind of format of this episode. Before

you go, where can our listeners contact you, get in

touch, follow you, learn more about how your career

progresses?

Zach Loertscher: The best place is LinkedIn, so just look me up on there

and connect with me, I won't reject you, I promise. The

listeners of this podcast are great people, I'm sure, as

well as everyone else, so that would be the best place

to get in touch with me.

Kirill Eremenko: Gotcha. All right, and Zach's got a very interesting

spelling of his surname, so we'll include the link to

your profile on the show notes, people can connect

with you there. Okay and one more question for today,

what's a book that you can recommend to our listeners

to help them enhance their career?

Zach Loertscher: It's not necessarily a data thing, okay, it isn't a data

science book and maybe it's been recommended

before, but I love the book, How to Win Friends and

Influence People, by Dale Carnegie. It's a little bit of an

older book but it is probably one of the best books

about forming relationships with people and really

learning how to, I don't know, not just win friends and

gain influence, but really, I don't know, build those

relationships. Because that can be one of the most

satisfying parts of your work, of your life. Even when it



comes to the data science part of things, it will help

you with communicating things like Kirill has

mentioned earlier, breaking complex things down to

make them simple and making you less of a robot and

more of a human, if that makes sense. That book has

had a big influence for me and I hope that anyone

who's listening goes out and reads it, it's awesome.

Kirill Eremenko: Definitely, fantastic book. I also recommend it and it's

come up three times today.

Zach Loertscher: Oh really?

Kirill Eremenko: Yeah, definitely worth picking up. All right Zach, thank

you so much for coming on the show. I hope our

listeners enjoyed our chat, I definitely did, it was a

nice, pleasant conversation and I think there's quite a

bit of value. I definitely learned a few things from you,

thanks so much.

Zach Loertscher: Yeah, I learned a lot from you. Thank you so much for

having me on the show.

Kirill Eremenko: There you have it, that was Zach Loertscher, an

aspiring Data Scientist and we discussed quite a lot of

different things. Hope you enjoyed the show, and a bit

of a different format this time where I answered a few

of Zach's questions. My favorite part probably was

when Zach talked about the way he goes about or the

way he thinks about selecting an industry to get into,

that it's important to speak to someone who's already

in there, or who got into there recently or is getting

into there. So that you can get their perspective, you

get some insider knowledge about that industry and

you make the right choice about your career.



On that note, make sure to get in touch with Zach, you

can find his LinkedIn URL at the show notes at

www.superdatascience.com/177, where you'll also find

the transcript for this episode and any other materials

that we mentioned. I'm sure Zach's gonna post some

very cool and interesting updates in the coming future,

and of course he'll be happy for you to get in touch

and answer any of your questions you have about

building a data science career and learning data

science for yourself. On that note, thank you so much

for being here today. Can't wait to see you back here

next time, until then, happy analyzing.


SDS PODCAST EPISODE 177 WITH ZACH LOERTSCHER › uploads › … · does a lot of data warehousing...

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