- Hello and welcome to
Experian's Weekly Data Talk.
A show featuring some
of the smartest people
working in data science.
You know, in most of our
shows we're talking about
the technical aspects of data
but today is a little bit different.
Today we're talking about
the art of data science
and in fact, today's topic
was inspired by a blog post
that Eric Weber wrote on LinkedIn.
And if you guys aren't
familiar with Eric Weber,
he is the Senior Data
Scientist over at LinkedIn.
He's also an advisor to the
Master of Science Business
Analytics program at the
University of Minnesota.
And just to give you a
quick little background
on his academic background,
Eric got his Master of Science
degree in Business Analytics
from the University
of Minnesota and then his
PhD in Mathematics Education
from the Arizona State University.
I wanna encourage you all
to follow him on LinkedIn.
The short URL to find
him is ex.pn/ericweber.
Eric, thank you so much
for being our guest today.
- I'm happy to be here and that
we're both on the West Coast
so 8 a.m. so for maybe if
people are watching this from
the East Coast happy late
morning, almost early afternoon.
- That's right.
(laughing)
We found out before the chat that Eric
and I are both early birds.
We love getting into
the office really early
so this is like our peak time.
- Yeah, everybody who's
watching is thinking, what?
8:00 in the morning.
(laughing)
- Exactly.
So Eric, I thought it'd be great
if you can kinda share
with us your journey
and what led you to start
working in data science.
- Yeah, absolutely I'm happy to do that.
I think most people in this
field, they would almost all say
they had a non-traditional
route because I don't think
there is a traditional one
at this moment in time.
I started out and I've
always been passionate
about education and I think that continues
to be sort of an undercurrent
to everything that I do.
So I've, along the way, undergrad
I focused in mathematics.
I focused in grad school
on mathematics education.
And all throughout this I've
continued to have a passion
for teaching, helping other learn
and helping to scale that impact.
And so as my interest of
it developed over time,
from let's say, starting
in the education sphere
to teaching small classes,
to my time at the University
of Minnesota where I was
teaching in a lot larger
lecture courses that still
topped out 150, 200 students.
- Wow.
- And I mean, it's big but
when you think about the level
of impact, you're thinking, okay,
I can affect these hundreds of students.
But there's still an upper
limit on what you're doing.
During that time when I was at Minnesota
I had the good fortune to
be able to be part of the
University of Minnesota the
business analytics program.
The first, part-time cohort
that went through the program.
And to put it in short, I was
just fascinated by analytics,
by data science, by the power that you
seemingly have at your fingertips.
Which I think still sort of blows me away
when I'm doing work.
And after I finished
going through that program
I had a lot of open spaces in front of me.
The question was, do I stay
in the academic setting
where I continue to love teaching.
Even being at LinkedIn,
I do miss the day-to-day
teaching component of things.
But I had the chance to come to LinkedIn
specifically to work on
their learning product.
And so it's the newest venture here.
Newest product that certainly
is a little bit different.
But we acquired Lender.com
about two years ago now.
And so a lot of what we
focus on is trying to
help professionals
develop the right skills.
Help them to develop the
right skills that they need
to get the jobs they want.
Develop in the jobs they want.
And really advance their careers.
And a big deal for me was the
chance to do this at scale.
And I think that's something that we have
a chance to do with LinkedIn.
And LinkedIn Learning
specifically, we have the advantage
of having this large network
that's already built.
This exchange of ideas
between professionals.
And I think we're just getting started.
And that sort of brings me
to today where that's my
focus everyday is trying
to expand the impact
as a part of our sales team
of Arlington Learning product.
- Well I think what's really cool, Eric,
is that kind of your passion
for teaching and education
and data science is kind
of merging together for you
at LinkedIn even though you don't have
100 students in front of you.
You're now educating possibly
thousands or millions
of people just through the
products you're helping
serve up to the right people, right?
- Right, and I think
that's part of the reason
that I care so much about what
I do today and this is why
when I tell people, find
what your passion is.
Some people are passionate
in general about data science
and that's awesome.
I'm passionate about data
science but specifically
about trying to help other people learn
and that sort of opens up unique paths.
And I think, well you
can get into that later.
But you gotta find what you care about.
Because data science is a
whole bunch of opportunity.
You just need to decide exactly what
your opportunities should look like.
- Eric, can you share a
little bit about some of
the data science work
that you do at LinkedIn?
- Yeah, sure.
So without going into details that will
open up anything about the company.
(laughing)
I always say, hmmm, imagine
that your lawyers are watching.
- Exactly.
(laughing)
- So generally speaking, I
work on LinkedIn Learning.
And I specifically support our sales team.
So at the end of the day,
if you wanted to describe
what I do in a relatively simple way,
everything that I build and
all the insights that I try
and provide are directed
toward making our sales team
more efficient, helping our sales team
understand our customers better.
And in general, for right
now as6 we're building a new
business, trying to help our
sales team find what the right
profile is for people that we
wanna bring in to the fold.
And I think that's a fascinating
part, especially when
you're starting a new venture,
is you get to see things like
customer segmentation that
other people might find
relatively boring, I think
it's really fascinating.
Because segmentation opens
up all these insights
that the sales team my
not have otherwise had.
And so at the end of the day I'm trying to
help our sales team be more efficient.
That can include simple
data pulls and insights.
It can include building a more advanced
and scalable machine
learning models over time.
But it kind of involves,
sometimes when I go into work,
I never know what it's going
to be on that particular day.
But as long as it serves the
purpose of making the team
more efficient, that's what I do.
- That's awesome, Eric.
And I gotta say, just over
the last year, I've seen leaps
and bounds in the types of products
and things going on within LinkedIn.
I mean the communities are forming.
I'm seeing great discussions.
I feel like LinkedIn has
really changed for the better.
I joined LinkedIn I think when
it probably a year after it
first started and it was more,
kind of, a resume type site.
And now, just in the last year,
all the different learning modules,
adding the skillsets and the stream.
It almost feels like a business Facebook.
I'm seeing really great discussions
happening in the stream.
- Yeah, it's a.
So for me, I mean I was a similar way.
I've probably been on LinkedIn
maybe since 2010, 2011.
But the way that I think about it
has changed pretty dramatically over time.
And I think the way that a lot of people
think about LinkedIn
has changed dramatically
even over the last year or two.
And that's intentional, right.
We wanna move beyond this
concept of a place where you
store your information so
that recruiters can find you.
That's certainly an important
part of our business
but there's also this community
building aspect to it.
And some people, you know, I
spend a lot of time reading
through my feed, getting
people's perspectives on things.
And I think, in a lot of
ways, given the outside world
and things that are going on.
And how Facebook and other
social media platforms operate,
people like to be in a place where
people focus on being professional.
And to me that's a cool thing.
People have this, it's not a written rule,
but there's sort of an unwritten rule of,
you're going to share stuff here,
keep it focused on professional things.
And I think that's an awesome
unwritten rule to have.
- You know, it was because I
was reading through the streams
that I came across you and your article
on the importance of
creativity in data science.
I wanna talk with you a
little bit about that.
Why is creativity so important
for a data scientist to have?
- So I think there's a lot of
ways I could respond to this.
The most direct reason for
me is that when companies
think about hiring data
scientists, they often have ideas
about what they want them to do, right?
They know they want them to be able to
organize and pull data.
In some cases be glorified analysts.
In other cases build more
advanced machine learning models
to do particular tasks.
But the important part there
is, the company is so familiar
with their data, well, in
some cases they are, that they
already have this set of tasks
that they want to accomplish.
So in a lot of ways, they don't
know what they don't know.
So they're missing out on what's possible.
And very often if you're not in this field
and you're not working
in a specific business
or type of contacts day-to-day,
it's difficult to know
what you're missing out on.
And so often, that creativity part comes
from the data scientist, him or herself.
They need to be able to
see maybe what's missing.
Be able to pick up on how things
could be done differently.
Be able to envision what
something might look like
two or three years down the
road and start that process
or data pipeline development early
in order to accomplish
those sort of goals.
And so creativity, I think,
there's a lot of aspects to it.
One is being able to think
about things operating
in a different way than they currently do.
And, of course, from a business
side, I think if you're
on a sea level or something
else, it's hard to,
you don't dig into the
data enough day-to-day.
You don't dig into the
predictions enough day-to-day
to understand how it could be different.
But the data scientist can
and I think that's add a lot of value.
Is not just in building
models and pulling data
but in trying to actually
produce innovation
for the business and innovative ideas.
- Yeah, and I think to
your point, I love the fact
that you're talking about just
how creativity from the very,
very start from just being curious
and how can we change things
from what the norm was?
From crafting the right
questions to, like you said,
what type of data should I be pulling?
Like all that is definitely an art.
- Right and I think that's the hard part.
When you go to look at
people's questions or what,
I don't know if we have a trending topics,
but the things get asked all the time are,
okay, how do I get into data science?
Like what are the hard and fast
skills that I need to have?
And certainly there are
a base set of skills.
You need to be able to
manipulate data, SQL, Python, R,
whatever it might be.
You probably need to be able
to build some predicted models.
You probably need to be able to
communicate with other people.
But the art part of that is how you
combine those different things.
Like not everybody's going
to do it in the same way.
Every particular task
or project that you do
is probably going to
require you to leverage
different parts of your skillset.
And so it is an art.
So you can be ready and you
can be ready to go produce art.
But I can't tell you how
to go paint something.
First of all, you don't
want me painting anything.
(laughing)
But I think it's a very.
And so people are often frustrated.
They're like, well I want the solution.
And people that are data
focused want sort of a hard
and fast or rule based system
to becoming a data scientist
and knowing how to do it.
When, in fact, that
depends on a lot of factors
that are project specific
or company specific.
- Eric, can you share
maybe one or two examples
of how creativity has
helped you in your work?
Either at LinkedIn or previously
on projects you've done.
- Yeah, absolutely.
So I think in the case where
I work with our sales team,
right, they care a lot about
being able to identify new customers.
Being able to identify a
business that they should pursue.
And I won't go into a ton
of detail but often there's,
on the business side, they
have, how do I put it?
They have real ground level expertise.
And so they've developed
a good, they've developed
ideas about what works
and what doesn't over time
and what signals are about
who they should pursue.
And often the data can tell a little bit
different story about
what those signals are.
And so you end up having
these two things, right?
You have maybe the business
side sense of what's important
and you have the data
side of what's important.
And the truth of the matter
is probably somewhere in the middle.
I don't think anyone's necessarily wrong.
They're just seeing different signals.
And so it's trying to be creative
about figuring out, okay,
you think this, I think this,
so I don't think either of us
is necessarily wrong, it's
just a question that we're
probably seeing something
different with the data.
And so it's that creativity
part and creativity, of course,
necessitates things
like being very driven.
You need to want to see these
things through to completion.
It requires patience.
Trying to find that true middle.
And I think that's true of anyone
that's working within a business context.
Being able to find that
truth that's in the middle,
it often requires really
creative approaches.
Not just to problem-solving
but to how you communicate
your own results with the business side.
I think the other thing is,
goes what I said before,
in trying to innovate.
Trying to say, alright, this is how we've
done things in the past.
This is how we've
organized the sales team.
What if we do this a different way?
What if we think about our
account strategy in this way
as opposed to this one?
Trying to actually do things
like trying to do a test,
for example, of how effective
a particular sale approach is
or something like that.
And so for data scientists,
the notion of A/B testing
is very straightforward for most.
And they think about it
in the sense of a product.
Where some users are served up
this new version of a feature
while others are served up
an older version of a feature
and you compare their behavior.
But when it comes to looking
at things like marketing
or things like sales, the idea was,
an A/B test is a little bit different.
'Cause ideas like randomization
and ideas like being able to
like, what are you measuring?
They become a little more complicated.
And a lot of creativity
is required in that sense.
And I've learned a lot about
that even from the other people
on our LinkedIn sales
teams and analytics teams
about how to think about these issues.
So those are just two examples.
I mean, I think it's
probably true every day
that I could pick an example
of where creativity matters
but those are two big ones for me.
- Mmm hmm, Eric, I gotta
say that the first example
you shared was really cool.
How much respect you have for
the business intuition side
based on somebody who knows the business
and has been in the
business for a long time.
They're sharing their insight with you
and then you're looking at the data
and maybe coming up with
a different point of view.
And you're like, okay, how
do both of these points meet?
Like, it's not an either
or, it's a yes and
and let's find a balance.
Let's see how we can
learn from each other.
- Yeah, I think, I mean
to me, that's key, right?
When you are in a data driven position
I think a quick way to
get yourself out the door
is to say you know everything.
Because in reality, I don't
know as much about sales
as someone who's done sales for 10 years.
I might know more about
the data but again,
if I think about the data or
what I'm seeing is kind of
an end product, like I see the
results of the sales process,
but I don't know what
it's like always to be
on the ground and to
be doing these things.
So there's a certain
measure of respect for
the business side and I
think this is true regardless
of the industry that
you go into, is people,
I would find it concerning
if a data scientist walked in
and said, here's the story,
this is really what's going on,
without taking the time to
assess what the business thinks.
To me, I mean, you are delivering
value for what you can add
to the business so it's silly to ignore
what the business actually thinks.
(laughing)
- It sounds like that humility
is a big part of this, right?
- Yeah.
I mean, I think, I couldn't
name names but who I've read
make posts about the importance
of humility but when you,
with data, I think sometimes
you can feel like powerful.
Like I have all the insight.
I can see everything.
But in reality, like with
data, you don't see everything.
You see a pretty limited
slice of the actual business.
It's whatever you're choosing to measure
at that particular time.
And so understanding that
you probably don't have
a full sense of what's
going on is important.
It doesn't mean that you're
not good at what you do.
It just means that you're being real.
And that's something that I
think when I first got into
data science, I think I,
when I was building models
I was thinking, alright, I
have developed the solution.
Like, this will solve all the problems.
Probably not.
It's probably not going
to solve all the problems
but it might move the
business forward a little bit.
And I think having that
sense of, I can do something
and you're a part of a team
effort is more important.
When I interview people and I
ask them questions about how
they would interact with the
business side, I look for that.
I care about, do you
have a sense of humility
that you don't know everything.
But sometimes rare to find, actually.
- Okay, would you say
that part of that humility
for you came from academia?
Like as you were learning and realizing
all the things you didn't know?
Would you say that that's fair?
- Yeah, I think that's a good point.
I mean, I, as an example
when I was at Minnesota
I was teaching statistics and
about design of experiments
and clinical trials in the
biostatistics department
at Minnesota and my office
was next to people who were
world class people who
were running these trials
in foreign countries during major breaks.
Like when we had the
Ebola crisis in Africa,
the people who were on
the ground doing the
clinical trial tests for vaccines
were from that department.
So it's really humbling to be
like, no matter what I know,
there're gonna be people who have
10 times the knowledge I do.
And I think it's a pretty
humbling experience.
And so academia certainly,
does it put you in your place?
I think probably.
(laughing)
And while the sales side that I work with,
they don't have such a
thing as a tenured professor
but you figure out who the
tenured professors are, right?
Okay, this person has
essentially ruled the sales world
for like 15 years and they know.
(laughing)
And so there's that
measure of respect saying,
I'm not gonna try to tell you
that you don't know what
you're talking about.
I'm just gonna figure out how
to help you wherever I can.
- Eric, one last question with
regards to art and creativity
and that is, I read a stat
in Forbes that was published
two years ago that said like
76% of data scientists view
data preparation as the least
enjoyable part of their job.
So I'm curious about maybe those
tedious aspects of data science.
How do you stay creative and curious?
How would you encourage someone to be
creative during those tedious moments?
- Well one, I'm surprised it's only 76.
(laughing)
But, here's how I think about it.
Everybody wants to build the fancy model.
Everybody wants to do
something hugely impactful.
But you're only as good as
the data that you're using.
So if your pipeline's not good,
if the data transformation
process is not good,
if your data cleaning process is not good,
there's a lot of issues that come up.
Because the model that you generate,
the insight that you generate
maybe doesn't have a strong
foundation that it needs.
And so it's like eating your vegetables.
Actually, that's not a good analogy
because I love vegetables.
(laughing)
Huge fans, right?
And it's this conditional
part that's gonna make
everything else you do really
impactful and reliable.
And so I think it might get
overlooked in some cases
but it feels very bad to
imagine that you're in
a sales scenario and you're
telling the sales team something
that is surprising to them or
maybe that they don't agree
with or that goes against
conventional wisdom.
And the last thing you
want to do when they say,
well do you trust the
data that you're using,
is to be uncertain at that point.
You've done a good job
establishing that pipeline,
doing data transformation,
cleaning, you're not to worried.
But if you haven't, that's the time
where things start to feel very shaky.
And let's say that they go
into your data and they say,
well, hey, I found this issue
with what you were doing.
And that can, I think in a lot of ways,
poke holes in how much
they trust the insights
and the things that you're developing.
So to me, you're not gonna be able to do
the fun stuff without doing this.
Sure 76%, 80% whatever it
is, maybe the least fun thing
but it's probably one of
the most important things.
- Well we only have a couple minutes left
so I just wanna ask a
couple quick questions.
The first one would be, what
would be your advice for those
that are watching, those that
are listening to the podcast,
aspiring data scientists, they
wanna get into the industry,
what would be your advice to
them on how to get started?
- I think it's a couple
things, two points of advice.
One is that you need to figure
out what your passion is.
What do you like doing?
If you're in data science
for the paycheck or the title
or something else like that,
you're going to burn out pretty quickly.
The reason is that the work is enjoyable
if it's the data science
part of it that you like.
If you're chasing these
other exter6nal things
I think it'd be very difficult for someone
to really love that life.
You're gonna get tired.
And so spend time assessing
what you care about doing.
Do you care about sales?
Do you care about marketing?
So you care about security?
Do you not necessarily care
about the field that you're in
but do you care about building
machine learning models?
Would you rather pulling data
and generating insights, using dashboards?
Depending on your interest area,
the position can look totally different.
So finding what you're
interested in matters a lot.
The second part is, be realistic.
I think what's really cool and frustrating
about getting into this field
is that there are incredibly
talented people in it.
Having an advanced degree
and having tons of training
in a programming language
and being a good communicator
are maybe necessary but not sufficient
for landing the job that you want.
Most likely the job search is
going to be mentally tough.
I think you're going to
get a lot of rejections.
I think I still have a hundred
rejection emails sitting
but I just stored them in
one folder in my email.
'Cause it's going to happen.
And so being realistic about the timeline
and also the position that
you're going to get in to.
Don't always assume that
you're going to walk in to
a high level data sciences
position in a company
because the people who are
in those high level positions
may have been there for
five years and they may be
high level research scientists that are
extraordinarily good at what they do.
You kinda have to be willing
to accept what you can get
and kinda build your way up from there.
And that definitely
required being realistic,
requires some humility.
I think that's an important
part of the job search process.
- I loved your advice.
Eric, one last question and that would be
for senior leaders who are
watching this broadcast.
They're looking to build
a data science team.
What would be your advice for them
on you hire for data scientists?
- Actually, a couple days
ago I wrote on LinkedIn
where I just put like a
top ten list of things
that I think companies need
to assess when they're hiring
for data scientists but I
think the first question there,
do you need one?
Data science is a really
hot field right now.
People, companies assume
if they don't have somebody
in that space that they're missing out.
But doing a needs assessment
on whether you actually need
to hire someone who's relatively expensive
and maybe hard to actually land in the end
is an important piece
before you do anything else.
And then it depends on the
maturity of the organization.
If you have data scientists
that are advanced
and you have them in
house and they're okay
doing the hiring process, great.
But if not, getting the
right person in the door,
for data science, is
important enough that I think
it's crucial to be willing
to go outside for help.
To ask other people to
bring in a data scientist
from another company who
maybe does consulting,
is able to help you with
that hiring process.
And I think the last part, don't undersell
the importance of the
soft skills in the side.
I think people hear data
science, they think technical
but at the end of the day,
you can get the most technical
person and if they can't
sell what they're producing
or they can't explain it
in a pretty digestible way
to other people, it's
gonna be difficult for them
to succeed and be helpful in that company.
So those are some of the
key things I think about.
I didn't say interview about this language
or ask this question.
- (laughing) Yeah.
- Those are secondary to deciding
you need to this in the first place.
- I love that.
I wanna recommend that
everyone follow Eric Weber
on LinkedIn so you can catch his articles.
You can interact with him there.
You can find his LinkedIn
profiles simply by going to
ex.pn/ericweber and that's a short URL
that just redirects over
to his LinkedIn profile.
Follow him.
If you have further questions,
feel free to interact with him there.
You'll also find all of his blog posts.
I think you actually have a Google Doc
linking to all of them.
- I do.
Very, very advanced and sophisticated.
It's just how it is.
- Which was very helpful, is very helpful.
- Cool.
- So make sure you follow Eric there.
And also as a reminder, we have
this Data Talk every single
week where we talk about
different data science topics.
You can learn more about upcoming chats
as well as the podcast by
going to ex.pn/datatalk%.
Take care and we'll see you all next week.
Thanks, Eric.
- Thanks.
