Are you having a post-DSGO2018 Conference depression? I think you’re not the only one so here’s a good fix for you!
We have Sasha Prokhorova as a guest today in the SuperDataScience Podcast to talk about her DataScienceGO 2018 experience. Aside from this, she also shares about her engineering background, her passions, and other stuff so make sure to tune in!
About Sasha Prokhorova
Alexandra (Sasha) Prokhorova is a Senior Electrical Engineering Student of San Francisco State University. She’s passionate about expanding her skillset by learning Data Science with various resources and tools.
From the moment I talked to Sasha during the conference, I knew I wanted to feature her here in the podcast. Sasha was very passionate and eager to build her career with the help of data science. Sasha is an electrical engineering student and was hoping to learn more when she attended the DataScienceGO 2018. Of course, she got what she was hoping to get (and a lot more) from the event.
During the first part of the episode, you hear Sasha share her favorites from the talks she attended. Two of the best takeaways for her were:
- “The market doesn't give a crap about your dreams.” – Ben Taylor
- “What is possible is often limited by how hard you try.” – Rico Meinl
Sasha says her DSGO 2018 experience was far from usual with the conferences she attended. Well, tell me a conference you’ve gone to that start the days with meditation and/or dancing? She was also astounded by how approachable and lively people were during the entire 3 days even though the schedule were filled with talks and activities. She was thankful to have this kind of avenue to get herself out there and meet people in the industry.
So, what is an engineering student doing in a data science conference? Sasha believes the data science skills are necessary for everybody. As they say, walk the talk. She, herself, is enrolled in online classes and self-learns a lot of tools (Python, Tableau, etc.) She reads a lot of data science books and listens to a lot of podcasts.
Sasha also shares her biggest failure – or, as Sasha calls it, the ‘temporary lack of result’ – is having a hard time approaching recruiters. If you’re like Sasha, then don’t miss out since a massive part of the episode is where I give tips for Sasha. I’m speaking from experience here when I say that don’t chase the recruiters but instead build yourself and put yourself out there so that they are the ones who will come looking for you.
As part of the advice I gave Sasha, I challenged her to write an article on her LinkedIn that will jumpstart her career. Check it out here! If you guys are also up to this challenge, post your article and send the link to [email protected]. The lucky one shall be reshared by SuperDataScience’s LinkedIn profile which has 25,000+ followers!
In this episode you will learn:
- Sasha’s best takeaways from DataScienceGO 2018. (06:00)
- DSGO 2018 is the best way to widen your network and meet new people. (11:54)
- There is noticeable greater female participation in DSGO 2018. (14:00)
- Sasha’s outstanding skillset: from electrical engineering to data science skills. (15:00)
- How does Sasha leverage to be more successful in data science? (20:20)
- What resources and tools do Sasha use to expand her data science knowledge? (23:46)
- Be passionate about something. (30:45)
- Call failure as the ‘temporary lack of result.’ (33:22)
- Kirill’s tips for your next job hunt. (35:46)
- Kirill’s challenge for Sasha and the SuperDataScience followers! (47:03)
Items mentioned in this podcast:
- Mental Models: Continuous Journey from Entropy to Equilibrium by Sasha Prokhorova | LinkedIn
- DataScienceGo 2018 Conference Recordings
- Confident Data Skills: Master the Fundamentals of Working with Data and Supercharge Your Career (Confident Series) by Kirill Eremenko
- Learn Python the Hard Way by Zed A. Shaw
- Statistics for Data Science by James D. Miller
- General Assembly – San Francisco
Kirill Eremenko: This is episode number 203 with aspiring data scientist Sasha Prokhorova.
Kirill Eremenko: Welcome to the Super Data Science podcast. My name is Kirill Eremenko, data science coach and lifestyle entrepreneur. Each week we bring you 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 back to the Super Data Science podcast. Ladies and gentleman, I'm very excited to have you on the show today. You can probably already feel the energy that I have, and that is because I just literally just now got off the phone with Sasha Prokhorova and we had an amazing podcast session which you're about to hear.
Kirill Eremenko: So, what did we talk about in this session? Well, first off, what you need to know is that Sasha and I met at Data Science GO 2018, which at the time when you're listening to this podcast was just over a week ago. In this session what I did was I asked Sasha about her experience at the event. I found that this was a much more interesting way to share with you some of the highlights rather than me telling you the highlights that happened at Data Science GO. It was very cool to hear them from Sasha's perspective, from an attendee's perspective. Through her lens you will see what her takeaways were and what were some of the key things that she learned from some of our speakers, like Ben Taylor, Rico Meinl, Randy Lao, and some other people. So, especially if you missed out on Data Science Go 2018, then this will be a great opportunity for you to catch up on some of the things, some of the key takeaways that an attendee had from this conference.
Kirill Eremenko: The other thing that we did is we talked about Sasha's background experience and her journey into data science. She's been learning data science for one and a half years and she actually brought up the usual concern that I hear that how do I get a job, it's very hard to apply for jobs and get through and get recognized and actually get invited and get job offers. I challenged Sasha on that, you will hear it, I had a whole rant on what I think on this topic and gave my advice on this topic. So, you'll hear that and plus I gave Sasha a challenge. A challenge to get her name out there and skyrocket her career.
Kirill Eremenko: In this podcast, by the time you're listening to this, she should have completed her challenge so stay tuned and inside the podcast you will know how to verify if she has or hasn't completed her challenge. I think that'll be a fun game. And, plus, to make it even more fun, during the podcast I announce the same challenge but in which you can participate in and there's a prize. There's a prize for the person that will do the best job on this challenge and you'll learn all about the details throughout this podcast and the prize is something that you don't want to miss out on. It's something that will help skyrocket your career and take it to the next level.
Kirill Eremenko: There we go. That's what this podcast is in a nutshell. I'll leave a cliffhanger like that for you and without further ado, I'm going to introduce to you Sasha Prokhorova, an aspiring data scientist.
Kirill Eremenko: Welcome, ladies and gentlemen to the Super Data Science podcast. Very excited to have you on the show today and we've got a very special guest, Sasha Prokhorova, calling in from San Francisco.
Kirill Eremenko: Sasha, welcome to the show. How are you today?
Sasha Prokhorova: Doing wonderful, thank you very much, Kirill. I'm very happy to be here.
Kirill Eremenko: Awesome! Very, very cool because for everybody out there, we literally just met with Sasha five days ago at the Data Science Go 2018 event and it was legendary. Had such a great time. Sasha, tell us a bit about Data Science GO 2018. How did you enjoy the conference?
Sasha Prokhorova: It was such an amazing event. I got to meet and connect with a lot of interesting people in the industry. It was not the usual format of the conference that I was used to. [crosstalk 00:04:24] There was a lot of ... (laughs) ... A lot of informal aspects, for instance, all the speakers were so personable and approachable and we started out the day with a little yoga and meditation, as well as a little dance. I thought that was refreshing.
Kirill Eremenko: That's awesome. That was very planned and also I think it went very well. A lot of people were enjoying it and opening up. Do you feel like you opened up? Do you feel ... Did you actually feel the energy in the room go up after all of those informal elements?
Sasha Prokhorova: I did, certainly. I felt very inspired.
Kirill Eremenko: I had attendee come up to me at the end of day one and he said ... well, at the end of Saturday and he said that "Hey, Kirill, the energy's so good here that, so high that I only now realize that all I had for food was a sandwich in the morning." And then he even skipped lunch even though there was, like, there was a network he liked, he skipped lunch because he was so into talking somebody and then he realized that he's not hungry and he's not tired simply because of the energy in the room which I totally loved, really loved everybody contributing. I think it was like a community effort in that sense.
Sasha Prokhorova: That's actually how I felt during, pretty much, most of the day out there at Data Science Go. I almost felt like I could have forgotten to eat because I just so absorbed in meeting people and talking to them and learning new things. That's the fear of missing out in a nutshell. [crosstalk 00:06:01]
Kirill Eremenko: Nice. Tell us what was your favorite talk?
Sasha Prokhorova: I really enjoyed the talk by Ben Taylor. The opening phrase was the market does not give a crap about your dreams. I thought it is very true because it's not about people seeking the opportunities on the market. It's what the market needs and this is what the market is going to select. The market is going to select the people that are right for solving certain problems [inaudible 00:06:38] particular company. It's all about the needs of a particular company.
Kirill Eremenko: So what was your main takeaway for your career from that phrase from Ben Taylor's talk? 'Cause it sounds like maybe for somebody listening to the podcast who wasn't at the conference, it sounds a bit like, I don't know, like pessimistic that the market doesn't really care about your dreams. I think Ben put it ... started off like that but then he explained it in a way that sheds light on the whole thing. What was your main takeaway?
Sasha Prokhorova: Important to have a passion and even borderline obsession. My main takeaway from this talk is the lack of experience is not really the end of the world because before when I was looking for jobs and internships in the industry, I was getting a little frustrated in it sometimes because you would need job experience to acquire job experience. It's almost like needing a pair of scissors to open a box that scissors came in.
Kirill Eremenko: That's a great analogy.
Sasha Prokhorova: It's almost like, given [inaudible 00:07:45] and that circle and Ben Taylor's talk gave me a really good insight about how to break out of the circle. For instance, it said lack of experience is a crucial but if you're capable of doing a project, my takeaway from it is just find a data set that you're passionate about and pick a data frame, decide what to do with it, and showcase your work. Showcase your work to a potential employer or to all those followers on Linkedin or even showing the family. Just get your work out there and show that you did something productive with your time. That you learned, you dared, and you achieved.
Kirill Eremenko: Fantastic. Love it. Tell us who else, who else, who's else's talk did you enjoy? Because you were there for the training sessions on Friday, but then the main event is Saturday, Sunday. That's one and a half days. I think we had close to 25 speaking sessions. Who else did you like? Who else did you love there?
Sasha Prokhorova: I really enjoyed attending the talk by Rico Meinl. My favorite quote from him is "What is possible is often limited by how hard you try."
Kirill Eremenko: Wonderful. Rico, I heard he did a fantastic job. He flew all the way from Germany. Do you know that Rico was an attendee last year?
Sasha Prokhorova: No, he did not mention that.
Kirill Eremenko: So he was an attendee last year, DSGO 2017, and then during the event he came up to me and he said, "Kirill, I want to be on this stage next year and I want to help inspire people." To that I said to him, "Hey, Rico, that sounds really cool, but you need to prove that you can do it. That you are going to actually bring value to people." And so, what he did is he went back to Germany. He started an AI meetup, which is now attended by several dozen, if not a hundred, people, it's just like once a month, once every several months. So, a meetup on AI [inaudible 00:09:56] learning, then he introduced artificial intelligence in the business that he's working for and the company he's working for.
Kirill Eremenko: He did quite a few cool things like presentations on AI and things like that, and then he came on the podcast and when he told all, all this, he told me all about this, I was like, "Rico, you have to come to Data Science GO. You have to present." He didn't take it lightly, that invitation lightly. He actually prepared his talk and then he hired an acting or like a speech, speaking coach, who helped coach him how to do this talk. So, this guy's really serious about the things he gets started in and, hence, the result. Everybody was very impressed with his talk.
Sasha Prokhorova: Wow! His dedication is truly admirable. He's such an inspiration for all of us.
Kirill Eremenko: Yeah, he's wonderful. Wonderful. Ben Taylor. Rico Meinl. How 'bout influencers? How 'bout people that you got to meet there, like who are also giving talks, but did you have ... Were you excited to meet the people that you follow on Linkedin in person?
Sasha Prokhorova: Absolutely. One of them would be Randy Lao. He's a great resource to follow for those people who are new industry, in the industry of those aspiring data scientists. He's posts are just so informative and incredibly concise and it's basically just a how-to instruction. The algorithms that you need to learn. The books you need to read. Just very on point.
Kirill Eremenko: And what was he like in person? Was he different to what you were expecting?
Sasha Prokhorova: He was very nice and approachable and kind. He was very appreciative of all the attention.
Kirill Eremenko: He's a very, very cool guy and I ... What I've found actually during the whole event was that most or all, almost all of the influencers that, who were there from Ben Taylor, Randy Lao, we had Nadieh Bremer, we had lots of ... Terry Singh, all them were very humble. They were very open to talking and giving advice and connecting with people and hearing attendee's stories and just getting into this community and really giving back. So that's what I really appreciated from them and I think it resonated well. There were so many, so many great conversations. What was the most surprising thing that you learned at the conference?
Sasha Prokhorova: The most surprising?
Kirill Eremenko: What was most impressive? Something that really got you inspired and, apart from the talks, I mean during networking opportunities with people?
Sasha Prokhorova: That everyone was really approachable. Data scientists, data engineers, you have to remember that they're people at the end of the day, very brilliant and outstanding people but they're people and it's important to connect. It's just important to get yourself out there, no matter how shy you are, and no matter how hard introducing yourself and talking to people is. It's really important and actually just get yourself out of the comfort zone.
Kirill Eremenko: That's a very good point. Oh, and one more thing I wanted to ask you. As a woman, what did you feel about how represented women were at the conference in terms of speakers and in terms of attendees?
Sasha Prokhorova: The demographics were very balanced. There were a lot of women who attended and I definitely felt a lot of support from everyone in the industry regardless of gender.
Kirill Eremenko: That's very, very good to hear because one of our, one of the things that we're trying to improve and change is the status quo. In data science, typically, it's about, the ratio of male to female is about 90 to 10. So 10% female in the industry, about so, but at our event for instance in terms of speakers, we had 35% female speakers and in terms of attendees, I don't have the numbers yet but as soon as we have the stats, I'll announce them as well. I think, I think we did quite well in that sense and it's important to inspire and show role models for aspiring data scientists. That, regardless of your gender, race, background, you can succeed in data science. That's, I think, is good to hear that you felt that at the event.
Sasha Prokhorova: No, absolutely.
Kirill Eremenko: Well, shifting gears, thank you very much for the quick overview of DSGO and your experience there. Let's now move on to your journey through data science. So, one of the reasons, for our listeners out there, one of the reasons why I decided to invite Sasha to the podcast was when we met at Data Science GO, I found her story quite inspiring. Actually, very different to what's, very unique, I'd say, or unique and quite inspiring for many people out there who are starting into data science or who are already in their journey in data science and want to look back and see how it was to go through it.
Kirill Eremenko: So, in short, Sasha will give us a background just now but Sasha's in a bit of a different industry. She's now specifically in data science. She's an electrical engineering student, but Sasha feels the importance of knowing data science and integrating it into her career. So, that's what I want to dig into a bit further and why you feel that way and how you go about it. How you're structuring your journey through data science and what [inaudible 00:15:50] you. So, to kick us off could you, please, give us a quick overview? Who is Sasha Prokhorova and how, what are you doing in San Francisco?
Sasha Prokhorova: I'm currently a student at San Francisco State University, pursuing my undergraduate degree in electrical engineering. Originally I'm from Russia, [inaudible 00:16:12]. That's where I obtained my first degree in linguistics.
Kirill Eremenko: So why did you just jump from linguistics to electrical engineering? That's a radical shift. That's like going from South Pole to the North Pole.
Sasha Prokhorova: I do agree. I've always been interested in languages while growing up, but also, when I grew older, I haven't always been good in math. Not at least until my early twenties. That's when I feel the gears really shifted somehow because I noticed a lot of people say that math is not really their thing because I think it takes a certain age to be able to appreciate certain mathematical concepts because of how abstract they are and I'm inclined to believe that this is what happened to me as well.
Kirill Eremenko: Okay, gotcha. That's very interesting and why data science then? So electrical engineering, yeah, I understand, but how is data science related to electrical engineering and how are you leveraging it?
Sasha Prokhorova: First of all, we're living in a world that's drowning in data, way more data than we can surmainly process. I'm a firm believer that it's very important to have certain data science and [inaudible 00:17:29] analytic skills regardless of the industry you're in and in order to maintain the edge in the competitive nature of today's world. It's just impossible to, it's very important to acquire those skills, at least, to any level.
Kirill Eremenko: Gotcha, gotcha. But is that just for technical professions like electrical engineering or would you say that's for management consultants and for, I don't know, somebody running a bakery store or for somebody who has a, who has a little tourism office? Do you think it's important to have data skills for anybody in this world?
Sasha Prokhorova: Well, yes, of course. We all produce data whether we want it or not and our customers do produce data as well, regardless of the industry we're in. If we are bakers or management consultants, we all use and produce data products to one extent or another.
Kirill Eremenko: Yeah, okay. I would totally agree with that. I think some level data acumen or data knowledge is necessary for anybody. But in your case, so electrical engineering, data science, are you planning on moving from electrical engineering completely to data science or are you planning to integrate the two and have a career that combines the two together?
Sasha Prokhorova: I don't believe I'm gonna move away from electrical engineering. I just enjoy this industry way too much. Currently, I'm working on a project of analog integrated circuit designs and I'm having a great time. But I do want to improve my data science skills and knowledge and I'm currently trying to teach myself some [inaudible 00:19:23] because it's just another passion of mine. Something that I enjoy to the great extent. I started going to some extracurricular classes outside of school in San Francisco. Thankfully, the data community is very strong in San Francisco and they offer us has a lot of resources to improve our skills and perfect ourselves. So, there's definitely a lot of things that you can explore and try. Such things as boot camps or evening workshops that you can just explore before you commit to the full time course. It's a great way to discover your passions and ...
PART 1 OF 3 ENDS [00:20:04]
Sasha Prokhorova: ...full-time course. It's a great way to discover your passions and interests and maybe even hidden skills and talents. Who knows?
Kirill Eremenko: Mm-hmm (affirmative). Gotcha. Gotcha. And now let's think about the other way around. So you already mentioned how you're going to use data science. Why are you going into data science now and like how that can help augment your career and take it to the next level. And in fact, how that could help anybody. But tell us the other way around, like how does your existing background help you be successful. As you mentioned, you have quite a diverse background, with linguistics and electrical engineering. How do you leverage your background to be successful or be more successful in data science?
Sasha Prokhorova: Well, actually I just started reading your book called Confident Data Skills, which I find an incredibly interesting read. And one of my favorite portions of it would be quote that data science is one of those skills that benefits from having experience in a different field.
Kirill Eremenko: Mm-hmm (affirmative).
Sasha Prokhorova: Such as linguistics in my case. Or history or management or consulting. I have very unusual background for other young professionals who are working in my industry. And I also have a very unusual angle that I approach problems, which also gives non-standard solutions.
Kirill Eremenko: Awesome, well tell us a bit about that angle. How would you describe the angle at which you approach data science problems? Very interesting.
Sasha Prokhorova: I would believe it's my ability to approach unstructured data due to my data in linguistics. And it's just my ability to read certain connotations that maybe a non-linguist would not identify right away.
Kirill Eremenko: Mm-hmm (affirmative). Okay, that's very true. Very, very interesting as well. So you're combining your linguistics unstructured data skills with... And what'd you get from electrical engineering? What kind of mindset or thinking do you leverage from that field?
Sasha Prokhorova: Mathematical background. It definitely implies a lot of structure, a lot of logic and a lot of discipline.
Kirill Eremenko: Okay. Gotcha. All right. So tell us then how do you go about learning data science? Like are you taking courses? Are you reading books? All right you mentioned like you're reading my book, which thank you very much. I'm very humbled to hear that you're enjoying it. What are your main points of contact with data science?
Sasha Prokhorova: I would definitely recommend couple of good books. One of them would be Learning Python the Hard Way. And there's also Statistics for Data Scientists. It's really well written and not a difficult read at all. But also use a lot of online resources, such as Code Academy and DataCamp. There is a lot of very good interactive exercises. And also I'm learning a lot of MATLAB because my school requires it. It's part of the curriculum for electrical engineers. And I recently discovered that you can do data analytics and machine learning in MATLAB, which made me even more excited. I can use my engineering background and just learn a couple new skills in MATLAB and I would be able to use this incredible and powerful tool for data analytics.
Kirill Eremenko: Mm-hmm (affirmative). Yeah, wow. That's a very good recommendation. So you started learning data science with Python. Is that correct?
Sasha Prokhorova: Yes. I was inspired by Craig Sakuma. He's one of the instructors in General Assembly. It's a school in downtown San Francisco. He taught me some Python and some SQL. And he was actually one of those mentors who made me believe that I can do it. I can program. I can learn coding. The way he taught Python and especially SQL, it totally made sense to me. He basically did what Ben Taylor suggested to do all along. Find the project that's exciting and important to you. He did it based on the music. We were analyzing his iTunes playlist in SQL. Not necessarily just for the genre or for the length of the songs, but for instance, how many songs does Craig have in his playlist that are love songs? And also what signifies a love song? Is it the word love, hug, kiss, or could they be used in any sarcastic contexts? That could be, that's one of the tougher projects for machine learning, too.
Kirill Eremenko: Okay. That's a very interesting project. When you were at the conference at [inaudible 00:25:03], did you attend Sinan Ozdemir's talk?
Sasha Prokhorova: Yes. Yes, I did.
Kirill Eremenko: Because Sinan is also an instructor at General Assembly. Or maybe he was, but he definitely spent quite a lot of time at General Assembly. And I just... In San Francisco as far as I remember. Did you know that about him?
Sasha Prokhorova: No, actually I did not. I cannot believe I missed out on that.
Kirill Eremenko: Yeah. Yeah, well, there you go. Yeah, I heard they have some very nice courses there. Okay. So do you attend like the General Assembly events in San Francisco?
Sasha Prokhorova: I do frequently. That's something that I enjoy doing after my regular classes at school. I would say spend the whole day at campus at San Francisco State, attending lectures and labs, I would spend some time in the library. But then in the evening, I would find something that's interesting and appealing to me that sounds like I might enjoy and I just go check it out. And General Assembly... And I just have fun meeting different people and learning new things.
Kirill Eremenko: That's very cool. And are those... How are you... In terms of technical complexity, how would you describe the General Assembly classes? Just for like listeners out there. Because General Assembly's not just San Francisco, it's all... I think it's nationwide for the US, maybe somebody else might want to attend one of these. Like would you recommend it for beginners or advanced data scientists? What kind of level do they have?
Sasha Prokhorova: You know, that's the beauty of this place. It's tailored for very diverse crowd. It works for very complete beginner. Even for someone who is just very curious about data science or machine learning. They can just attend an evening workshop and just get the gist of it and decide if it's right for them or not. And they have more advanced programs as well. Such as bootcamps and more full-time courses. So yeah, it's an amazing, amazing resource.
Kirill Eremenko: Mm-hmm (affirmative). Okay. Awesome. All right. Well tell us a bit more about your... You know, you mentioned you learned Python already. And how did you find learning Python? Like obviously everybody's background is different, and you had some experience, I'm assuming you had experience in MATLAB before Python? How did you find Python after MATLAB?
Sasha Prokhorova: I enjoy [inaudible 00:27:23] a lot because it truly made sense to me. It was very similar to MATLAB and the search and Python syntax structures. They echoed MATLAB in my brain.
Kirill Eremenko: Okay. Gotcha. And is there any other tools that you're looking forward to learning sometime soon?
Sasha Prokhorova: Tableau. That's actually one of my good friends, and one of my mentors who I met at the Open Data Science conference last year. His name is Pratyush [inaudible 00:27:52]. He suggested that I should learn Tableau as a first step in my data analytics journey, and just create a project in Tableau and showcase it.
Kirill Eremenko: Yup.
Sasha Prokhorova: Because Tableau is known to be a very flexible and eloquent, and yet very powerful tool. And I think it could be a good starting point for any aspiring data scientist or analyst.
Kirill Eremenko: Wow. Definitely. I really like Tableau, that's kind of like where... I think I started my data science journey from SQL, then I moved to Tableau, then came R and Python. Everybody has their own way. But, yeah. It's good to always kind of be looking forward to the next step, the next thing that you're gonna be learning. So tell me this Sasha, do you take courses on Udemy? Just out of curiosity?
Sasha Prokhorova: Yes, I do. I actually took your and Hadelin's course about data science careers. I downloaded a couple of courses about Python and I'm actually very excited to embark on that journey. Yeah-
Kirill Eremenko: Awesome. And I'm assuming, well from what you told me, that you listen to the Super Data Science podcast as well?
Sasha Prokhorova: Yes. It's actually one of my favorite podcasts. I discovered it when I was commuting to my industrial engineering internship in [inaudible 00:29:14] and yeah, I just came across it. And I was so grateful and lucky that this resource fell on my lap. Because I was actually looking forward to my commute to work so that I could listen to the podcast.
Kirill Eremenko: That's awesome. Thank you. Thank you for the comment. And tell me, how long have you been exploring data science for so far?
Sasha Prokhorova: I wanna say for about a year and a half.
Kirill Eremenko: Yeah and a half? Okay, so the reason why I'm asking all these questions is because I'm trying to understand... Or actually I just want to show to our listeners what passion means. What passion looks like. So as you can see, Sasha is reading books on data science, listening to podcasts, taking courses on Udemy and Code Academy and DataCamp. She's attending conferences, not just DataScienceGO, but she's also been to ODSC. She's attending the General Assembly occasionally when she feels like doing something fun after a hard day at University. Winding down with some data science at General Assembly. And I'm sure there's lots more other things that you do in this space. You follow people like Randy Lau on Linkedin and you find ways to get in touch with Ben Taylor, or maybe meeting him at a conference and asking him about some advice. So you're getting mentors directly or indirectly.
Kirill Eremenko: So as we can all see, like you, this, I wanna just show to our listeners, especially those who are starting out or those who want to like propel their career and you might be finding that your career's not really going where you want it to. Well, as Ben Taylor described in his talk, you've gotta be passionate about something. And this is what passion looks like. To me this is what passion looks like, these are the indicators of passion. Sasha is definitely a person who is passionate about the field of data science. Because otherwise she wouldn't be doing all this. Sasha would you agree that you're passionate about data science?
Sasha Prokhorova: Absolutely. I would say passion has a power to move mountains if you are determined enough.
Kirill Eremenko: Mm-hmm (affirmative). Definitely. Definitely. And now you're on this podcast. And I don't think that's a coincidence. Like I... Probably when we were there we didn't talk for long, but already just by your excitement and energy that you came into that conversation with I could feel your, you know passion it kind of like translates itself. And so therefore when somebody who's passionate, like maybe Sasha in your case, when you go for an interview in data science, you're gonna like in a 30 minute interview, the recruiters or data science manager, they will feel that from you as well. Just like how I felt it. And hence, it will be so easy for you to get any kind of career that you want. And people who don't feel it, that is kind of like they're going to be missing out.
Kirill Eremenko: And that's for our listeners out there, once again it doesn't matter if you're just starting out into data science or you're already an expert in data science, you wanna position yourself like that. You wanna be the person that's emanates this energy, this passion or bordering on the level of obsession, that people will come to you with job offers. So Ben Taylor had this example in his talk that there was a group of students that he was talking to and all of them were like you know I would love a job in data science, but it's so hard to find one. And among them there's this one student out of like maybe seven students. Among them there's this one student who had all the job offers because all the employers in the area or all the companies that knew about this group, they knew that this guy was super passionate and they could feel it from when he was sharing online, how he was talking, what he was doing. And you want to be in that position. You want to be getting all the job offers. Would you agree with that, Sasha?
Sasha Prokhorova: Absolutely.
Kirill Eremenko: Yeah.
Sasha Prokhorova: It's important to love what you do and have good work ethic. And just keep trying and trying and trying without being afraid of failure. Because failure's just a natural part of the learning process and it's inevitable. And I think, actually as you said during the conference, that we learn a lot about success, but we also learn ten times more from failure. Because as long as it's important to know what to do, from failure you actually learn exactly what not to do.
Kirill Eremenko: That's a great way of putting it. Okay, speaking of failures, tell us a bit about, what is... Or let's talk about your failures. What would you say has been like your biggest failure, that you've learned from the most, in this pursuit of data science and technology and data and career some.... Attached to data.
Sasha Prokhorova: Well I wouldn't necessarily call it a failure yet, because I'm just so new to this industry. I haven't even entered yet. I would call it a temporary lack of result.
Kirill Eremenko: Mm-hmm (affirmative).
Sasha Prokhorova: Because it's also important to know how to approach recruiters correctly because this field is so competitive and it's so cut throat. And recruiters, both in Linkedin and in real life, they're so overwhelmed by the volume of applications they received. So I've applied to probably hundreds of positions that are relevant to my field and I either received either thank you but no thank you or no response whatsoever. But I don't let it discourage me, I just keep trying. So basically, short answer to your question, my main failure would be not getting an entry level position yet, since I'm still at school.
Kirill Eremenko: Yup. [crosstalk 00:35:10]
Sasha Prokhorova: But my main takeaway from all this job hunt and the conference would be for recruiting managers correctly. I have the theory that I call what keeps you up at night. You would ask the manager what are the main challenges that your company faces nowadays and what can I do to help you to solve those problems? To improve your company and to achieve the goal by the end of the year that you want to. What can I help you with to help us both succeed?
Kirill Eremenko: Mm-hmm (affirmative). Okay. All right. So, I've got a few comments here. So first one, I would like to comment that I wouldn't agree that it is a cut throat field, and I'll explain why. Because when I was a consultant in Deloitte, right? And I know what cut throat means and what cut throat looks like. And that is like a completely different story when you are, when people who you're working side by side with... I'm not talking about this about specifically at Deloitte, so don't wanna get anybody in trouble or anything like that. But just I've seen the world of consulting, and that is cut throat, right? Like when people are, like you kind of like think they're friends, and then there's promotions in question and you have this two year policy to... Like you're either up or out within two years. You either get a promotion or it's implied that you leave the company because you're not good enough. And you know, in that kind of environment, where everybody's competing with each other, that's what I define as cut throat.
Kirill Eremenko: And data science, I think data scientists as a community are much stronger. Like I wouldn't call consultants as a community, like I'm sure they are communities in consulting that are fantastic, but overall in general it is more cut throat. Whereas in data science, everybody wants to help everybody. Everybody's sharing their code, everybody's commenting on each other's mistakes. There's plenty of resources like Quora and Stack Overflow and Kaggle and wherever you ask your question, you get answers very quickly. I would say it's a more communal effort. But I do agree with you in the sense that, the fact that there is so much, like there's a massive demand for data sciences, but there's an overwhelming supply. There's so many people that have gotten into data science just for the sexiest job of the twenty-first century or the massive salaries and so on, that are there maybe for the wrong reasons. Or that are... You know recruiters have so much to choose from, and in that sense yes, it can be very difficult to get those applications and job positions. So, in that sense, disagree that it's cut throat. I would say that terminology is different, it's just that it's overwhelming supply at the moment.
Kirill Eremenko: On the other hand, what I wanted to say is, do you mind if I give you a bit of advice in terms of how you approach your career? And I think that it would be helpful for our listeners as well.
Sasha Prokhorova: Please do, I would love that.
Kirill Eremenko: All right. So what I would say in this case is what you're doing, I would say what you're doing wrong and what a lot of people are doing wrong, is they're going for the recruiters. Yes, inevitably you're going to send hundreds of job applications and you're going to get refusals, you're gonna get people turning you down. And it is not a reflection of your skills or passion. Like we already established on this podcast already that you're definitely passionate about data science, you're doing so many things, you're learning. You're gonna go a very long way in this field. Like I can already tell that you have a very bright future.
Kirill Eremenko: The question is, how do you people, as you said, there's so many job offers or job applications that recruiters get that they get like for every offer, for every job posting they get maybe a thousand, I don't know a couple hundred job applications. And it is physically impossible to go through them. So no matter how great you are, if you're going through the standard pipeline, standard process, you will find that you are, they might just not see your application in the first place. Like if they were to see it, then you'll stand out to them. But if they don't see it, it's never gonna stand out. And moreover, as they say about 70 to 80 percent, not just as they say, studies have shown that 70 to 80 percent of jobs are filled or job postings are filled behind the scenes. They're never actually posted online for everybody to see. What we see online is all these jobs offers or job positions that recruiters and managers need to fill. That's just 30 percent of the whole job market. Most of the jobs get filled through referrals, through managers going out there before posting a job and just like looking for somebody through friends of friends, through people in your network on Linkedin and stuff like that. So, first step is, we only see 30 percent of the demand for data science-
PART 2 OF 3 ENDS [00:40:04]
Kirill Eremenko: We only see 30% of the demand for data scientists and moreover, for every job, there's hundreds of applications and therefore nobody sees your application. So, it's a losing game. You're playing a losing game and some people turn to get up numbers and they send a thousand applications and maybe one or two succeeds.
Kirill Eremenko: That's not the opposition that you want yourself to be in. Right? You don't wanna be scavenging for jobs and only getting the one where the manager did notice your application and therefore, you're just picking out of one or two jobs that might not ultimately be the best job for you, but that's all you have to choose from. You wanna flip the table. You want to be in the ocean of people applying for jobs, an ocean of applicants or data science professionals. You want to be like a shining star.
Kirill Eremenko: You want to be something that stands out, like if you look at an ocean in the darkness of the night and there's nothing there, it just looks black But if there's a ship sailing from left to right, you will see the ship right away, right? It stands out. So you wanna be that shining star. And how do you get to that level? How do you become the shining star?
Kirill Eremenko: Well, it's actually ... there's nothing difficult about that. You just have to start building your brand online. You have to start making some noise. You have to start making some ripples in the water so that you do attract attention, because if you're doing same thing as everyone else is doing, there's no way you're gonna stand out.
Kirill Eremenko: For instance, that's what I did, and I did this a long time ago when I was, you know, when was this? 2014, so four years ago when I was leaving Deloitte and I decided, I want a job. I don't wanna be in consulting anymore. I want a job. And one way I could've gone about it, and I tried to do it, but then I didn't have time, because I was still working at Deloitte. And I looked around, and one way I could do it is apply for jobs in data science, but then I realized that it's taking too much time. I'm way to perfectionist to just send out a standard template resume to all these jobs. I wanted to tailor my resume to every single position, write a cover letter. That was taking me hours for every application, and that was not sustainable, and not scalable. So I couldn't-
Sasha Prokhorova: Yeah. I'm guilty of that too.
Kirill Eremenko: Yeah, and so I couldn't turn that into a numbers game. I couldn't send thousand applications out at once, because I knew that I'm too perfectionist for that. So instead what I did was, alright, let's flip the game. Let's flip the table, and instead I'm just gonna start posting on LinkedIn. Not even huge stuff. Not even [inaudible 00:42:33] writes an article, which takes a few weeks to write. I just started reading, finding stuff online that is relevant to data science and technology, reading it, commenting on it in like one or two lines of comments, and then re-posting it on LinkedIn, saying, hey guys, I found this article. I found it interesting. This is what I thought about it. And I think it's controversial. I think, I agree, or I disagree, it's my opinion. And I would post that and I actually automated the posts. I would read all those in the weekend and then I would post them, get a tool like Hootsuite and post them throughout the week. You know you gotta post it like three times on Monday, or Tuesday, Wednesday, and Friday. Or Thursday when people actually read that stuff.
Kirill Eremenko: And within six weeks, magic happened. I started getting recruiters checking my profile, I started getting managers, and within six weeks I got three job offers. I'm not making this up, I had three jobs offers within six weeks for a very basic LinkedIn profile with only a couple of years experience in the field. All I did was just start making some noise and I didn't write my own class of articles, I just commented on stuff and I got a job offer from a pension fund which is in Australia called [inaudible 00:43:43] fund, in the city that I lived in and I got two job offers from banks in Sydney. From very large banks, I think both of them, or one of them was one of the big four banks in Sydney. I actually went through the interview process with I believe all three companies and then two of them, the third one, I just didn't go to the final interview stages. Two of them gave me job offers which one of them I picked and I went to it and I worked there.
Kirill Eremenko: In essence, all of them were almost double the salary I was making at Deloitte. So, not only I got job offers, not only I got double my salary, but I actually didn't have to do much. I didn't have to apply for any jobs myself. They just came to me. Right? So and now it's been four years later, the demand for data scientists has skyrocketed, the applicants - there's still an ocean. It's a bigger ocean, but it's not an ocean - not many people doing much about standing out. Still an ocean of applicants. But the demand has skyrocketed, so it's so much easier to stand out now. All you have to do is make some noise, post some articles, plus you could write about what you learned in general assembly, write a little article about how you went into data science, what you learned there. You don't even need a blog, you just write those in LinkedIn [inaudible 00:45:00] share them there.
Kirill Eremenko: You could write about what you're reading in a book, what you're taking in a course. You know, write up a couple of those things, share this podcast episode that you've been on. I have no doubt that within, by the start of next year, by the start of 2019, you will have so much attention. If on LinkedIn, if you get premium you can see who [inaudible 00:45:21] who is visiting your profile, who is seeing what you're doing, what the company they're from, what positions they are. You will see slowly managers will start popping up, recruiters will start popping up, and then the job offers will start coming. And that's all it takes. That's my thoughts on this.
Kirill Eremenko: What do you think?
Sasha Prokhorova: I completely agree. I believe blogging is the new CV as mentioned by Andy Parker, a medium whom I follow. And it just important to generate quality contents, and just put yourself out there.
Kirill Eremenko: Okay, my question is why, if you believe that, why are you not doing that? Was something preventing you from doing that?
Sasha Prokhorova: I'm trying to accumulate more skills and more knowledge that I can share with people.
Kirill Eremenko: Oh my, this is the typical issue. Why! You hear this all the time. This is fear that I am not enough. This is fear that I'm not good enough. You've been in this one and a half years. You can have so many people. There is literally 100s of thousands, as we've discussed at the conference, there's a shortage of 173,000 data scientists nationwide in the US right now. There's so many people. There's 100s of thousands of people going into this field. You're one and a half years of experience of learning data science is golden to tens, if not 100s of thousands of people.
Kirill Eremenko: You can start now. Just take the first step, write the first article. Make it, or just share something. You will see how many you have helped. And even if you help one person, that's already a massive step and trust me, you don't need to be an expert in this field to be able to share your experience and help people.
Sasha Prokhorova: Maybe all I need is just to begin. I just need to find a project that I am passionate about, and just start writing, start exploring, and just trying to find out what I'm capable of.
Kirill Eremenko: Okay. How about, do you want to make a commitment on this podcast publicly? Like Rico says -
Kirill Eremenko: What's it called? What is the term, a radical commitment? Or something like that?
Sasha Prokhorova: A reckless commitment.
Kirill Eremenko: Reckless commitment. How about we do one of those right now.
Sasha Prokhorova: Absolutely. I was actually quite enamored by his words that you can be an expert in something in three months if you commit to it. Let's say my commitment for the next three months would be finding a data set on let's say, [inaudible 00:47:53] or some resource like that, and just start working with it, starting to look for patterns and see what I can make of it.
Kirill Eremenko: Love it. I totally love that. I think we will lock that commitment in, but I think you can do better. So, do you have any exams in the next week?
Sasha Prokhorova: Yes, actually.
Kirill Eremenko: How many exams do you have?
Sasha Prokhorova: I'm kinda half-way through my midterms, so on Monday I have my integrated circuit design class, where I have to analyze the performance of certain MOSFET transistors.
Kirill Eremenko: And then after Monday?
Sasha Prokhorova: I have a communications systems and I also have a power systems.
Kirill Eremenko: Okay. Cool. So, do you think you'll be able to find three hours of free time until Thursday next week?
Sasha Prokhorova: Absolutely! I think I'm gonna break it into the increments of 30 minutes each day, which would bring me to six days of week on working on the project, without it taking away too much from my course work. I'm convinced I can do that.
Kirill Eremenko: Awesome. Okay, so, the new commitment that I'm offering to you right now on the podcast is that - so this session, we're recording this today on Friday the 19th of October, and this session is going to go live on evening of Wednesday the 24th of October. So that is five days away. My challenge to you is can you write a 500 words article that you're gonna share on LinkedIn by Wednesday, and then our listeners will be able to check, because once you write it, you will send us the link, or you send me the link, and I will include it in the show notes. So as soon as this session is live, so for our listeners, when you're hearing this, this session is live, on iTunes or SoundCloud, wherever you're listening to it, and that means that by now Sasha has finished writing her first 500 word article and it's live on LinkedIn and you can go to the show notes and check it out there. So the show notes, I'll announce where they are, and [inaudible 00:50:05]. You can go to show notes, click on it and read it.
Kirill Eremenko: How does that sound to you Sasha?
Sasha Prokhorova: It sounds really good. I'm very excited, yes, let's make complex, simple.
Kirill Eremenko: Nice, nice. Very good. So, we're going to do that and that's a good way - and then you can then e-mail Rico, and say, hey Rico I did your reckless commitment thing and this is what I came up with. And that's a good way to get - sometimes we need to kickstart, right? So, we need somebody or something external to force us to actually do something about our careers and lives, and this is going to help you kickstart into the process and then hopefully, once you've written the first article, and you've seen how many people you've helped and how easy it is - then you will get into the mood for it and maybe you'll start writing one per month or two per month, and that is the way, I think for you and many people in this field, that is the way to cause those ripples on the water so that you will be seen by recruiters and managers.
Kirill Eremenko: Sounds good?
Sasha Prokhorova: Yeah! I think, it sounds great. I think we all need to be pushed out of our comfort zone every now and then, and this is kind of what happened to me today, and especially seen Rico's and yours, and using it [inaudible 00:51:22] as knowledge and dedication. Those [inaudible 00:51:24] are really and truly contagious, and inspiring to me.
Kirill Eremenko: Fantastic. And I want to actually extend this invitation further to our listeners. If you are stuck in the same boat as Sasha and you know you've been applying for jobs, especially if you've been applying for jobs and with no success, then I challenge you to take one week - so this podcast is going live on the 24th, evening, 24th of October, so one week until the first of November, to write something. Doesn't matter how long it is, aim for 500 words, but even if you do 200, that's enough. And, share it on LinkedIn. Write, and then see what happens. See how you feel, how long it takes you, and in fact in order to make this even more fun, send your link to your article to podcast at SuperDataScience.com, and we will pick the best one and we will reshare it on our LinkedIn. So we will pick the best one that you guys write up, and we will reshare on our LinkedIn with 25,000 plus followers. So you can actually impact a lot of people.
Kirill Eremenko: But it has to be done by the 31st of October, so all submissions need to be in first of November. So, there's my challenge out to you guys, and that will help you kickstart your work career and if you already have a career as well, if you are already a successful data scientist doing plenty of work and your happy with everything, it's a great way to give back to the community and I also encourage you to put this bid into this challenge.
Kirill Eremenko: See Sasha what you did! You started a whole thing.
Sasha Prokhorova: Hashtag homework challenge!
Kirill Eremenko: Hashtag - yeah, let's call it hashtag SDS homework challenge. One word. Okay, awesome. That is really fun so I'm going to actually put a reminder for myself to check the submissions. What else? Tell us what else. We're slowly coming to a wrap up of this podcast, what are some thoughts that you would like to share with your fellow data scientists, maybe people you met at DataScienceGO, maybe - just people who are listening to this podcast, or getting into the field of data science or moving from another field into data science. What is something that you would like to share [inaudible 00:53:39]?
Sasha Prokhorova: I would say don't stop exploring and don't be afraid to discover new interests.
Kirill Eremenko: Mm-hmm (affirmative). Curiosity, right? Is the [crosstalk 00:53:50]
Sasha Prokhorova: Curiosity - absolutely.
Kirill Eremenko: Yeah.
Sasha Prokhorova: Curiosity and passion and thirst for knowledge, for constant learning. Next stop learning.
Kirill Eremenko: Yeah that's very true. Very true. I find curiosity - you know [inaudible 00:54:04] obsessions? I find curiosity my obsession. I find that sometimes I - for instance, let's say I'm cooking something, I don't know, a pasta or some beans or something like that, and then I know that the recipe says do this, but then sometimes I just have this idea of what happens if I do this? What happens if I put the ingredients in the wrong order? What happens if I add this ingredient that's not in the recipe, or what happens if I replace this with that. And sometimes, it's just such a burning desire to explore what will happen that I just like, what if? And I do it. And then it's either, most of the time it's a failed result, to be fair, but sometimes something epic comes out of it. It's not just in cooking, it's pretty much in anything that I do. I always - as soon as I have this question in my head, what if? I don't let it slip away. Very rarely have I let it slip away. I'm always, okay, let's do it.
Kirill Eremenko: Screw it, like Richard Branson says, screw it, let's do it, let's see what happens. And I think that's curiosity, right, in data science you gotta be the same. You gotta be like, what if I write logistic regression in this way, what if I apply this data set, what if I worked on this project, what if I read this book and so on. What will happen? And don't let that slip away. As soon as you have the what if, there's always gonna be another voice saying, I'm too tired, I'm too lazy, I really know that this other method is gonna work. Well nothing new comes out of doing the same things the same old way. You gotta try new things, and that's when you break boundaries, whether it's in science, in exploring new fields, or whether it's in your career and your personal life, in general and things that you are capable of doing.
Sasha Prokhorova: Yeah absolutely.
Kirill Eremenko: Awesome. Okay well, Sasha, thank you so much for such an inspiring session. I had a massive pleasure talking to you, I'm sure lots of people learned from this. Before I let you go, could you let us know where our listeners can get in touch, contact you, follow you, learn more about your career and see where it takes you?
Sasha Prokhorova: Feel free to follow me on LinkedIn, it's Aleksandra Sasha Prokhorovaa. Sasha in the parenthesis, because Sasha is short for Aleksandra in Russian. Yeah the last name is Prokhorovaa.
Kirill Eremenko: Wonderful, okay. So, well also include the URL to your LinkedIn in the show notes for all listeners to catch up on there. On that notes, once again, thanks so much for coming on this show, best of luck with your career. Let's stay in touch and I am looking forward to reading your article for Wednesday next week.
Sasha Prokhorova: Thank you very much Kirill.
Kirill Eremenko: Alright, take care.
Kirill Eremenko: So there we go, that was our chat with Sasha. I hope you enjoyed it. You can get the show notes and check if Sasha has completed her challenge successfully at www.superdatascience.com/203. There you'll get all the show notes and everything from this podcast, all of the things that we've mentioned.
Kirill Eremenko: Another thing I wanted to outline today, before we finish off, is that during this session we talked about DataScienceGO 2018 quite a lot. As you could see, and feel, and hear, Sasha had an amazing time. There was plenty of speakers there, and lots of things to learn. So if you missed out on DataScienceGO 2018, or if you attended and missed out on certain sessions, because we did have two rooms in parallel, so if you missed out on certain sessions, then I have some great news for you. You can get the recordings from DataScienceGO 2018 and keep them for life, today. You can go to DataScienceGO,
www.datasciencego.com/recordings, and you will find all of the sessions there. You'll be able to purchase the whole package and keep it for life, and revisit any talks that you loved if you were there, any talks that you missed, if you weren't there, and get all the value of it. We recorded every session with professional camera crew, so the quality is outstanding and you get the full package both Saturday and Sunday included.
Kirill Eremenko: So make sure to check out datasciencego.com/recordings, if you want to relive this experience or get all of the value from our speakers that our attendees got. Of course you won't be able to get the networking that is something you get only by being there, but at least you can get all of the value that our speakers were there to share in terms of their talk, in terms of the things that they prepared for this conference.
Kirill Eremenko: So, highly recommend checking it out, it's datasciencego.com/recordings, you can find it there, and on that note, thank you so much for being here, for being part of the SuperDataScience podcast today, spending this hour with us, with me and Sasha, and I look forward to seeing you back here next time. And until then, happy analyzing!
PART 3 OF 3 ENDS [00:59:17]