You’ve got the education, the experience, the recommendations, but how do you make sure your resume will impress the recruiter or HR as soon as they see it?
In this episode of SuperDataScience Podcast, we chat with Kristen to give us great tips and strategies for sure results so make sure to tune in!
About Kristen Kehrer
Kristen Kehrer is the Founder of Data Moves Me, a known data science influencer, and the co-host of the Data Science Office Hours Podcast. She is also an author who’s about to publish her first book, Mothers In Data Science, soon.
It’s difficult to package yourself in a resume to 100% catch the interest of a recruiter and HR representative. But it doesn’t mean it is not doable. I know that you’ve scoured the internet (so many times) for ways on how to organize and make your resumes impressive. You’ve followed the tips but, in the end, you don’t see great results. What’s working? What’s not working?
Kristen, who’s very passionate about helping the data science community is here to help you. Kristen has been producing a lot of valuable content online – from blogs to podcasts to online courses. In addition to that, she was also one of the speakers for DataScienceGO 2018 Conference. She’s a big influencer in the field so there’s a high chance you’ve been following her works for quite some time. If not, then go connect with her.
For today, she chooses to focus on up-leveling your resume. There are great tips and strategies she mentioned that you may have never heard of. One tip, which may be seen as absurd, is sending your resume in Word document format instead of a PDF. Listen to Kristen tell the reason behind it.
Learn also how to showcase your technical skills and at the same time your interpersonal skills. Kristen emphasizes the interpersonal skills – social, leadership, work etiquette, etc. – since companies look at these and we, data scientists, tend to undermine how important they are. Data storytelling is also a great skill so to add to your skillset. Start honing them and learn how to collaborate with other people. You can easily add all of these on your resume.
We also talk about the tools that could serve as a foundation for starting data scientists. Kristen shares her experience when she started with SQL. Discover also why she choose to use both the Python and R programming language. One of the techniques that Kristen uses is multivariate testing when dealing with specific data. She talks about why it’s essential that it’d be the first technique to study and learn compared to A/B testing.
In this episode you will learn:
- Kristen Kehrer shares her DSGO 2018 experience. (03:22)
- Anyone in and outside the field can give back to the data science community. (04:32)
- Collaborating with other people. (08:00)
- Multivariate Testing vs. A/B Testing. (14:56)
- “Data Science is both an art and a science.” – Kristen (20:55)
- What's the most foundational tool that a data scientist should use? (22:25)
- R vs. Python (25:55)
- Handling Big Data. (30:10)
- Data Storytelling: Why is it important? (33:20)
- Kristen's works that involve Artificial Intelligence. (39:22)
- ‘Black-box model'. (41:43)
- Up-Level Your Resume. (46:01)
- It's better to send a Word file than PDF file of resume. (53:10)
- Mothers of Data Science. (56:14)
- Kristen's special discount coupon for SuperDataScience listeners! (01:03:14)
Items mentioned in this podcast:
- SDS 205: Contributing to the community as a Data Science Influencer with Kate Strachnyi
- How to use Customer Segmentation To Learn by Kristen Kehrer | Medium
- Up-Level Your Resume course + coupon for 50% off: sds50
- Data Moves Me
- DataScienceGO Conference
- Weapons of MATH Destruction by Margaret Vanderburg
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.