SDS 177: Building a Career in Data Science

SDS 177: Building a Career in Data Science

Building a Career in Data ScienceWelcome to episode #177 of the Super Data Science Podcast. Here we go!

In the incoming years, we will be witnessing a radical change in data science education. Would you agree?

On today’s episode of Super Data Science Podcast, Zach and I talk about utilizing online platforms, integrating machine learning in the healthcare industry, and sighting the future of the formal education.

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About Zachary Loertscher

Zachary Loertscher is currently finishing his bachelor’s degree in Business Analytics from Brigham Young University – Idaho. On his free time, he self-learned various tools such as Tableau, Python, R, SQL, and Business Objects Data Services. He curated the List of Epic Data Science Mentors for enthusiasts who want to dive in Data Science.

This type of out-of-the-box thinking is exactly what you need to stand out in a career in data science. For more tips & tricks on how to move forward, don't miss our career-focused data science event: DataScienceGO 2018.

Overview

Getting the degree from a university and highlighting it on your resume could get you the ultimate dream job. Well, that’s what we are perceived to do by society.

In this digital age, it’s just fitting to question the steadiness of the traditional system of our education and look into the incorporation of online courses in your learning journey. This is not to say that studying on a fixed schedule inside a four-cornered classroom and finally getting that diploma is becoming invaluable. With the fast-paced learning environment, we should keep up. Academies should have up-to-the-minute curriculums for students and we should make use of the ever-available resources in front of us.

For example, Zach is just finishing his undergrad but he went the extra mile of studying various tools by exploring online courses. His eagerness to learn data science also led him to make the List of Epic Data Science Mentors. This list became popular and helped a lot of enthusiasts also. Aside from online courses, he utilized LinkedIn. He put his self out there – building a brand, posting a lot of content, and engaging with the data science community.

He’s also looking into using data science machine learning to help with healthcare issues. He’s spotted the opportunity where he can contribute more. He continues to learn by reaching out to people who work in the industry.

Zach and I share our thoughts on the current state of data science education for the most part of this episode. I believe that the most important thing is to be crystal clear on your intentions when getting a degree. It doesn’t matter if it’s online or traditional. Those online certificates are gonna be valued less and those university certificates are gonna be equally valued less. Having a degree indicates the possibility of contributing but there’s so many resources online that you can demonstrate it much more effectively and succinctly.

Remember: drive, motivation, discipline, and structure are required. Make your rules and follow your rules. Have a vision where you’re going. If you think you’re still lost, you might get inspired by when we talk about the most and least favorite parts of data science.

Start listening and please don’t forget to tell me what your stand is!

In this episode you will learn:

  • Zach is pursuing Business Analytics and learning more about the world of data science and machine learning. (05:45)
  • LinkedIn is a powerful networking tool to build your portfolio, engage with the community, and learn more about data science. (08:38)
  • Zach is looking at the opportunity to use and harness the power of machine learning to help with healthcare issues. (16:57)
  • Formal education vs. Online Courses. (23:22)
  • “It’s about making your rules and playing by the rules” – Kirill (31:30)
  • Drive, Discipline, and structure are very important if you want to do self-learning. (34:58)
  • “Data Science is the best field for entrepreneur lands.” – Zach. (41:56)
  • What’s their favorite part of data science?
    • Zach – Data Visualization. People can easily gain insights from well-presented data. (49:18)
    • Kirill – Breaking down the complex into simple. (51:43)
  • What’s their least favorite about data science?
    • Zach – Programming. (53:30)
    • Kirill – getting lost in the specifics of certain algorithms. (54:30)
  • Zachary prioritizes work-life balance. (56:40)

Items mentioned in this podcast:

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Episode Transcript

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Kirill Eremenko: This is episode number 177 with aspiring Data Scientist, Zach Loertscher. Welcome to the Super Data Science Podcast, my name is Kirill Eremenko, Data Science Coach and Lifestyle Entrepreneur and each week we bring inspiring people and ideas to help you build your successful career in data science. Thanks for being here today and now, let's make the complex simple.

Welcome back to the Super Data Science Podcast, ladies and gentlemen. Super excited to have you on board and today we had quite an interesting episode with an interesting guest, Zach Loertscher. Zach is an aspiring Data Scientist who has made huge progress in terms of building his own career and building his online presence in the space of data science. And interestingly enough, the way I actually found out about Zach was through a blog post that he made with a list of Data Scientist mentors to connect with to follow. And I was so inspired by that idea, I thought it was a great idea and a great way to help others, that I had to invite Zach onto the show and find out about how he actually thinks of data science, of the community, and of building your presence here and also playing your part, your role in helping others.

That's what we talked about quite a lot on this podcast and you'll get some very valuable insights in how you can better help the data science community and build your own online portfolio. Also, we talked about his thoughts on data science education, and in this podcast we actually flipped it around and Zach asked me a couple of questions, which I was totally not expecting. And I got to answer a few questions on the show as well, so you'll get to know a few of my thoughts, especially on the situation with data science education, with universities, with online courses and things like that. All in all, quite a fun episode, a bit of a different style this time. I hope you enjoy it. I can't wait for you to get straight into it. Without further adieu, I bring you Zach Loertscher, an aspiring Data Scientist.

Welcome back to the Super Data Science Podcast, ladies and gentlemen. Today I've a very exciting guest on the show, Zach Loertscher. Zach, welcome to the show. How are you doing today?

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

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

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

Kirill Eremenko: Swiss, Germany. I was in Switzerland in the Swiss-German part for a whole month in march this year. Very nice place actually, it's very neat and very clean. Have you been back since?

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

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

Zach Loertscher: Yeah.

Kirill Eremenko: Well Zach, first of all I wanted to say a huge thank you. The reason why I reached out to invite you to this show is because I saw a list that you put together and somebody else represented in image format, which is a list of top mentors in data science. I was very humbled to see myself on the list, that was very exciting for me but that's not the reason of course, why ... it's not, "Oh just because of me." The thing is, I found it very inspiring that you went through the trouble of actually putting together a list of mentors in data science, people who you can learn from and who inspire you to grow in this space. So, thank you so much for putting that together and for sharing it with all the aspiring Data Scientists. Today, I just wanted to talk more about that and your journey in data science, how does that sound to you?

Zach Loertscher: That sounds awesome.

Kirill Eremenko: All right. Well, tell us a bit about yourself. Where do you live? And what are you currently doing in your life?

Zach Loertscher: Currently I live in Idaho, in Rexburg. Not many people know about this place but it exists. I'm going to BYU Idaho, pursuing a degree in business analytics and I am about to graduate in a few weeks. Yeah, I'm really excited about data science. I've been studying it since I did my internship down in Utah, and I was working for their business intelligence team and I found this really cool tool called Tableau. That was just really, really cool to me. Then I was also having to learn how to program with Python 'cause we were doing a lot of data transformation and I was going through one of those long days of going through tutorials and trying to finally find what you actually are looking for. I stumbled on Kaggle and I was like, "Oh this is cool. What are these people doing?"

Kirill Eremenko: Yeah.

Zach Loertscher: That's how I discovered machine learning and data science and since then it's just been a really fun ride. That's a little bit about me and my stage in life. Yeah.

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

Zach Loertscher: Yeah.

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

Zach Loertscher: Uh huh.

Kirill Eremenko: And what was the university?

Zach Loertscher: BYU Idaho.

Kirill Eremenko: Oh okay, okay. I didn't catch that. Okay, cool. I was surprised to learn that, it was a very apt degree for getting to the profession of data science. You don't hear many degrees in business analytics, is that a recent degree that they've come up with or has that been around for a while?

Zach Loertscher: Yeah. Well, it's been here for a few years. They actually just created a data science degree but I'm already so far into it, I'm like, "Well, I'll just finish with business analytics and pick up all the other stuff along the way."

Kirill Eremenko: Might as well, might as well.

Zach Loertscher: Yeah.

Kirill Eremenko: Okay, cool. Why did you get into business analytics in the first place? Because that was what, four years ago? Is it because of what you heard about data science and what's going on in the world? Or was there another reason?

Zach Loertscher: Actually my dad, he works in business intelligence. He does a lot of data warehousing and he started in reporting and I was like, "You know, this looks interesting to me. I know there's high demand for it." And I didn't actually really know about data science when I started out on this degree. I was like, "I'll just go along this path that my dad has gone along." It wasn't really until my internship when I really discovered data science and what it is and I found out, "Hey, business analytics actually fits into this a little bit."

Kirill Eremenko: Yep.

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

Kirill Eremenko: Yeah, yeah. That's pretty cool. Okay. So you're on this journey, but tell me this, many people who are studying are just happy studying. Yet, you go above and beyond to actually find online courses, like you mentioned before the podcast, I think you did one of our courses or at least a couple courses already, then you finding resources and you put a list of mentors together who inspire you in data science. What pushes you to do that? Why did you decide to go that extra mile?

Zach Loertscher: I think it's mostly been knowing that LinkedIn is such a powerful networking tool and I've been really trying to push for a stronger network as I'm approaching graduation. That was really the first place I went to because for a very long time, for I don't know, four or five months, I would just look at LinkedIn and look at what other Data Scientists were saying and just follow them and say, "Oh, that's cool, that's what they're doing." But I wasn't really posting anything. I noticed that a lot of people on LinkedIn talked about, "It's such a great networking tool, it brings people together, it helps you find a job." I was like, "I'm gonna try this out and I'm ready to contribute. I've been looking at this for a while, I've been studying it for a while and I want to contribute, I want to make a difference."

That's really what prompted me to put it together. I remember when I was just starting at data science, someone else had posted a list of a bunch of Data Scientists I should follow and that's when I really started learning about it. So I was like, "Let's pay it forward a little bit." And I had no idea that, that post would be so popular, but it was really exciting, it was really cool and that's really my motivation. One of the best parts about it is getting messages from people just thanking me. I've heard other people say the same thing 'cause I haven't done too much, but just thanking me for contributing what I have, saying, "Hey, you helped me with this." And that's just one of the most satisfying parts about it all. Yeah.

Kirill Eremenko: Yeah, yeah. Gosh, and you actually helped me as well, apart from finding YouTube and watch this podcast. The way I found your list, I think Ben Taylor, you know Ben Taylor?

Zach Loertscher: Yeah.

Kirill Eremenko: He recommended me to look at your list for guests to invite to Data Science GO 2018 and-

Zach Loertscher: Oh cool.

Kirill Eremenko: Yeah, and I was going through your list, I was like, "Oh, I haven't met this person, I haven't met this person." And I just went through every single one and invited everyone to connect on LinkedIn. It was a great opportunity for me to connect with the top influences in the field of data science, so thank you. It really helped me out as well.

Zach Loertscher: Yeah, you're welcome.

Kirill Eremenko: In fact, we're probably gonna share this on the show notes for this episode, for all listeners, I highly recommend going to check it out and then just connecting with every one of those influencers there and following them, and learning from them just as Zach did, and I'm learning and others are learning as well. Yeah, that can be a good addition to peoples LinkedIn networks. Tell us a bit about, if you don't mind, since you made the decision that you want to build this network and you want to contribute back to the community, how have you felt the impact? You mentioned that people have said thank you, but has anything else happened for you in this period of time?

Zach Loertscher: I feel like by posting, you develop these ... they're not real life relationships but there are certain people who will always comment and you develop these friendships almost on LinkedIn, which is awesome. But also, if you think about LinkedIn and the platform and what it's designed to do, it's designed to keep you on the site as long as possible. They get their revenue through ads or through hooking you up with a company or someone else. So if you have a lot of posts and you're putting a lot of content out there that people are enjoying and engaging with, when a recruiter searches for you, it will boost you in their search results. 'Cause they want recruiters to find people who are active on the site so that the recruiters will stay on the site longer. At least, that's my theory.

That's basically what happened for me, is as I was posting more and engaging more with the community, I was getting contacted by a lot more recruiters. It really wasn't until I started posting so much that, that happened. So that was just an unseen benefit for me and one of the best ones for me because it's led to a lot of opportunities and a lot of connections.

Kirill Eremenko: Yep, yep. That's definitely what LinkedIn is all about, and putting yourself out there and building your brand and portfolio and helping others is gonna get a lot of attention going your way. Yeah, that's a good start, you're definitely on the right track with your career. You haven't even graduated yet and you already built up this portfolio, how does that make you feel?

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

Kirill Eremenko: Yeah.

Zach Loertscher: I just feel really privileged and blessed to have that opportunity, that there is a good group of people that see my posts, that's exciting for me 'cause I get to share my journey with other people and hopefully inspire other people who come from a similar background or maybe are just beginning. And maybe share some encouragement or motivation, share some of my discouragement to help them feel encouraged 'cause it's a hard journey for everyone. That's the exciting part for me.

Kirill Eremenko: Gotcha. How much time on average do you think you spend per day on LinkedIn?

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

Kirill Eremenko: An hour and a half.

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

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

Zach Loertscher: When I'm posting, it feels more like ... I don't know, I put a lot of thought into it and it takes a lot more thought than when you're just scrolling, right?

Kirill Eremenko: Right, right. Yeah, yeah.

Zach Loertscher: But no, it's really exciting 'cause I've been following a lot of these people for a long time and you get to see their journey as well. You also discover a lot of new trends in the field, new things that people are discovering, new cool projects that someone else did. I'm always seeing these cool new innovations that people created with technology or deep learning, things like that. That's really exciting for me too.

Kirill Eremenko: Who would you say is your favorite Data Scientist to follow on LinkedIn?

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

Kirill Eremenko: I see you have commented on quite a few posts by Randy Law recently.

Zach Loertscher: Yes, yes, yeah.

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

Zach Loertscher: Yeah, he's probably one of my favorites. He's always posting something really positive and motivational, and also posts these awesome lists of resources. And if someone else is asking me for help, I usually just send them one of those lists of resources 'cause they're so awesome. Yeah.

Kirill Eremenko: Yeah. Yeah, okay. That's very cool. All right. That's a great way of getting this knowledge, by following the people that are at the top of data science or the cutting edge of data science and they're trying to break it down in simple and complex terms and then you can just learn from them and see what resources they use. You get ahead of the game, you skip all that searching that they are doing and they save you a bit of time that way, I guess. That's very cool. What about your future career? You're graduating in a couple weeks. That's very exciting and congrats on that.

Zach Loertscher: Thank you.

Kirill Eremenko: Where do you think you're gonna go from there? Have you already lined up a job? Or is there an industry that you're interested in?

Zach Loertscher: Yeah, actually I have. I don't know if I'm supposed to keep it on the down low or not, I'm not sure.

Kirill Eremenko: Probably stay on the safe side but maybe just ... all right, congratulations. If you've lined up a job that's fantastic. But yeah, tell us once you have a job, are you going to continue learning? What kind of data science are you going to continue learning? In what area are you doing to direct your education?

Zach Loertscher: Yeah, I'm really fascinated by the healthcare industry, actually, and a lot of the innovations that are happening there because one of the projects that I did, there's a really cool data set on Kaggle about breast cancer. Actually, it was the first data set that I ran a logistic regression on and it was really fascinating to be able to see I can get 95% accuracy at predicting breast cancer. Something that nobody likes and something that everyone's researching to try to get rid of, you can use and harness the power of machine learning to help with this issue. So that's the field that I'm hoping to end up in, really. Is in that field of using machine learning within healthcare, because the traditional healthcare system has just been, you go in, you see a doctor, someone who's been to school for several years and they're very wise, they're very practiced. But there's no way that you could ever harness all the data that the medical professionals use in one day without using Data Scientists. And that just makes me so excited.

I saw a video the other day of ... I don't remember where it was but they were using deep learning to spot cancer cells in realtime, draw a little circle around it through a microscope in realtime, and it was all based off of these deep learning algorithms that they had run on these images. It's just fascinating to me, so that's where I really want to end up is at the healthcare industry, 'cause I think there's so much potential there and a lot of potential to do good as well.

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

Zach Loertscher: Uh uh.

Kirill Eremenko: It's spelled H-I-M-S-S, and I just recently found out about it myself, just a few days ago. I think this one is held in February and it's massive, it's like 40,000 people attend and it's all about healthcare and technology, the intersection and ... it's not all just data science but there's a ... I haven't been to it but I heard about it from a data science podcast, I'm assuming a large portion of it is dedicated to data science. Why I'm mentioning this is because first of all, it stands to show that this is a very rapidly growing industry, or intersection of industry of healthcare and data. Also, maybe you and others who are interested in healthcare can check it out. It's HIMSSconference.org, might be a good thing to attend maybe.

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

Kirill Eremenko: Okay. I understand it's a noble cause to help people with healthcare and apply to data science in that area, is there any ... there's also other areas of where you can apply data science. Finance, you can apply data science in safety, in full detection and other areas. How did you single out this one? We already understand why, that you want to help people, but what thinking process did you go through? Did you just meet someone who is in healthcare and you got inspired? Or did you go through lots of industries and you picked this one? Or was it just because you were able to get a job in this space and then you learn more about the company and you were inspired by what they do? Can you walk us through the thinking process of somebody's who's studying, how do they pick an industry that they're gonna go into?

Zach Loertscher: Yeah, absolutely. I think one of the most powerful things that someone can do before they decide on an industry, like, "I'm gonna go in finance or I'm gonna go into healthcare.", or whatever it might be, is reach out to people who are currently working in that industry. Better yet, people who have recently been hired in that industry. For me, as I was pondering about going into the healthcare industry, I did reach out to a few people who do work with data from the industry, and they both spoke very highly of it. They said it's fascinating the things that you learn. So for me, that was a really good motivator. Also, just thinking about where ... data science has come a long way for business but I think there's a lot of potential for growth still in the healthcare industry. I think business has been on the bleeding edge, obviously there's research, but business has been on the bleeding edge of using data science. And I think healthcare is coming to that point where it's going to be using it a lot more.

So, just spotting that opportunity gets me really excited, like, "There's gonna be a lot of jobs in this field, there's gonna be a lot of opportunities." So I think just being aware of what's happening in the field and thinking, "Which industry could really explode next?" But probably even more importantly is talking to people who are in that industry so that you get a good idea of where it's at and is it going to explode next?

Kirill Eremenko: Yeah. No, that makes it very clear. So you are identifying an opportunity for the situation to grow, and you're right, in business, competitive pressure makes companies adopt newer and newer technologies all the time. And that might not really be the case in healthcare, it might take a bit longer for that to happen. Okay. That's very good. The other thing I wanted to ask you is actually what you asked me before the podcast, you mentioned that you are interested to find out about degrees. Is a formal education required in data science? Or are online degrees an exception? Can you repeat that question? What is it exactly on your mind when you're pondering that?

Zach Loertscher: Absolutely. As a new graduate, and I've found that position ... I'm thinking a lot about grad school and traditionally, in the field it has been, "You need a graduate degree." But the cost of education everywhere is skyrocketing, but also the availability of open source or very low cost education online is also skyrocketing. So I'm wondering what your take is on these online certificates, maybe if people go to Corsair, or Udacity, or Udemy and learn about these things, do you think that companies will begin to value those certificates as much as they value an advanced degree? Or do you feel like it'll keep on the same trend that it's been following?

Kirill Eremenko: Okay, that's a good question. What I would say is that, I think those certificates ... and this might be a bit of a controversial answer but I think those certificates, those online certificates are gonna be valued less, and I think university certificates are gonna be equally valued less. The reason I say that is because ... especially once you already have a degree, which I personally think if somebody's looking to starting a degree, at the very beginning, you can go without it. But especially if you already have a degree and you already have a job, from here what counts is your experience, is your ability to demonstrate that you have industry or industry level experience in the field. And that's all that matters because people don't really care about, or employers don't really care about another paper.

At the end of the day, whether you have a paper or not, what they care about is, can you solve their problem, can you add value to their business, can you add value to their bottom line of their profit and loss statement. And having a degree might indicate the possibility of you being able to do that, but there's so many other ways right now online that you can demonstrate that much more efficiently and much more succinctly. Whether it's by going, like you said, to Kaggle and doing projects there and adding them to your portfolio, or using, again, Tableau and building an online Tableau public portfolio and showcasing things there. Whether you're on a website or blog and sharing information there, you don't even need to blog these days. As you've noticed, you just share stuff on LinkedIn and ...

Well, you can't really share if you have a job and you're working there and you have made some breakthroughs, you can't really share those things because that's sensitive information. But you can share the techniques that you use, again, if they're publicly available techniques, or ideas that you came up with if you're not violating any intellectual property. Or just to share thoughts on other data sets and how you would approach other challenges. Ultimately, if somebody's signing up for a degree such as a master;s degree, there has to be an intention in mind. You cannot just sign up for a degree and say, "Okay, I'm signing up for this degree just for the sake of doing it, just for the sake of having a paper." That's definitely, in my view, a waste of time.

Zach Loertscher: Yeah.

Kirill Eremenko: If you have an intention in mind, for instance, I want to have, not just a data science job but like in healthcare, which is really, really great stuff, but I want to have a Senior Data Scientist, Senior Machine Learning Expert in the field of healthcare. Or maybe, let's say Senior Machine Learning Expert in cancer prevention. You have a very specific goal in mind and that's hwy you would pursue a master's degree in machine learning or something like that. Well, as long as you have this intention in mind, all you have to do now is replace the word degree, which I find is like a safe bet, or a person that ... like if somebody who would take a degree is ... maybe not always the case, but in my mind, it might be somebody who just lacks the proactive approach to be creative and come up with an alternative solution that might be faster, but it might be a bit more difficult.

Let's say this one, you go and you create a portfolio of projects and you share them through LinkedIn consistently for six months, that are specifically either about machine learning or cancer prevention using data science, or and machine learning. And if you keep doing that for six months and you share your work on one project every four weeks, let's say in six months you share six massive projects that you did, you described them, you spend a week or two just writing that blog post out, in addition to the two weeks that you spent on the project. So in six months, you have six big projects that you shared with the world that added value to businesses, and people, and professionals, and aspiring Data Scientists, and that is going to give you so much more visibility than just the paper that you can share on your LinkedIn or in your resume. The process is gonna be faster.

A master's degree is gonna take you at least two years, or at least one and a half years. This can be done in six months and it also is gonna be more current because those degrees that you see in universities, unfortunately, they are usually outdated by the time they are released. Because to go through the formal approval process in the university, they have to put the curriculum together, get it approved by the Dean, by the faculty, by the university, et cetera, et cetera, et cetera. By the time it rolls out, it's already been at least 6 to 8 to 12 months and the world's moved on from those case studies, from those methodologies.

Not that far but there might've been a breakthrough or some other discovery that is not included in the degree and therefore you're like, what you're studying or what you're paying money for, there's already something newer out there. Whereas if you're doing it yourself, you can always adapt. All those projects take a month at the most. That's my take on it, especially having completed a degree and having a job, you're not pressed for time, you have a way to sustain yourself and you have this hopefully free time to work on projects, I would just go do it my own way.

Zach Loertscher: Yeah, yeah. Absolutely, cool. Wow, that's a very powerful answer, I really appreciate that. I got some gold nuggets of wisdom in there.

Kirill Eremenko: No worries, man. Sometimes it's sad to see people losing time, time is the most precious thing we have. I get the appeal of a degree, whether it's a bachelor or a master's degree because it's what our parents did, what our grandparents did, what everybody's expecting you to do. It's a big scary to go without a degree, or without a master's degree, or without a PhD because still, it has some way, this feeling that it's an accomplishment, it's like a check, "I checked this box off." But ultimately, if you'll even look at the most successful people in the world, most of them are drop outs, there's Mark Zuckerberg, Steve Jobs, they never completed universities, as far as I know. I might be mistaken somewhere here, but most of these people, they realize that, "Hey, no, there's something else I can do." It's about making your own rules and playing by your own rules.

University and all those degrees, they are rules that have been created over time and society has imposed upon itself and accepted. And by following them, you follow a safe path that is guaranteed to get you somewhere. And even though, it's just the perception of those guarantees. Those guarantees are actually fading, dwindling away exponentially as we move through the years, as we move into the world of internet and technology and more people are actually coming online. This guarantee is actually dwindling away, it stays in our mind through this upbringing, through our cultural education and things like that, but in reality, this is the best time probably in the history of human kind to break rules and play by your own rules. I'm not talking about legal rules, I'm talking about cultural rules of the way that we are used to building our careers and education and so on. The people that break the rules are the people that create their own rules and play by their own rules, those are the people that get ahead the fastest and succeed the most.

Let's say for instance in your case, you've created your own rules by saying, "Hey look, I want to give value back to the community on LinkedIn." That's already not what most people do, 99% of people don't ... I don't know how many people have or don't have LinkedIn but let's say out of the people that do have LinkedIn, 99% of people don't share valuable blog posts of their own creation. But then you take it even further, you're like, "Well hey, how about not sharing just a blog post, how about sharing a list of mentors, people who have influenced me. Let me collate that information." That's like you creating your own rules, nobody else had thought of that, especially since they said, "Oh maybe a few people have." But you were like, "Let me do that from my perspective."

Look what that's gotten you, so many people have contacted you, so many people have gotten value out of it. And right away, as soon as you do something that doesn't conform with the rules, you ... there's a saying, "If everybody" ... I don't remember exactly how it goes, but, "If everybody around you thinks you have a stupid idea, it's either they're true, they're right, you have a stupid idea, or you're on the verge of a breakthrough." Right?

Zach Loertscher: Yeah.

Kirill Eremenko: I'm not saying it was a stupid idea, but I'm saying if you're not conforming with everybody else, you might not do anything, you might be wrong, but on the other hand you might be right and therefore you will have this exponential leap all of a sudden. And "Bam!", in one blog post you have thousands of followers, people are contacting you and you added tremendous amount of value to others. So yeah, I guess the same goes for education and stuff like that.

Zach Loertscher: Yeah, that's awesome.

Kirill Eremenko: Yeah. That's my take on these things. Any other questions? I like this approach. Do you have any other questions for me that I might be able to help you out with?

Zach Loertscher: I'm curious a little bit about your own journey. You're a successful entrepreneur, you've started your own business, you're telling me you've got people from all around the world in your company and I also share this same philosophy. I know I'm finishing a degree but as I come along in understanding the power of teaching yourself and seeking resources on your own, I've changed my mindset a little bit there as well. One of the parts that I've struggled with and I know a lot of other people struggle with is the discipline that is required. You go to school and it's very structured and you have accountability in place with professors, or I don't know, maybe you're facing pressure from someone else, and it is a very new idea to take your own path and take charge of your own education. So, I'm wondering, on your own path, what has helped you the most to, I don't know, keep that drive, keep that motivation, and keep that structure in place? 'Cause that structure is very important.

I've definitely experienced just traveling down the rabbit hole on 15 different web pages, all in one day and not really learning anything at all. I'm curious, what tips or techniques have you found to be the most helpful in disciplining yourself and creating an organized learning environment for yourself?

Kirill Eremenko: Oh, that's a good question. Oh okay. All right. Well, I guess one thing is I don't like wasting time, that's number one, because I don't like setting myself back or putting myself at a disadvantage because I'm being lazy and knowing that, that will cost me time later on because I know I probably won't get that time back. Realizing that, that you only get every hour, or day, or even I think in more terms of years. Like if this is when I was doing my degree or when I was working at Deloitte, I knew that if I fail at something, failure's okay, but you learn. But if I fail because I'm lazy during [inaudible 00:36:13] then that's gonna set me back in terms of my being able to progress through my degree, or through my promotions, and so on. So I was like, "I can't afford to do that because I'm responsible to the future version of myself. Future Kirill, he's gonna be upset with me or I'm gonna put him at a disadvantage, that's not cool." That realization is important, I guess.

Zach Loertscher: Yeah.

Kirill Eremenko: The other thing is, have a vision. What do you want? If you don't have ... they have a saying that, "Without a target, you're gonna miss every single time." Right? Have a target, have a goal. Where are you going? What's the purpose of what you're doing? In terms of organization of your work, there's a great methodology by Tony Robbins called the RPM, called the Rapid Planning Method. He talks about identifying the result that you want, then setting a purpose behind that result, which is the emotional driver behind what you want. Let's say you want to learn R, in your degree you might know Python but you want to learn R by the end of the year. So that's your result, "I want to be able to code a random forced algorithm in R by the end of the year." That's gonna be your result but then what's the purpose? It has to be emotional, it has to be like, "So I can actually help prevent cancer and save lives of people, lives of people in Idaho, or in other places."

Zach Loertscher: Sure.

Kirill Eremenko: Yeah. And so the M is the Massive Action Plan, that's something to look up the Rapid Planning Method by Tony Robbins, it can help you with the organizational side of things. The reason why I was bringing this up is because, "have a goal in mind", that's the first part of the RPM, have a vision where you're going because unless you have that, it's really gonna be hard to be disciplined. Discipline is a micro thing, it's within a day, within an hour you have to be disciplined, but you can't achieve micro effectiveness without a macro vision, without a macro goal in mind because where are you going, right? You might be disciplined for an hour, for a day, for a week, or a month but then you're gonna be like, "What's the point of all this? Where am I going? What's the purpose?" And you're gonna lose motivation. So that's another thing.

The first image that pops to mind when you ask about discipline is, when I was working at Deloitte and also at the same time building these first courses I was grading, just every 15 minutes I had a timer going off when I had to write down, did I spend those 15 minutes effectively and were they good 15 minutes or were they bad 15 minutes? Did I waste them looking at Facebook or YouTube or whatever? And then I was tallying them up for the whole day.

Zach Loertscher: Oh wow.

Kirill Eremenko: Yeah, it's pretty intense. There's a timer, Pomodoro Timer it's called, you can get a version.

Zach Loertscher: Yeah.

Kirill Eremenko: You know that one?

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

Kirill Eremenko: We have some people on the team that actually use it and it works exactly like that, every 10 or 15 minutes, whatever, it goes off and you have to write down what you did. You just have to be very strict with yourself. Another good saying I heard is, if you want to be an entrepreneur, a successful entrepreneur, you have to be the harshest slave driver for yourself. It's not about bossing around other people, it's about bossing around yourself and not letting yourself rest. Like all right, you rest when you can, but yeah it's important to be very strict with yourself. That's what I was-

Zach Loertscher: Yeah, awesome. More golden nuggets. I hope everyone who's listening is taking notes 'cause this is awesome.

Kirill Eremenko: This is a fun podcast, it's like all of a sudden a reversed situation. All right, well let me ask you a question then.

Zach Loertscher: Sure.

Kirill Eremenko: You mentioned entrepreneurship, and you're in healthcare and data science, do you think that data science is a good space for entrepreneurship? Or is it a space where you solely should focus on building a career and progressing up the career ladder?

Zach Loertscher: That's an awesome question. I haven't been in the "real world" yet, so I'm still finishing up my degree and everything. So take my response with a grain of salt, but I think that the data science field is probably the best field to be entrepreneurial in because it has everything set up for it on the internet. Everything that is popular is opensource, everything that you'd ever want to share can be shared and is being shared and people are consuming it. If you're wanting to build your brand or your business or whatever it is, data science is probably one of the best fields for it right now because if you think about it, for other fields, not a lot come to mind at this moment. I don't know what it is about data science but it's really taking off online. Since that's the future of our society, is this collaborative, cohesive societies, I think it's probably one of the best fields.

Kirill Eremenko: Yeah. Yeah, no. That's very cool. You definitely can connect with the right people apart from the resources Amazon Web services, or [inaudible 00:42:23] SQL, and all these tools, Python, I think they're a [inaudible 00:42:27] tool, all these online tools and datasets. The other thing is that you can connect with the right people to build this team, or even international team of Data Scientists and make things happen. That's what Kaggle's all about, right? They have some projects where you can participate as an individual Data Scientist but there's some projects where you're just not allowed to participate, as far as I remember. You're not allowed to participate as an individual, you have to be part of a team, right?

Zach Loertscher: Yeah.

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

Zach Loertscher: Yeah, it's awesome. And something else to note that I've noticed, I've been pretty heavily in the job search for the past few months and something that might make the entrepreneurial route more appealing is it takes a very long time to set up the data infrastructure required to really have a good functioning data science team. And you can correct me if I'm wrong, but to have your data warehouses all set up and normalized and to have your data capturing processes all automated, if a company just jumps on the data science train and hasn't done any of that prep work and you were hired by that company as a Data Scientist, you might find yourself in a situation where, "I'm just not doing what I was expecting." Right?

Kirill Eremenko: Yeah.

Zach Loertscher: So, that might make it more appealing as well 'cause a lot of companies are still making that transition right now. I put a post out a couple months ago talking about this and it got really good response as well. Talking about this, how every company is at this different stage of their data evolution. Some companies are towards the backside of things, they're doing everything in excel spreadsheets and email and other companies, they have all their data is all distributed, it's all in the cloud, they've got realtime reports going, they have realtime models running and making decisions. So yeah, when you're comparing the entrepreneurial side of things to the company side of things, the entrepreneurial can be a little bit more appealing in that sense. You get a little bit more control over what you're doing and maybe you don't have to wait so long for a lot of those things to happen right now.

Kirill Eremenko: No, that's definitely a good point. The difference is or can be, it depends on how you set up the entrepreneurial side of things but let's say if you're a Data Science Consultant, difference is you only need the tools that you actually need, that you are used to. I hear you use Python and Excel, or Python and SQL, or the combination that you like, Python/Tableau/Excel and you set them up for yourself and then in any business you go and you're like, "All right guys, I'm here. I can help you out with this specific type of problem, here's my rate and give me your data." They give you their data, you take it back, you upload it or even if you use it through their tools, you can make sure that they've set ... if they don't have it set up and if it's going to take a long time, you just move on to the next line.

Either way you do it, you have those tools in your arsenal and you perform the analytics and that's it, that's all you're worried about. You're not worried about all the red tape, you're not worried about waiting for approvals and so on and so on. You have a plethora of choices of companies that could be your potential clients because ultimately any business has data these days, you just need to show them that you can add more value than you're gonna cost them, that's it.

Zach Loertscher: Right, right. Absolutely.

Kirill Eremenko: Yeah.

Zach Loertscher: Absolutely.

Kirill Eremenko: We had a guest on the podcast almost a year ago now, and I still really like the approach that he takes and it's about not charging the client until they see value. So as a consultant or a Data Scientist ... and that's a starting point for a data science entrepreneur, you being a consultant, you can go into other spaces later on, make great products and stuff. But there, to get started as a consultant, you say, "Hey, I can add value, I'm not gonna charge you anything, I'm gonna do this project on my weekend." You do the project and then you say, "Hey, if you like it, you pay me. If you don't like it, that's okay, you keep the results and no problem." At the end of the day, if your project added ... I don't know, let's say it's a business that makes $100 thousand dollars a month and you just added 10% to their bottom line, you added $10 thousand dollars every single month, from now on they're gonna save $10 thousand dollars.

It's pretty obvious that they're gonna be okay with paying a consultant like that, a certain amount that is around the $30 thousand dollar mark or $50 or whatever.

Zach Loertscher: Oh yeah. Yeah, absolutely.

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

Zach Loertscher: Yeah.

Kirill Eremenko: Okay, all right. Let me ask you another question. What is your favorite part about data science?

Zach Loertscher: My favorite part of data science. I think I'll go back to when I really discovered it, I think the most exciting part for it, of data science for me, and it still is, it's just the idea that you can give power to a system to make decisions. Does that make sense?

Kirill Eremenko: Mm-hmm (affirmative).

Zach Loertscher: In my mind, I mean, maybe a lot of people don't think that's exciting but for me it's really exciting. I think that, that's just an amazing thing because I've worked at places where decisions are made by gut feelings or people maybe don't use the data to the fullest capacity that it could be used to. But even taking it a step further and saying, "Look, the data, we can use this and harness this to tell us things that we could never find out otherwise.", is really exciting for me. The other part that I think is really exciting about data science, is I love the data visualization part of it. I love being able to take something that's hard and coarse and rough as math and numbers and put it to a visual that people can understand and digest and really gain insights from. Those would be probably my two favorite parts. Sorry, I know you asked for one and I gave you two.

Kirill Eremenko: No, no, that's cool. No, that's good. Yeah, no, I like that. I like that you have at least a couple of things that you're super excited about in data science and definitely a very diverse field where anybody can pick what they're most interested in. Like somebody listening to this podcast might disagree with you in the sense that they have their own preference, they might be excited about the data preparation part, or identifying the challenge or the problem, or talking to clients and things like that. Totally normal, totally agree with that.

Zach Loertscher: Absolutely. What about you?

Kirill Eremenko: For me?

Zach Loertscher: Yeah.

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

Zach Loertscher: It's hard to choose.

Kirill Eremenko: Yeah, I know, it's hard to choose. I think for me it would be breaking down the complex into simple, it's that part where I know the insights, I know what I found. Now, how do I explain it? How do I make these faces of my audience light up and see in their eyes that they have passed through a threshold concept, for instance. A threshold concept is once you learn something, that's a threshold concept, you never see the world the same way again, right?

Zach Loertscher: Yeah.

Kirill Eremenko: Like, "How do I make sure that they understand this and they can apply it and they can make their businesses better?" And so, that's probably ... again, just like you, I probably have a couple. That one and the other one would be the whole investigation process, the whole digging to find the insights. Once you're in the project, you really with your heart and soul in the project, it's really fun. You can get lost in the project that time will fly by, you know that feeling, right?

Zach Loertscher: Yeah, yeah, totally. Yeah. Yeah, you can spend a lot of time in the exploration phase but it's just 'cause it's so exciting and fun and new and you never know what you'll find.

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

Zach Loertscher: My least favorite part.

Kirill Eremenko: Yeah.

Zach Loertscher: I'm gonna go out on a limb here, I know a lot of people get into Data Scientist from a developer background so maybe I'll get a lot of flack for this but probably the programming, that's gotta be my least favorite part. And maybe it's also because I'm coming from a more business analytics side of things where I want my tools to be on AGUI and nice and easy to use. I don't like to worry about the syntax or spend forever on google finding things. That being said, I realize that there's so much power in being able to have control, exact control over your data, over your project and your work flow, but you really can't have inside of AGUI, so something like, I don't know, Tableau. It's an incredible tool but at some point it has limitations, or excel, it can be hard to automate sometimes. That's probably my least favorite part, but I recognize that, that's also one of the most powerful parts too.

Kirill Eremenko: Yeah, gotcha. I agree, I like programming but I don't like the getting lost in the specifics of certain algorithms.

Zach Loertscher: Yeah.

Kirill Eremenko: Sometimes you gotta remember to add this line, you gotta remember this part of the algorithm, and this hyper parameter, the tweaking of these statements. So, I like creative programming when you can just come up with stuff and make things, like even you're writing your own algorithm. That's really fun, you're writing something that's ... but sometimes when it gets too mechanical and you forget something, the debugging of the code, that can be quite tedious. Especially if you forget or you don't notice that there's an error in the code, not because you're approached, but just because you forgot something that's part of an algorithm. Definitely then that can be a bit tedious.

Zach Loertscher: Yeah.

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

Zach Loertscher: And to anybody who's listening who's maybe at the initial point of this learning curve, for me, that learning curve's been more of a brick wall that I haven't really climbed up it, I've more smashed into it as much as I could until I finally have broken through, at least a little bit I think. If it's hard for you, it's been hard for me too, so don't worry.

Kirill Eremenko: Yeah, yeah. Well, yeah exactly. At the end of the day, if somebody really doesn't enjoy programming, there's so many other ways you can be a Data Scientist, even without programming. Like you said, there's so many great tools like Tableau for instance, where it can add so much value programming a single line of code.

Zach Loertscher: Yeah, absolutely.

Kirill Eremenko: Yeah. Okay, well let me ask you one more question before we wrap up. What career aspirations do you have? I know you're in the health industry now and you're starting out as a Data Scientist, is there anything you're aspiring towards? You want to be a Chief Data Officer, or Chief Data Scientist? I don't know, do you want to maybe have your own business one day? Or do you want to be an executive, like a Chief Executive Officer that uses data science? I don't know, I've mentioned a few executive positions, I don't know why, maybe there's other roles that you're more interested in, but what's on your list of aspirations?

Zach Loertscher: I would say I'm pretty easy to please, I don't really aspire to super, super high level positions because I want to always maintain a good work life balance. I never want my work to become more important than my family or taking care of my wife, whatever it might be. So I'd say the biggest aspiration for me is just to find a position in which I get to do data science and also maintain that all in balance. And maybe that'll involve some give and take with things like pay and salary, but that's ultimately my goal because I feel there are a lot of things in life that bring happiness besides prestige or position or money. For me, the title isn't super important as much as the work life balance. And also, is really important, is the people I'm working with. Are they collaborative? Are they excited to be at work? Or is it a team that just shows up and just does what their told and then goes home, you know what I mean?

Kirill Eremenko: Yeah.

Zach Loertscher: Those are probably the two most important parts for me, the title can come later I think. That would be my highest aspiration.

Kirill Eremenko: Gotcha, gotcha. Fair enough, that's a very fair answer and it's good to hear you got your priorities sorted out in a nice way. Okay, well Zach, thank you so much, enjoyed the new kind of format of this episode. Before you go, where can our listeners contact you, get in touch, follow you, learn more about how your career progresses?

Zach Loertscher: The best place is LinkedIn, so just look me up on there and connect with me, I won't reject you, I promise. The listeners of this podcast are great people, I'm sure, as well as everyone else, so that would be the best place to get in touch with me.

Kirill Eremenko: Gotcha. All right, and Zach's got a very interesting spelling of his surname, so we'll include the link to your profile on the show notes, people can connect with you there. Okay and one more question for today, what's a book that you can recommend to our listeners to help them enhance their career?

Zach Loertscher: It's not necessarily a data thing, okay, it isn't a data science book and maybe it's been recommended before, but I love the book, How to Win Friends and Influence People, by Dale Carnegie. It's a little bit of an older book but it is probably one of the best books about forming relationships with people and really learning how to, I don't know, not just win friends and gain influence, but really, I don't know, build those relationships. Because that can be one of the most satisfying parts of your work, of your life. Even when it comes to the data science part of things, it will help you with communicating things like Kirill has mentioned earlier, breaking complex things down to make them simple and making you less of a robot and more of a human, if that makes sense. That book has had a big influence for me and I hope that anyone who's listening goes out and reads it, it's awesome.

Kirill Eremenko: Definitely, fantastic book. I also recommend it and it's come up three times today.

Zach Loertscher: Oh really?

Kirill Eremenko: Yeah, definitely worth picking up. All right Zach, thank you so much for coming on the show. I hope our listeners enjoyed our chat, I definitely did, it was a nice, pleasant conversation and I think there's quite a bit of value. I definitely learned a few things from you, thanks so much.

Zach Loertscher: Yeah, I learned a lot from you. Thank you so much for having me on the show.

Kirill Eremenko: There you have it, that was Zach Loertscher, an aspiring Data Scientist and we discussed quite a lot of different things. Hope you enjoyed the show, and a bit of a different format this time where I answered a few of Zach's questions. My favorite part probably was when Zach talked about the way he goes about or the way he thinks about selecting an industry to get into, that it's important to speak to someone who's already in there, or who got into there recently or is getting into there. So that you can get their perspective, you get some insider knowledge about that industry and you make the right choice about your career.

On that note, make sure to get in touch with Zach, you can find his LinkedIn URL at the show notes at www.superdatascience.com/177, where you'll also find the transcript for this episode and any other materials that we mentioned. I'm sure Zach's gonna post some very cool and interesting updates in the coming future, and of course he'll be happy for you to get in touch and answer any of your questions you have about building a data science career and learning data science for yourself. On that note, thank you so much for being here today. Can't wait to see you back here next time, until then, happy analyzing.

Kirill Eremenko
Kirill Eremenko

I’m a Data Scientist and Entrepreneur. I also teach Data Science Online and host the SDS podcast where I interview some of the most inspiring Data Scientists from all around the world. I am passionate about bringing Data Science and Analytics to the world!

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