SDS PODCAST

EPISODE 207

WITH

KRISTEN KEHRER

Kirill Eremenko: This is episode number 207 with founder of Data

Moves Me, Kristen Kehrer. Welcome to the Super Data

Science podcast. My name is Kirill Eremenko, Data

Science Coach and lifestyle entrepreneur. And each

week, we bring you an 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 to the Super Data Science podcast, ladies

and gentlemen. Super excited to have you back on this

show. And today, we've got a very inspiring guest,

Kristen Kehrer, a data scientist with 10 years of

experience, a data science influencer, a future data

science author, a co-host of Data Science podcast and

many, many more exciting roles that Kristen fulfills in

the space of data science in the way she gives back to

the community. And in fact, Kristen was one of the

speakers at DataScienceGo 2018, and her talk was full

of energy.

Kirill Eremenko: There was lots of excitement, lots of people came up to

Kristen after her talk. And today, she is here on the

podcast to share her journey in the space of data

science with us. And in this podcast, you'll find a lot of

valuable tips. You'll find out how and why Kristen uses

certain data science tools from SQL, to R, to Python, to

big data tools, visualization tools. You'll also find out

why Kristen uses R sometimes, and why she uses

Python sometimes, and why Kristen recommends to

make sure that you know both of these tools and what

each one of them is good for.

Kirill Eremenko: You'll also hear some valuable career hacks and tips,

whether you're just starting out into data science or

whether you're an advanced data scientist. You'll find

hacks on what technical skills actually add value to

businesses and are quite easy to learn. You will find

out what to do about your soft skills, how to give back

to the community, and in fact, how to better structure

your resume. And in fact, in terms of that the last one,

you'll find a special surprise waiting for you towards

the end of this podcast, Kristen shared something

exciting with us in terms of her course on building a

resume.

Kirill Eremenko: So lots and lots of value for all level of data scientists

and lots of energy from Kristen Kehrer right here on

the show coming up just now. So without further ado,

I bring to you Kristen Kehrer, founder of Data Moves

Me.

Kirill Eremenko: Welcome Ladies and gentlemen to the Super Data

Science podcast. This is going to be fun because we

were just recording this podcast with Kristen and then

my computer crashed, so this has got to be our second

attempt at it. Kristen, my huge apologies for that, but

it was so much fun. It was this great energy. So let's

recreate that from the start. How are you feeling about

that?

Kristen Kehrer: I'm feeling great. Let's do it.

Kirill Eremenko: Awesome. Okay. All right. I believe we started off by

me complimenting your amazing energy at

DataScienceGo and how you were inspiring everybody.

You brought in so much positivity to the event. How

did you feel about everything that happened there?

Kristen Kehrer: Oh my God. I thought it was amazing. It was fantastic

to meet people who I've been building relationships

online with for the last couple months, to meet them

and give them a hug or other people that I've been

interacting with on their posts and really get an

opportunity to meet them and connect, and everyone

was warm and friendly and the energy was incredible

the whole weekend.

Kirill Eremenko: Yeah. Thank you for the compliments. The amount of

energy you brought was just incredible. I think your

talk was one of the ones where people are like laughing

the most and having a really, really great time. That

was really cool to hear and see. Just for our listeners,

for the sake of our listeners, Kristen does a lot of

things in the space of giving back to the community of

data scientists. Kristen writes her own blog posts,

you've got webinars that you run. You've got these

sessions with Favio Vázquez. You appear on podcasts,

you're writing a book with Kate Strachnyi was on the

Super Data Science podcasts just not that long ago.

Kirill Eremenko: You're just generally helping people, speaking at

conferences, and you have your own website with a

course on it. So that is very, very exciting. I want to

ask you, where do you find the motivation and energy

to do that?

Kristen Kehrer: Yeah, it comes from true passion. I work 9:00 to 5:00,

and everyone says that after you have kids you have

less time, but that just hasn't been the case for me

because my kids go to bed at 8:00 pm and then I have

from 8:00 to 11:00 to work on other initiatives, and I'm

not a TV watcher so that's what I'm doing. Like last

night, I was turning a video into an image data set so

that I can start doing some object detection in my free

time. The people that are just so much fun and I like

taking part in data science office hours with Terry

Singh and Kate Strachnyi and Favio and some others.

Kristen Kehrer: I'm building these amazing relationships, and it's not

like I'm coming from a place of I need to give back. It's

that I just am because because it's so much fun. It's

really like my purpose.

Kirill Eremenko: So it's something that you enjoy and you actually want

to do?

Kristen Kehrer: Yes. 100%.

Kirill Eremenko: That's very cool. Would you say that, I believe I asked

you this question and I think it's an important one so

I'll ask it again. Would you say that's you have to have

like in your case, 10 years of experience and be an

expert at something to be able to give back? Or do you

think that anybody who's even starting out in the field

has the capacity to give back and help others?

Kristen Kehrer: Anyone in the field absolutely has, or not even in the

field. If you are in school and you learn something

cool, share that with others. There are people who

want to read it. The LinkedIn community is incredibly

welcoming, put yourself out there and you're going to

be so pleasantly surprised with the response that you

get. It is a little bit making yourself vulnerable to put

yourself out there, but you absolutely have something

to share with those who are not learning the same

stuff.

Kristen Kehrer: You could be studying at a different school than

somebody else and learning the material in another

way, and you may help somebody better understand

an algorithm or something else, or you may give them

that aha moment that really helps someone.

Kirill Eremenko: Got you. What I like about your approach is that you

work in collaboration with other data scientists as

well. So in addition to giving back on your own, you've

taken it to the next level and you have these are

webinars with Favio and you're writing a book with

Kate. Tell us a bit about that, how do you go about

finding these partnerships, working to maintain them

and create projects together?

Kristen Kehrer: It's all been pretty organic. It's like Kate Strachnyi had

posted on LinkedIn forever go, "Hey, I'm doing Humans

of Data Science. Comments here if you want to take

part." So I commented and when it was my turn to be

on Humans of Data Science, which was open to

anyone. You could have been a first year data science

student. I met Kate and we have an incredible

friendship now, I'm not overselling it. I'm actually

traveling to her house in New York next weekend and

I'm going to spend the night.

Kristen Kehrer: I've made friendships on LinkedIn. And with Favio we

were in this group chat and we just started talking

about similar things. We started talking some more

and decided to launch our webinar series together. I

come from that mentality of, put it out there,. I don't

overthink anything too much, like if somebody has a

great idea, I'm just typically like I'm in, within the

construct of like I definitely set healthy boundaries for

myself and that way I'm always able to meet my

deadlines, but if I have time for something and I can fit

it in and it's exciting, I'm just the type of person who's

going to go for it.

Kirill Eremenko: That's a very cool way of putting it. So just like

network and connect with people online, chat, and

when you find someone with similar interests, grab the

opportunity by its horns and give it a chance, right?

Like if somebody is suggesting something, you don't

have to commit to a year of work together, but like give

it a go and see how it works out. And if the first time

you guys are able to create something that gives value

to other people, why not continue, right?

Kristen Kehrer: Yeah. Actually, that's totally how my blog started. So

my friend Jonathan Nolis, he's also a data scientist, I

noticed him getting active on LinkedIn and I texted

them and I was like, "What are you up to Mr. LinkedIn

social guy?" And he was like, "You should write a blog

article." And I was like, "Okay." I launched my first

blog article in March and now I get a lot of shares and

a lot of likes on my blog articles and I haven't been

doing it for very long.

Kirill Eremenko: That's amazing. Just for all listeners, we're actually

talking like massive, massive growth and impact.

That's how in demand this space is, and that's how

much people are hungry for help and knowledge in

this space. Kristen started in March blogging, and now

she has, Kristen you have 13,000 followers on

LinkedIn. In fact, congratulations. It just went over

13,000 as we were speaking. That's awesome.

Kristen Kehrer: Thanks.

Kirill Eremenko: That's so cool. And so when you say blogs, you don't

have to have ... I think you have your own ... You blog

on your website, but people can just blog on LinkedIn

as well. Is that correct?

Kristen Kehrer: Absolutely. You can create LinkedIn articles. My first

article I ever posted was actually on Medium first. I

sent that article into Towards Data Science, it got

rejected and then so I just submitted my next article

Towards Data Science and that one got accepted. And

so now anything I write can go in Towards Data

Science, and I would like to say that my first article

was awesome.

Kirill Eremenko: Nice. What was it about?

Kristen Kehrer: It was about using segmentation to learn, and how in

business, oftentimes you'll hear people say, "I want us

to do a segmentation. Can these be the segments?"

And it's like, "No, we should use an unsupervised

algorithm." At least that's absolutely my preference.

The algorithm decides what the natural groupings are,

or at least have an understanding of what your natural

groupings are. There's also a lot of times where I've

heard in multiple companies that I've worked for, "Hey,

we've done this market segmentation, can you tie

these people back to our actual customers?"

Kristen Kehrer: And the answer is, "No. Like, I don't have the survey

questions that you asked for my whole dataset. But I

can build you a segmentation on your internal data,

and if you want we can append third party data." And I

was also talking about like really thinking about

creative ways that you can come up with new

variables. And in one of those I mentioned, one of the

variables that I mentioned was like, "Can you

determine customers with the seasonal usage

pattern?" And then after I wrote that blog article, I

went on to find customers in our database who had

seasonal usage so that we can message to them

differently.

Kristen Kehrer: So instead of looking at somebody who has used less

than normal over the last couple months and thinking

that they're a retention risk, we're now able to market

to them differently, and say, "Hey, here's how you can

build your business in the off season." And that's

really helpful. I work for Constant Contact, they sell an

email marketing solution for small, medium, large

businesses, anyone who would benefit from email

marketing, but there's certainly people who, if you are

a ski resort or something, I don't know if a ski resort

would need it, but in the off season, it would be useful

to their business to continue thinking about list

building and continue thinking about how they can

stay front of mind in the eyes of their customers.

Kristen Kehrer: And so I feel like we're able to add value for these

people who sometimes go dormant for months at a

time.

Kirill Eremenko: That's a very, very interesting approach. I think that's

really very valuable. We actually were talking before

about techniques. Maybe this is a good opportunity for

us to revise the conversation. What would you say, you

have a whole array of techniques that you have

expertise in from time series analysis to forecasting

cluster analysis, segmentation, neural networks, text

analytics, survival analysis, full factorial MVT. What

would you say is the most valuable? And I really liked

how you mentioned previously that you've got, what

you like yourself, what you enjoy and what's useful to

the business? Do you mind sharing that with us again,

please?

Kristen Kehrer: Yeah, sure. So what I was saying is exactly ... like

there's things that I find super fun, and of course

when I was identifying seasonal customers that was

sort of like an off label use case for the model. And so

things that are a little bit more innovative and fun, like

that's really exciting to me, but a lot of the times where

I'm able to add the most value is in things like

multivariate test analysis, which isn't a skill that most

people have. I don't know that it's taught in a lot of the

data science programs. That's me just conjecturing, I

don't have any factual information on that.

Kristen Kehrer: But I haven't really found too many other people who

are well versed in MVT, so I'm able to teach that to

other analysts at Vista Print and I'm able to teach that

to other analysts and data scientists at Constant

Contact and that allows them to do multivariate tests

on their website and really be able to understand the

interactions that are going on there instead of doing

iterative A/B testing where of course you'd be like

losing some information. And so that's teaching other

people how to read a Novas and do this analysis and

that opens up more possibilities for in terms of testing.

Kirill Eremenko: Yeah. Could you give us like a sample application of

MVT. I don't know, some of us maybe who are not

familiar with the technique might be able to see the

value and then start learning it.

Kristen Kehrer: Yeah, sure. So like I said, if you do iterative A/B

testing, you're not able to see the interaction of certain

variables. So in a multivariate test, it might be

something like you are promoting a sale on the

website, and in what areas should you promote that

sale, right? Because all of the real estate on the

website is important, and if you are not promoting the

sale you could be mentioning other copy or promoting

other products. So let's just say this is a site wide sale

or something, and maybe you'll have that in the

marquee, which is like the header, maybe you have

that on a product page in like a little box.

Kristen Kehrer: There's just so many different areas of a website.

There's the header, the footer, the marquee, different

product tiles. And any of those tiles could be swapped

out. And so you'd basically be looking for what is the

optimal placement or combination of placements that

is best for promoting a sale that's either going to lead

to a higher conversion rate or a higher revenue.

Kirill Eremenko: So, you're kind of like a testing multiple changes at the

same time rather than one by one, or like two verses

two, like one versus one many times?

Kristen Kehrer: Right. Exactly. If the sale is either on or off in a certain

placement and there's four different placements you're

considering, then that's two the four. And so whatever

that is, 16, so you're testing 16 different things. So it's

on in placement one and off in the other three

placements, and all those permutations up until you

get to having all four on. Intuitively a lot of people

think like, "Oh, okay, if I have the sale on in four

different placements, that's going to be better than

only having it on in three." And that's actually not true

a lot of the time.

Kristen Kehrer: You can find an optimal way of placing that message

and freeing up other space to message to other things.

But the benefit of the MVT is that you learn of the

combinations and the interactions. Whereas in a split

test or even if your split test has multiple cells, so if

you have sell A, B, C and D and you're doing different

things, you're not understanding the interaction

between A, B and C. Whereas in a multivariate test,

you can actually get at what's the effect of the

interaction of these three things, having all three

things on at once versus having four independent

cells.

Kirill Eremenko: Yup. Makes sense. Thank you very much for the

example. You mentioned as well that the two things,

that there's something that is really valuable to the

business and I can see how this would be an extremely

valuable skill to bring into the business. But then you

said that there are things in data science that you are

most excited about. So what would you say out of

these skills that you have, out of these different

algorithms that you use, what would you say is the

one that you are most excited about?

Kristen Kehrer: Yeah. I honestly get excited to build any type of model.

Kirill Eremenko: That's a good thing to get excited about if you're a data

scientist.

Kristen Kehrer: Right now I'm working on a large cluster analysis that

I'm really excited about. For me, it really is. Data

science is both an art and a science and being able to

... the added complexity comes in when you think

about your output and does this make sense in terms

of the business question and really like trying iterating

and trying different things and finding that answer

that truly gets at the business question that's

actionable, that people will ... we can automate this

and tag people and build campaigns off of it. I just

enjoy it all, and I'm really enjoying the segmentation

that I'm currently working on.

Kirill Eremenko: Fantastic. In addition to a lot of different algorithms

and skills, techniques that you have, you know quite a

bit of tools. You're a very technical person in from my

perspective. You know SQL, R, Python, Tableau,

Hadoop as well. In fact two types of SQL. Could you

tell us, what would you say is the most important

foundational skill or tool out of all of those?

Kristen Kehrer: Yeah, so I always say SQL because even though every

day now I'm in Python and I'm writing my SQL queries

in Python, day one, if you're a data scientist and you

walk into a new company, they're going to say, "Here's

our data warehouse. This is where you're getting your

data from," and you can have all of the techniques in

the world to build models, but if you're not able to

access the data and pull it correctly in a way that

makes sense, then you're sort of stopped at the

starting point.

Kirill Eremenko: Totally, totally agree. When I was starting out at

Deloitte, that was the single most valuable skill that I

had. And I brought into the business, I think I actually

studied SQL before the interview quite extensively to

make sure like I know how to get the data out of their

databases to work with it. And SQL isn't that hard,

right? It doesn't take that long to learn.

Kristen Kehrer: No, absolutely not. I taught it, not taught. Well, I have

taught it, but I learned originally on the job, and it was

something where it was a skill I didn't have, it was on

the job description. and I reached out to the company

and I said, "You know, I don't have any experience

with SQL but I'm competent and I can learn." And I got

the job and they taught me SQL, and it wasn't very

long before I was up and running. Even at Vista Print

where I was managing people, I'd have reports that

would also come with no SQL experience, and there we

didn't have people come in and teach us.

Kristen Kehrer: So I learned with like an external consultant that

literally came in and taught a group of us SQL. But at

Vista Print, I was teaching people SQL and it was

literally just like sitting down, and it's, "Here's these

tables and this is the Schema, and this is how you

read the Schema and now we're going to do some

joins." And people get up and running really quickly.

It's not a huge barrier. Like if you're somebody who's

listening right now and you don't know SQL, like you

can go and take an online course and do some

Googling, and with some effort you can pick it up

relatively quickly.

Kirill Eremenko: Oh, fantastic. Yeah, I totally agree with that. SQL, a

very good skill. And also, I see that SQL, I'm assuming

Microsoft SQL and pose gridscale. So it's good idea to

know at least two types of SQL because this for

dominant types of SQL in the world. There's also

oracle and there is also a mySQL. And so out of the

four, it's good to know at least two, get you through a

lot of situations. And then I also know that you used

both R and Python. Can you tell us a bit about how

and why you used the two tools rather than sticking to

just one of them?

Kristen Kehrer: Yeah. I had started with just one tool, I started with R

in 2004, and this was before R Studio.

Kirill Eremenko: Whoa, before R Studio. I can't even imagine R without

R Studio.

Kristen Kehrer: R Studio didn't come out until like 2010.

Kirill Eremenko: Wow. That must have been hectic to type in all that

code into a word editor.

Kristen Kehrer: The editor. Yeah, it was definitely. R has gotten so

much easier. Like if you're new to R, you should be

really grateful that you're jumping in at this time

because the learning curve was rough back in the day.

That's where I started with all my modeling, but in my

master's degree, there wasn't as much ... I wasn't

working with a database, so there wasn't as much

manipulation to do. So y core strengths in R is really

the modeling piece, and then I started picking up

Python only about six months ago and so I'm sort of in

the middle of this identity crisis where I will do a lot of

my manipulation and cleaning and automating

different things in pandas and NumPy.

Kristen Kehrer: And then if I'm building a model, I will call rpy2 and

run an model in R through Python after I do the data

cleaning in Python.

Kirill Eremenko: That's definitely a bit of an identity crisis. But I would

say it's beneficial that you are constantly interacting

with the tools because like I've met people who are

very proficient in R, and then they start learning

Python, and then two years later they haven't used R

that much and they don't really remember how to use

it and they're not as confident. Like even if there's

something that ... Because some tools are good for

some things, other tools are good for others. R and

Python how both have their advantages. And so in

those cases, people would know even that R might

have an advantage of doing something, but because

they haven't used it for two years, they will still stick to

Python.

Kirill Eremenko: Would you agree that like by using them constantly,

both at the same time, you are maintaining this high

level of acumen and you can jump into either tool

whenever you need it?

Kristen Kehrer: Oh yeah. I have both open on my work laptop right

now and I will just go back and forth. Or if somebody

mentions the new R package on LinkedIn, checking

that out. I want to use the coolest, newest, shiniest

thing and it doesn't matter which tool it's in.

Kirill Eremenko: Definitely. And I hope this serves as a inspiration to

our listeners that ... A lot of time we get asked the

question, R versus Python, which one to learn? Well,

learn both. Start with the one that's ... try out both,

see the one that you feel better about and then just

learn them both. I would personally say that probably

Python is a bit easier to learn. What'd you think?

Kristen Kehrer: Oh, absolutely. In terms of data manipulation,

Python's very intuitive to pick up, but at the same

time, R has some modeling capabilities that are tried

and true, and those packages have been around for

awhile and Python's starting to catch up. But even just

a couple of months ago, they released auto ARIMA in

Python, but it had been available for a long time in R.

And so there are certain times where just the depth,

it's the breadth and the depth of statistical modeling in

R that can just land you in R sometimes.

Kirill Eremenko: Yeah, totally agree. So another skill that you have, an

interesting one on your list of skills, which as we can

see, is already building up quite a diverse list of skills

in terms of data science. Is Hadoop and Hive, so that's

us moving into the space of big data. Could you tell us

like how valuable is it to have those skills? How

valuable has it been for your career to know how to

deal with big data?

Kristen Kehrer: I think it's been super valuable in a number of

different ways, and one of them is just simply that I

don't need to speak to the big data team if I think of a

variable that, or someone asks a question, if one of my

stakeholders asks a question and I know that that

data is available in the big data environment, I don't

need to ask somebody else to get it for me. I'm not

waiting on somebody else, or nothing's going to hold

me up when I'm trying to access all the data that I

might need for a model.

Kristen Kehrer: So that's been super useful. And then I think part of it

too is we hear big data and I had been hanging out in

the regular data world for a while and these things

become sort of big in your head like, "Oh, that person

... Everyone's talking about big data, and so you think

it's going to be this like thing that's scary or

intimidating and it's not. Like Hive is very similar to

SQL once you figure out how to access the big data

environment, like you can really easily start querying

that and getting results back in and it intuitively

makes sense if you already have the SQL knowledge.

Kirill Eremenko: That's very inspiring to hear. If people are interested in

big data, it's probably a good idea to check it out to at

least as you say, have that level of knowledge that

allows you to go in and get the data that you're looking

for and deal with these tools and learn them on the go.

So once you have that initial interaction with big data,

you see that it's not actually that scary, it's not that

different to SQL then that'll be helpful. Like personally,

I've worked with big data on the job using Greenplum

and with one of their consultants, we were going

through these things and indeed, it has its own

specifics, but at the same time, you can quite quickly

get your head around, not in extreme depths of the

topic, becoming a big data expert, but to have that

skill, to be confident that you won't get lost when you

need it. I think that's very useful for everybody.

Kristen Kehrer: I'm not setting up a cluster or anything.

Kirill Eremenko: Yeah Got you. And then let's quickly chats about

visualization. So that's another skill that you highlight

that you have in terms of data science and indeed your

talk at DataScienceGo was on killer presentations,

bringing model output to live of data storytelling. I

don't know, it was almost an hour talk or we're not

going to go through the whole thing now, but can you

get us some of the biggest takeaways? Why is data

storytelling such an important skill for data scientists?

Kristen Kehrer: We get this reputation that we are the person who's

going to try and solve this problem. We go and hide in

the corner for six months and then we emerge and we

try and explain our results to the business and to our

stakeholders in a way that they don't understand. And

a lot of these algorithms that we're building, the first

one that I start with is a neural net that I had built in

2011, and how I presented it to the business. And that

was showing them a bunch of functions that wasn't

going to land with the audience because these were

people who were nontechnical.

Kristen Kehrer: And instead of explaining it to the business in terms of

functions that they don't know what a Sigmoid

function is or maybe they've seen the graphs, but they

certainly don't need to see the function, I can bring

that to life by showing them examples of certain days

that I had forecasted, and what day is the forecast fell

apart because there was a popup thunderstorm or

what days the model performed particularly well. And

really bring to light like, "Okay, I built this model and

this is when it works the best. Here's some things that

we need to consider and when it's not going to work as

optimally."

Kristen Kehrer: And I can just show them nice intuitive graphs, or

even when I just talking about identifying customers

with seasonal usage patterns, I wasn't talking about

four year transforms. It was, "Here, look, here's a

customer," and I went into the database and I found a

person who was seasonal. And it was clear that their

business was going to be seasonal and I showed a

picture of that person in their logo and gave them an

understanding of this specific person and what their

needs might be. And then you're able to see their

usage pattern in a really simple graph.

Kristen Kehrer: And it's like the model said, this person was seasonal.

And I can also show a picture of Joan, this woman

runs a church group and churches are typically

looking for donations year round. And so you see that

this woman's a usage pattern isn't going to come up as

seasonal because regardless of the month, if I plot year

over year data, in any month, she could have sent zero

times or she could have sent one times. And in some

months, there was a spike, but there was no way for

the model to say that she was seasonal because there

was no definitive pattern to the way that she was using

the product.

Kristen Kehrer: And so, even if you're building a model that is

complex, there are ways that you should be able to

talk to the business and to create those visualizations

in a way that doesn't set the person off. Not set off,

that's not the right wording, but like showing model

output. If I show logistic regression model output, and

I had an example in my presentation where in 2013, I

thought I was doing better and I had this logistic

regression model output and I had converted log odds

to odds because of course, who knows what log odds

are like intuitively when they look at it?

Kristen Kehrer: And the thing is that slide did nothing for the audience

because first of all, I would have had to explain that

the coefficients were multiplicative and I would have

had to explain what the P values meant. And that

totally detracts away from the fact that the model that

I had built said, "Okay, these customers are more

likely to come back. We should target them." And sort

of what makes up these group of customers that are

more likely to come back and on the flip side, who are

the people who are less likely to come back and why is

that?

Kirill Eremenko: I Totally agree. And I think it also takes time. If you

find yourself explaining what logo odds are and how P

values work, then that's going to take like 20 minutes

at least of your audience's time, and by the time you're

finished, they've already forgotten what the whole

conversation was at the start and half of them are

already asleep. I'll say you really need to take into

consideration the technicality of your audience, the

average or the minimal technical level in your

audience and tailor your presentation to that.

Kristen Kehrer: Absolutely. Because if you show them model output

and you lose them in the beginning, you're not going to

get them back either for like your heavy hitters slides

at the end, they're already like, "Oh, this AI mumbo

jumbo even though it's not AI, you know." But we're

throwing that term around all the time.

Kirill Eremenko: By the way, what do you think of AI? You use neural

networks in your work, how powerful have you found

them to be?

Kristen Kehrer: The model that I built had a make of 0.85. I was

building this neural net to forecast hourly electric

load, and this was super instrumental in determining

capacity, like whether or not we had to move over

energy from one subsystem to another. I forget all of

the terminology in terms of what they did, but it was

so that they could manage the capacity of the load.

And originally, I had had some ARIMA models that I

had built to do this, but realistically, the relationship

between load and the weather, is nonlinear. We were

able to get much better accuracy, which was actually

had business implications in terms of making sure

that people's lights don't go off.

Kristen Kehrer: And that wasn't scary either, it was a whole lot of just

data, it was making sure that we took into

consideration daylight savings time and dummies for

holidays, dummies for the day of the week, dummies

for everything, dummies everywhere. And temperature

and dew point and humidity and amount of snow fall.

So there was like a lot of data, but it was way more

accurate than when we were using an ARIMA model.

Kirill Eremenko: Wow. That's really the power of AI right there. It's an

inspiring example of how you can take one approach,

replace it with deep learning, artificial intelligence, and

all of a sudden, you're taking so much more into

considerations. The price you pay ties into this whole

visualization and presentation. The price you pay is

that, it's harder to explain these models. A lot of people

see them as black box models. What are your thoughts

on that?

Kristen Kehrer: Obviously, the coefficients aren't as easily

interpretable as if we had a regression model or if we

had a cart decision tree where you can say, "Okay,

we're maximizing entropy and this guy is the most

important." But at the same time, you're still able to

take a step back and say, "Okay, I know that this

model isn't going to perform well when we all of a

sudden have a thunder storm or it's dependent on the

weather forecast. If the weather forecast for the day is

crap, then I'm not going to be able to accurately

forecast the load. I'm dependent on the weather

forecast." Those things are very conceptually easy to

understand, and I can explain those things.

Kristen Kehrer: The problem with stakeholders is that they just get

nervous when there's a black box and you can't calm

their nerves by showing them, like taking their hand

and saying, "It's okay. When the weather forecast is

good, this is what we can expect in terms of our

average error and on certain days, we're going to see

this behavior, but that's okay." And really spell it out

for them. So it can still be something that's difficult to

understand, but you can still explain it in a way that

makes people trust you, makes people become an

advocate of your work.

Kristen Kehrer: And that's what we're really trying to get to, is a point

where you're considered a thought partner and you're

not just the person who the business is going to come

to and say, "We need a model for this, build it."

Kirill Eremenko: Got you. That's a great way of putting it, that as long

as you can calm the people down and then be their

partner, that's what they're looking for. And yeah,

that's a great way of putting it.

Kristen Kehrer: It's that trust.

Kirill Eremenko: And you've got to build that so that they can ... And

that ties into like storytelling and presentation skills,

These are all people's skills, you can't build trust if

you're just focused on technical, technical, technical.

I'm really enjoying how this podcast is unraveling

because there are people who need to build out their

technical side of things, especially if you're starting out

as a data scientist, you've got some valuable, super

valuable tips here on what things to focus on, where to

start in SQL, Python, R, and how to build up your

technical expertise. But at the same time, if you were

already an advanced data scientist, you want to up

skill, up level your technical things.

Kirill Eremenko: And you've mentioned a couple of things like

multivariate testing that people don't often think

about. But also you want to be thinking about your

soft skills, your people skills, your presentation skills.

How are you going to show yourself, not just as a

person who can crunch numbers and get the outputs

and build a model, but a person who can bridge the

distance between the technical world and the business

decision makers' world, because those are the data

scientists that ultimately become the most in demand,

that thrive the most, that becomes the most useful

data scientists to business who can not just derive

insights, but actually communicate them and help the

business decision makers implement those insights to

help drive the business forward.

Kirill Eremenko: So it's been so great so far. What I want to talk about

now is you have a blog, it's called Data Moves Me, if

people haven't seen it, it's datamovesme.com. It's not

just a blog, it's a website. And I think you're doing

some great things there. So you have the blog if

anybody wants to invite you to a conference or work

with you on a project, there's a great work with me

part. But also I specifically wanted to touch on your up

level, your resume part. Tell us about ... You have a

course there, you have a course on how people can up

level their resumes. Tell us a bit about that. How did

that start?

Kirill Eremenko: Because I believe you only started this website in

August this year. Tell us a bit about this journey and

why you started and how are you helping people with

their resumes?

Kristen Kehrer: A couple of the first blog articles that I had published

were around what a job in data science looks like and

how to effectively interview and what a successful job

hunt looks like. And I had worked with a career coach,

I think I had already mentioned that I had gotten laid

off at one point and had the opportunity to work with a

career coach, and it taught me a lot. And so I shared

that through my blog and as a result, people started

sending me their resumes. And they'd say, "Can you

take a look at this? I need help. I'm not hearing from

the companies that I apply to."

Kristen Kehrer: And so for awhile, I was just, if somebody sent me

their resume, I'd just review it in my spare time and

send it back. And I saw a number of common themes,

and after I saw a number of common themes, I was

like, "I want to create a course so that I can help

people to effectively promote their skills and be more

targeted and communicate their value to the business

in a way that the business is going to be more

receptive to." And so I created that course and made it

available, and it was one of those things that if you do

something a couple of times, you're supposed to

automate it. So that's what I did, is I automated it.

Kirill Eremenko: Nice. And now you can reach more people and help

more people, right?

Kristen Kehrer: Absolutely. Absolutely.

Kirill Eremenko: What's the feedback been so far of the course?

Kristen Kehrer: Oh my God, the feedback has been incredible and it's

really difficult to put reviews up because a lot of these

people that are going through the course currently

have a job. And so they want to remain anonymous,

but nothing feels better than when somebody emails

me and they're like, "I got a job today." And of course,

the resume does not get you a job, I just want to be

clear that the resume opens the door to the interview

and then once you go into the interview, you need to

take it from there. But for those people who aren't

getting ...

Kristen Kehrer: I had one guy who has a PhD, and a ton of experience,

he's an older gentleman and he had been applying so

many places and not hearing back from anyone. And

he went through my course and in applying to 20

companies, he heard back from five and one of them

was Google. And so, it feels really great to get that

feedback in the way that I'm able to help people, it's

like a really special-

Kirill Eremenko: Fantastic. Can you give us like a tip, like an insight

from your course, something that's already on this

podcast, people can get value by just by knowing this

one thing? What would you say is one of the most ... I

don't want you to share the whole course here, I'm

sure, we won't even have enough time for that, but

give us like one thing that would bring value to our

listeners.

Kristen Kehrer: Definitely in terms of being able to get past the

automated systems and being able to get into the

hands of an actual person, it's really important that

your resume is parsible. Any of the medium to large

companies, majority of them are going to use these

automated systems and if you're Tableauizing your

resume, which I didn't even realize that was a term

until I'd seen it on LinkedIn, people creating their

resumes in Tableau or if you're putting charts on your

resume to show your skills with SQL is five stars,

those things aren't parsible, so you're not going to be

able to get through the automated systems. And then

again, I really push people to think about the value

that they're adding. Because you hear, you're

supposed to start with a verb and you're supposed to

end with a result.

Kristen Kehrer: And a lot of times, people are like, "Well, I don't have a

concrete, 'I added 5% in revenue.'" And so they leave

that out. But if you automated the process and that

saved man hours, that's value. There's a lot of things

that are value, that aren't necessarily as quantifiable.

Kirill Eremenko: Got you. Well, those are some valuable tips that you

recommend actually in your experiences including

those that value as much as possible, and highlighting

them.

Kristen Kehrer: Absolutely. Absolutely. Even on my own resume, like

with the neural net, it was, I built a neural net to

forecast hourly electric load. "Okay, cool story, Bro.

What was it used for?" "Oh, well, okay. Actually, this

was imperative during heatwaves to make sure that we

could manage capacity." That's value or, "I helped to

automate A/B test analysis through writing in our

package, that saved four hours per test that we ran

because we didn't need to have an analyst doing the

same thing over and over and over again." And that's

not a machine learning algorithm, that's just

automating a process.

Kristen Kehrer: And it's like, "But I'm saving four hours of somebody's

time," and a business is going to see that and be like,

"Wow, this person gets it." They can explain the value

that they're providing, and it's not always just, "I

increased revenue by 3%." Or, "I increased conversion

by 2%."

Kirill Eremenko: Yeah. And also the business, as you say, the business

sees that this person gets it, like they see that you

think, not in terms of just like, "I like doing data

science work. I like cool projects," which it's just a

valuable attitude in itself, but they also see that you

are thinking about how are you bringing value to the

business, how you've brought value to your past

business or your current business and therefore,

you're going to be thinking in the future about their

business as well. And they want people like that on

board, They want partners, as you said.

Kristen Kehrer: Absolutely.

Kirill Eremenko: Awesome. Fantastic. And can you comment on that tip

again. I found that really valuable. You shared with me

before that it's better to send Word versions of your

resume rather than PDF versions. Why is that?

Kristen Kehrer: Oh yeah. I actually have three blog articles on Data

Moves Me, one is around just getting past ATS, the

Automated Tracking System. One is around

positioning yourself during a career change and the

other one is about writing like crisp, concise content

that makes an impact on your resume. And in that

first blog article on getting past the applicant tracking

system, I put a link to Indeed where it shows you the

number of applicant tracking systems that are in use,

the number is in the hundreds.

Kristen Kehrer: And a lot of the older systems have difficulty parsing

PDFs. And so to hedge your bets, it's better to submit

your resume as a .DOC because you know that it will

be parsible by the applicant tracking system. So the

newer systems can parse PDFs, but not all of them

can.

Kirill Eremenko: Wow. Is it Doc or Docx is also okay.

Kristen Kehrer: Docx is also okay.

Kirill Eremenko: Okay. Well, you see, I didn't know that and I found

that very insightful. When I was applying for jobs, I

would always send PDFs because I thought they look

prettier and the person, when they open it up they

can't see all the underlines, in case there's some coma,

that I didn't put on purpose or formatting stuff. But

that's very insightful too, I hope it's very helpful to our

listeners here. And this is going to come completely

spontaneous, Kristen, I'm sorry to put you on the spot,

but I have a question for you. Would you be willing to

create a special coupon for our listeners on the

podcast in case there are listeners who are interested

in taking your course and would like to participate,

would you be willing to help out by like some special

discount for our listeners here?

Kristen Kehrer: Oh, absolutely.

Kirill Eremenko: Awesome. Thank you. So we'll discuss that after the

show, and everybody we'll include it in the show notes

and I'll mention the show notes at the outro of this

episode. So make sure to check out Data Moves Me

and we'll get some wonderful coupon from Kristen.

Thank you so much for that.

Kristen Kehrer: Yeah, no problem.

Kirill Eremenko: Okay. Well on that note, I think we've covered off quite

a lot. I'm sure there's a lot more. I have a whole ton of

questions like how you've managed teams, the

importance of building up brands, neural nets, which

you talked a little bit about, but probably one last

thing I wanted to cover off before letting you go is, the

book that you're working on with Kate Strachnyi called

Mothers of Data Science. Could you tell us a bit about

that and how the idea came to be, and what is this

book going to be all about when it's released?

Kristen Kehrer: Oh my God, it's so exciting. And I understand that it's

super niche, not everybody is a woman and certainly,

not a mother. So it's not necessarily for everyone

because it's super niche, but it was just an

opportunity. We interviewed Cathy O'Neil, Carla

Gentry, Lillian Pierson, Natalie Evans Harris, just like

a bunch of amazing women. And so it was really an

opportunity for Kate and I to have fantastic

conversations with women that we admire who are bad

asses in data science, who have been doing it for a

while, but also talk about the fact that a lot of times,

we're working in teams, that we're the only woman.

Kristen Kehrer: And when you have a child and you're working in an

all male team, things can get a little hairy in terms of

just trying to balance everything. And I understand,

I'm not saying that fathers don't have a lot to balance.

My husband's is absolutely a 50/50 partner in

everything that we do in the home, but it was just an

opportunity to get personal with some people that I

really look up to and share our experiences as mothers

of data science.

Kirill Eremenko: So cool. I can already feel the excitement in your voice.

What would you say that, like some of the biggest

highlights are that you've had in these conversations?

Kristen Kehrer: It's funny. I just love talking to Cathy O'Neil and she

just makes you think about everything. I had met her

at ODSC in May of this year, And I absolutely fell in

love with her personality, it's very straight and to the

point and she absolutely brought that to the interview.

And it's not necessarily something that will make it in

the book, but after reading her book, Weapons of Math

Destruction and all the things that she's thinking

about in terms of ethics and bias in modeling and how

we're perpetuating these biases, and then to talk to

her.

Kristen Kehrer: And I'm like, "I'm really excited about the work I do."

And she's like, "Yeah, but I don't want your emails."

And it's just like ... I just loved moments like that and

hearing about the struggles of Carla Gentry who, she

didn't have an incredibly easy time. And she talks

about some of her regrets in terms of choices that she

made putting work in front of her family life. And it

was just fascinating to watch, because she's been in

the industry now for 21 years. So to just hear from

someone who has just so much experience in ...

Kristen Kehrer: And actually, we also talked to Olivia Parr-Rud, who is

a grandmother of data science, and she was talking

about how she built a logistic regression model on

45,000 rows of data and would have to run it over the

weekend, and it would take that long to run. Oh man,

there was just so much fun, interesting to connect

with these people.

Kirill Eremenko: Even though you say it's a niche book, it sounds like

an interesting read. I would totally be interested in

reading about that. Obviously, I'm not at the stage

where I have kids, I'm not even a father of data

science, but to me, it sounds quite exciting, these

journeys. It's always interesting to hear somebody's

journey through their career, through data science and

the struggles they had. And like family time, is family

time for everybody, not only if you just have children.

So I'm very, very impressed and I'm grateful that

you're working on this project, I think it will help many

people. And personally, I will pick up one of these

copies. When is it coming out?

Kristen Kehrer: Oh man, Kate and I have a goal of making sure that it

gets out this year.

Kirill Eremenko: This year. Okay, good. Maybe some Christmas

presents for some people.

Kristen Kehrer: Yeah, Christmas presents.

Kirill Eremenko: Nice.

Kristen Kehrer: In your stocking, Mothers of Data Science.

Kirill Eremenko: Awesome. Okay. Well on that note, Kristen. Thank you

so much for coming on the show, it has been an

incredible pleasure. And before I let you go, could you

let us know, the listeners on the podcast, where are

some of the best places to find you online and follow

you and your career?

Kristen Kehrer: Oh yeah. Absolutely on LinkedIn. That's I think where

I'm the most active. And so you can absolutely follow

me there. I'm also on Twitter @Datamovesher and I'm

on Instagram @Datamovesher. My Instagram is

definitely more personal, I posted a picture of my kids

tonight, but I'm around and you can find me.

Kirill Eremenko: Awesome. Fantastic. And obviously, we also have the

website, Datamovesme.com.

Kristen Kehrer: Yes.

Kirill Eremenko: Great. And as mentioned, we'll include the coupon for

the course in the show notes. One last question.

What's the book that you would recommend to our

listeners to inspire and help their careers in data

science?

Kristen Kehrer: Oh man. Obviously, you need to pick up women,

Mothers of Data Science and the book that I read most

recently that I just mentioned was Weapons of Math

Destruction, and it really did push me to think about

these models that I'm building and to think about the

effect that they have on society.

Kirill Eremenko: Thanks so much. I've heard of that book, Weapons of

Math Destruction, I haven't read it yet, but that's

another reason to pick it up, your recommendation.

There you go. On that note, thanks so much, Kristen,

for being on the show today here and sharing this time

and your expertise in the space with us, which I'm

sure a lot of people got a lot of inspiration and insights

from this. Thank you so much.

Kristen Kehrer: Oh my God. Thank you so much for having me. This

was so much fun.

Kirill Eremenko: For sure. The pleasure is mine.

Kirill Eremenko: There you have it. That was Kristen Kehrer from Data

Moves Me. I hope you've enjoyed this episode as much

as I did. My personal favorite part was when Kristen

mentioned that there's two types of valuable skills in

data science, the ones that are useful, something that

you enjoy, that are useful to you personally, that

you're learning a lot through. And there's those other

ones that are useful to the business. Sometimes they

will match up and that's amazing, sometimes they

won't, but it's good to know both. It's good to know

which skills are great to explore and have fun with and

potentially find new ways of applying.

Kirill Eremenko: And it's good to know which skills are solid ones that

you want to go to and you know that there's a high

chance that they will bring value to the business,

because a lot of time, that's something that data

scientists miss. You need to know how to add value to

businesses. And it was very nice of Kristen, of course,

to share a coupon with us for her course. If you'd like

to take the course and definitely use the coupon in

that case, you can find it at

www.superdatascience.com/207. That's where you'll

find the link to Kristen's course and the coupon that

she mentioned.

Kirill Eremenko: It's for you to take her course on building your

resume, and also you'll find all the show notes there,

all the things that we've talked about, the materials,

link or URL to Kristen's LinkedIn, and the books that

we've mentioned. And make sure to, even if you don't

take the course, make sure to connect with Kristen

and follow her on LinkedIn, because there's going to be

lots of exciting announcements. And personally, I'm

looking forward to the book, Mothers in Data Science

coming out, hopefully later this year. So I can pick it

up, and I highly encourage you to check out a copy as

well.

Kirill Eremenko: The show notes are once again, at

www.superdatascience.com/207. Hope you've enjoyed

this and maybe you will be at DataScienceGo 2019

next year, to meet inspiring people like Kristen and

other speakers that we've had. On that note, thank

you so much for being here today and I look forward to

seeing you next time. Until then, happy analyzing.