SDS PODCAST EPISODE 189 WITH RANDY LAO

Transcription

1 SDS PODCAST EPISODE 189 WITH RANDY LAO

2 This episode 189 with data science mentor, Randy Lao. 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 let's make the complex simple. Welcome back to the Super Data Science Podcast, ladies and gentlemen. Super excited to have you on board. I've got such an exciting episode coming up you won't believe it. So on this podcast, I have the legendary Randy Lao joining us for the call. You're probably already familiar with Randy. If you're not, I'll get you up to speed very quickly. Randy is a data science legend because he is a person who went from zero on LinkedIn, from just staring out to 45,000 followers and several data science jobs in just a year. How crazy is that? That is a breathtaking ride and that is something that we talked about in this podcast. Randy disclosed all his secrets on how he built such an amazing portfolio, such an amazing reputation online and how he's helped thousands and thousands of people. Specifically, in this podcast, we covered the following topics: we talked about the whole process of getting a job in data science. So starting off with preparing your LinkedIn, your resume, your portfolio. Helping people through sharing how you're learning data science and then moving on to actually getting those job offers. We

3 talked about three different ways to get a job in data science. How to get job opportunities through networking. Tips for your LinkedIn. How the data science interview goes, the three steps involved in that, and tips for your interview process. On top of that, we also discussed other topics that Randy is very passionate about such as the end goal of data science, what's the whole purpose of data science. The what, how, and why of data science. Speaking in data science events and meetups and many, many more. So all in all, this podcast is full of exciting topics and a lot of energy. When you listen to Randy, you'll hear how passionate he is about data science and that energy that comes with that passion. I wish for everybody to emulate Randy in one way or another in their own careers and really help lots and lots of other fellow data scientists and this podcast is a great way to get started on that journey. Without further ado, I bring to you Randy Lao, a data science mentor. Welcome to the Data Science Podcast, ladies and gentlemen. Today, I've got a very special and exciting guest on the show, Randy Lao. Randy, how are you going today? Welcome to the show. I'm doing great, Kirill. Thanks so much and again, thanks to everybody who's watching this podcast. Really appreciate it. Thank you. It's so great to get you on the show. I've seen a few things that you're caring and giving back to the

4 community and just super excited. You are a very inspirational guy so I'm looking forward to what we can chat about today. Thanks. Awesome. Let's start off with where are you right now. We chatted about this just before the show, but for our listeners, where are you located today? I'm at Los Angeles right now, California. Is that your home base? Yeah. Well, I actually live in a city Cerritos. Cerritos. Which is not too far from LA, but LA is my hometown. Okay. Got you. It was interesting for me to realize that because I'm in San Diego right now and so we're just talking to each other. Yeah. We're like right next to each other. We're like was it 200 kilometers away from each other? It doesn't happen often that way. Yeah. Yeah. Anyway, Randy, tell us a bit about yourself. If somebody off the street were to ask you, "Randy, what do you do for a job," what would you reply? I would say I'm more of a data science educator, if that makes sense. So I have two jobs. I work as a data analytics teaching assistant at USC and my other job is a machine learning assistant at Data Application

5 Lab. In total, I have a great background in the education space. More of a data science educator, if that makes sense. Yeah, okay. Got you. So working two jobs. One in data science teaching, one in machine learning teaching. How does that affect your time? I'm assuming you'd be super busy with work. Yes, but again, I think it's really rewarding for myself. I have to push the limits. I don't want to be in a state where I have a lot of free time. I don't like that. I like being very busy, being productive, and I also get most of my energy with talking to people. So that's something I found out there in a job. When I'm with people, I get more energy. Okay. So like extroverted personality you'd say. No. I'm actually introverted. Oh, interesting. Very interesting. How do you get energy then by speaking with people? I got out of my comfort zone and this goes back to I think two years ago. After college, I wasn't the type of person that was very active. I didn't join any clubs. I didn't really reach out to people until when I took a data science bootcamp called Springboard. I've heard of them. Yeah. It's a really nice bootcamp and I just graduated from there two months ago. One of the criteria was attend a meetup and write something about it. That point, I never attended a meetup. I was nervous. I'm

6 not the guy that likes to talk and from that point on, I realized how cool the community was with data science and machine learning. I didn't know there was people with the same mindset, the same interest all together collaborating in one room. That exposure just opened up my eyes a lot. Another task was create a LinkedIn profile and this opened up so many doors, so many opportunities that I never imagined. This is back to the audience. If I were to give one advice to kickstart your career in any field you want, whether that's data science, machine learning, healthcare, education, my best advice is learn how to network, communicate, and build relationships with people because it's those random opportunities that comes down your way that can change your life and you just need one of that. Wow. That's great advice. How long ago was that meetup that you attended or when you created your LinkedIn profile? Everything happened very sudden. That meetup happened last year and my LinkedIn happened last year as well. I'm just looking at your LinkedIn profile for our listeners, last year you created your LinkedIn profile, now you have 45,000 followers. Yes, that's correct. That's correct. That's probably the most or second most followers I've ever seen on LinkedIn.

7 Oh, really? Yeah. How did that happen? Tell us. You obviously know some magic tricks for networking. Yeah. I did not expect that to happen but again, I'm a very active member on the LinkedIn community, especially with the data science and machine learning community. From that year on, I learned so much in regards to not only networking, I learned a lot about building relationships and providing value to the community. So my saying goes you have to give back to get back. You have to give more to get back more. When I first started LinkedIn, I realized that everyone has a different learning pathway and for me, my best way of learning is to reiterate and write an article or write a post. That's my way of learning and I used LinkedIn as a platform to ask a lot of questions and also post relevant things and relevant concepts of what I'm learning, whether that's data science or machine learning. Along the way, I posted a lot of free resources that I found online and people loved it. I provided consistent value to the community of what I'm learning so as I'm posting these things, I'm telling everybody what I'm learning throughout my data journey. That's what I'm learning. Again, it's been a year. I met a lot of cool people and along the way, there's more publicity. I met some influencers on the LinkedIn community, got a chance to meet up with them, got a chance to talk with them, got a chance to meet you. That's just that to the networking aspect of building your career.

8 Funny enough, I came to you myself. I came to you first, right? I was like, "Whoa, who's this Randy guy? He's doing so well." Yeah, that was a shock. I was really surprised. Well, man, I'm really surprised that you only started a year ago. It feels like you've given so much value that you've been doing this for two or three or four years. Yeah, I get that a lot. I'm surprised myself, but it's been a blast. I'm having fun posting a lot of stuff every day. Yeah, that's [inaudible 00:10:29]. So tell us, how long does it take to write one of these articles? I'm looking at A Beginner's Guide to Machine Learning that you published recently. For instance, how long did that article take you? Okay, for me, it takes at least three weeks. Three weeks? Wow. So one week to actually get the structure laid out. The second week would be to research. I would research each topic, get it nailed down in my head, and I'll spend the next third week, I will do some rough drafts and then I will finalize it within maybe the last two days. I was expecting you to say, "Oh, it doesn't take long like two days." [crosstalk 00:11:17]. I don't have much time so I have to split it up for three weeks.

9 Yeah. But I guess that's what it takes, right? You want to give value to the community, you need to invest the time. I don't really write articles that much because for me as well it takes a very long time. The recent article I wrote [inaudible 00:11:37] and that took me about three weeks or so as well. Even like recording a video, for instance, which as well... it looks like it's fast. It looks like oh, it's a five-minute video. Actually, the amount of research that goes into that is immense. Yes, I totally agree. Totally agree. But it's worth it in the end, right? As you say, you're having fun. You're meeting people. You're growing your career and you're seeing others grow around you as well. Yeah, and I would like to add on what you just said right now. Again, to the viewers and to the audience out there, another great thing that you can do not only for yourself but for the community as well is again to give back. Whether you're giving back free resources, you're giving back your relevant thoughts, any videos or podcasts, all of these things are unique to you. You have a story to tell that's only from yourself, from your mind and that story you don't know well it's going to impact someone else. That's what I always say. Yeah, exactly. You can't calculate in advance what effect you're going to have on others. Yeah. That's good. The least you can do is share, I guess. I like your approach. You learn, it's not like you're an

10 expert in something and you're just teaching that. You're learning as you're growing yourself. I love that. Personally, I find that even better to share it that way than if you're an expert like... no offense to the experts out there, but I just think that when you're learning yourself and you're sharing right away, you've just gone through that journey of acquiring this knowledge. You know what the difficulties are. You know what the pitfalls are that others are going to have and you can help them avoid those pitfalls because you've literally just done that journey yourself like two days ago. Yeah, no. Exactly. I just made a post today too about the value of teaching because I think teaching is... again, everybody has their own way of learning, whether that's reading a book, watching a video, audios or actually writing. For me, I retain my information from teaching. There was a quote saying, "When one teaches, two learn." I love that quote and that stuck with me because I believe that when you're teaching, whoever your audience is, there could be one, ten, a hundred, a thousand, as long as you're making that impact and you're teaching somebody to learn, not only are you reiterating that concept for yourself, meaning you're relearning that material, you're also helping other at the same time. Yeah. Totally got you and totally agree with you on that point. Well, let's rewind a little bit because you had a massive job in terms of your networking and career in the past year and I think it's a great testament for those listening out there that it is

11 possible because sometimes, people look at successful people in LinkedIn or other platforms and think that it takes years and it's virtually impossible to get it, but it is possible. Now, let's rewind back a little bit and tell us a bit how you got into this field in the first place. Why machine learning? Why data science? Okay. Yeah, so this is from last year and I graduated with a computer science degree and a bachelor's and to be honest, I didn't have any mentality or any future goals in work with data at all until I started hearing all these buzz words. You know, a typical machine learning, big data AI, data science. I started reading all of it and it caught my interest like yeah, this is actually the feature. There was a quote from Mark Cuban that I read recently, he said, "If you aren't familiar with the term key learning in the next three or a few years, you're going to be a dinosaur." That caught my attention and it's just the fear of missing out of open opportunity [inaudible 00:15:48] caught my interest. I got introduced to data science from Springboard and from that point on, I just got obsessed with it and kept on learning and growing and I'm still learning along the way 'til this day. Yeah. Well, that's really cool. I know another quote from Mark Cuban that caught my attention. I didn't hear about that one, but the one I read before is the world's first trillionaire will be an AI entrepreneur. I think I heard about that, yeah. Yeah. How cool is that?

12 That's crazy. Yeah. Maybe it'll be you, Randy. We're going to create the next AI. Who knows? Yeah, got you. Okay, so you weren't planning on getting into data. You heard a quote. You took the Springboard course and you got hooked and you decided to keep pursuing this area. Then what happened? Because right now you're involved in two teaching positions and one actually where you're doing data science work, if I'm not mistaken. How did you get into those? For people, it might seem that it's quite hard to find jobs in data science and machine learning. Yeah, I got it. Again, this is for all these new people out there. Last year, I didn't have a connection. I started off with zero LinkedIn followers. I'm going to give you my biggest tips in regards to getting a job and getting yourself out there starting from scratch. For me, what people like is the consistency of value. Whenever you're giving back any sort of value, whether that's resources, advice, tips, articles, videos, you're providing some sort of knowledge from yourself to somebody else and if that value is of quality, you're going to build a following, you're going to build relationships that love your work. That's what I did with LinkedIn. I used that platform not to just connect with people but to share my journey with them and this is what I really want to emphasize to everybody out there. You have to learn

13 how to network because in the end, you're going to be talking with people. This is a people world. You're going to be here by yourself and the best way to do that is to connect, build relationships, and get yourself out there. You have so many social media platforms. You have Facebook, Facebook groups, you have Slack Chat, you have LinkedIn, all of these people and groups that have the same like-minded interest as yourself whether that's data science, machine learning, AI, use that. Use that platform to connect with them, learn from them and also teach them. So that's what I did for the past year. I contributed a lot of my resources. I talked to a lot of people and by doing so, you're going to get these random opportunities coming to you. People are going to recognize you that hey, this guy is really interested in data science. Let's talk to him. Once you talk to new people, you get new opportunities. These past two jobs I'm in right now are all based off of networking. I didn't apply to any of these jobs. These were jobs that came to me through open opportunities from networking. Wow. Fantastic. That's great advice and I'm really hopeful that people will take it to heart and use your case as a live example that everything is possible. Apart from sharing on LinkedIn, which is very important, I guess creating your profile and structuring your profile and the information you put out there about yourself is also quite important. Any tips on that?

14 Yeah. Right now, if you're in a job search, there's three ways to get a job. One is to turn your resume the online traditional, old style turn in your resume. Two is from a third-party connection. It might be like you have a friend that works in a company and he hits you up. That's the second way. Third way is the internet. Your online presence. Your virtual self. This is where your online resume plays. This is where your LinkedIn profile is into effect. This is where your Facebook profile is into effect. Your internet presence. Virtual presence. If you can utilize these three platforms and know how to optimize it, you're going to have a higher chance at landing a job. In the case of optimizing your LinkedIn platform, right now it's very hard. There's a lot of recruiters actively looking on LinkedIn to recruit people so my best advice, one is get a good photo. It could be a good headshot but nothing too flashy because again, if you think about it, a good headshot or a good photo represents yourself and it's all about first impressions. So when a recruiter comes up to your profile and they don't see something that they like, that's something on their self because again, it's first impression, but other than that, you're going to have to create your job description, your personal profile to sell yourself. So don't write content about okay, this is what I do. This is who I am. You should tailor it in regards to how you can contribute your core values and assist the company. This is vague, but my best advice is your online presence is a way for you to sell yourself and let

15 other companies know that you have the potential skills that they need. Yup, okay. Makes total sense. I like that approach of tailor your LinkedIn to... not just talk about this is who I am and what I do but actually, think of the job that you want and think of how you can add value to that job and describe yourself from that perspective. Yeah exactly. Imagine if you are already in that job, how would you be adding value? What's your dream situation? If you approach it from that perspective, then people who are looking to fill your dream job, when they read your profile they'll see that it matches. You're making it easier for them. They don't have to look at what you currently do and then derive from that how you'll be helpful in the role that you want. You've already done that step for them. Yeah, exactly. Got you. And finally, if somebody does get conducted on LinkedIn, so they have a profile, they're adding value to the community, and they finally get contacted by a recruiter and they're interested in the position, do you have any tips for the actual interview process? Yeah. Normally, the interview process might take up to three steps. Your first one is normally an interview with HR. So this is very typical behavioral style. Your goal here is to win them. Know who that HR person is, get on her good side, and do your research, do your homework on the company and job you're applying for.

16 Again, the HR is here to just get a quick overview on who you are. When you have that interview, you got to prepare yourself not only mentally but emotionally. You have to show your passion. If someone's talking on a phone and you're very monotone like, "Oh, hi. Thank you for having," in contrast to, "Oh, hey. Thank you so much for having me," that is a difference from night and day and that makes the HR person know, "Wow, this guy is really excited for the position and I really want to put him to the next step." In regards to that, show passion, do your homework on the company, look up some problems or any current or past projects that they work on so you can talk about that during that interview. Another tip would be to smile during the talk. Just be happy, be in the moment and if you showcase all of this, you're going to be more than qualified to your next round. I'll add to that. Even if it's a phone interview and you smile, people will feel your smile. Yeah. You know? Definitely. I'm smiling right now. Yeah, exactly. I just also wanted to add, this reminded me of a story I have thought of in ages. When I was applying for Deloitte for my job there in consulting, actually, I applied for accounting and when they called me, this exact situation, they called me from the HR department and they're like this first screening round. The thing is they called me and they were like, "Hey,

17 Kirill, you went through tot screening rounds, wanted to catch up and find out a bit about you." I was like, "Oh, that's so cool." And then they're like, "So your application is going to the next round in the forensics division." I was like, at that point in time, I only heard the word forensic from CSI shows, so I was like, "What?" Because of my surprise in my voice and genuine, that took me so much because shock that I didn't even have to be nervous. I was like, "All right, I got to Google this forensic thing." I think she could totally hear my excitement just because of that, so yeah. That's a cool story. That's a cool story. If anybody's ever nervous, just remember that story, how Kirill didn't know what forensics is. Yeah, I'm going to keep that in my note. That's a great story. So assuming we've passed that first round and we have. We are data scientist. We're adding value in LinkedIn. Everything's great. We passed the screening. What comes next? Again, the first round is going to be behavioral. That's just to give you a high-level overview on what type of person you are and if you can be a good fit for the company. Once you get past that, depending on how big the company is, you might get moved on to maybe two or three more process, but normally, the next step is a process of something called the technical interview. This is where, depending on the role you're

18 applying for, I'm assuming data science or machine learning, these technical interviews can consist a lot of a lot of questions about either algorithms, programming, and for sure SQL programming questions and also business used case questions. So if I were to give you a structured layout on what to expect on a technical interview, I would say focus on learning SQL, learning how to query, maybe do some complex queries. A good resource for that would be SQLZOO if you look it up on Google. Practice some statistics and probably questions. Practice learning how to code, whether that's Python or R. They typically ask you coding questions normally around the easy to medium level on a website called LeetCode, so leetcode.com. Another final step could be questions in regards to business used case questions. One example that I got is the recruited asked, " let's say you're a data scientist from Tesla and your boss comes up to you and says, "Hey, Randy, we have a job for you. We just deployed our new model Z and right now, our biggest concern is understanding user sentiment. Given a month, can you give me a report on how you can go about doing so?" That's it. It's very open-ended and what these questions do, they test your knowledge on how you can approach a particular problem, how you can ask question to get more information and utilize everything you learn from the data science pipeline to complete a goal. That's my tip on that.

19 Wow. That's a very good overview of all the questions that possibly can come up. It's pretty accurate. It's interesting that you mentioned SQL because indeed, people tend to focus on Python or R and that's fair enough. Ut one of the main things that a lot of these companies will question on is SQL and I'm assuming that's because where data science... I won't say data science started in that area because you need to be able to access the data in order to query it and a lot of enterprises actually store their data in a form of a scale, whether it's Oracle or it's SQL server or it's [inaudible 00:29:36] SQL and so on. Definitely they are a great stool to have in your arsenal to start with. Yeah, that's correct. I was actually listening to a podcast, O'Reilly podcast, and this one was about... you might be interested in this one. This episode was called Teaching and Implementing Data Science and the AI in the Enterprise. Oh, I haven't seen that. I'll send them to you. It's really cool. I forgot the surname, but it's a gentleman called Jerry and he is an AI/data science consultant in one of these big companies. What I really liked about this podcast is probably for the first time I heard somebody aptly describe what a data scientist is. I totally agree and I said this myself, that a data scientist is not just somebody who is able to run a machine learning algorithm or sis able to build a model. Data science is much more than that. It's also a person who can

20 explain the insides, who can use things like SQL, and [inaudible 00:30:41] tools, and so on. But in this podcast, what really liked was they said or this gentleman Jerry, he said that a data scientist is a person who can do research and this is in line with what you're saying that a data scientist is actually a scientist and being able to put together that experiment to understand okay, so what are the constraints of the problem? What are the circumstances? Okay, now we've been giving these inputs or this thing to explore, investigate. How are we going to construct this experiment when you're actually going to go and collect data or you're going to use data that's already collected in order to derive those insights. So I'm really glad you pointed that out because that's the whole data science mindset. It's not just about running an algorithm and being able to code. It's about being in that mindset of researching whether something is significant or something has an effect or something doesn't have an effect. Yeah. I love what you just said and if I can add more to that, I would love to. Yeah, sure, of course. Again, back to your statement on like the data science and... what did you say, something about algorithms, can you repeat that? Data science is not just a person who can put an algorithm together or build a model or crunch numbers.

21 Yeah, exactly. So would like to add onto that. I would say for anybody in the field of data science, your main goal, your main mission as a data scientist is to be a problem solver. That's the end goal. It doesn't matter how you solved that problem, as long as you're using the tools and data to get something done, you're doing your job. Whether that's data visualization, data modeling, data analyzing, data storytelling, reporting, anything you're doing with data, that's some aspect with data science. I would just say data science is a field where you're just constantly solving business problems using data. Yeah. Totally agree with you on that one. Data scientist is a problem solver because what's the point of doing data science if your insights are not going to solve any problems, or adding value, or nobody's going to use them to make any decisions. It's just a hobby. You're just putting numbers together at the end of the day. Exactly. And then, I also like to tell this to the audience. If you're very new to the field, I know a lot of these free courses, these free online tutorials, they're giving you all these tools but what I want you to do instead is to open up your mind and always think about how you can apply this to some sort of business problem because no matter how much data you have, that's only going to explain the what. No matter how much tools and technology you're using, that's only going to explain the how, like how to solve it. You're going to have to go beyond that and focus on the why,

22 which all goes back to the problem you're trying to solve. Got you. I actually see the LinkedIn that one of your top interests is Simon Sinek and that really ties in with his what, how, why. Yes, exactly. That's a great book. Yeah. Well, I haven't read the book. I've seen the TED talk that he's done and I really enjoyed it. Oh yeah, love him. He's very smart. Yeah. For our listeners, it's called Start with Why Simon Sinek. It's really cool. Okay, well on that note, I think we've gone through the whole journey of getting a job in data science starting from zero, with zero followers, with zero experience, somewhere where you were a year ago, and moving all the way to 45,000 followers. Now I realize, Paolo, our event manager, he actually told me, "When you talk to Randy you'll see that he's like one of our success stories but on steroid because... " Wow. Was that Paolo? Yeah, Paolo. Yeah. Tell him, Paolo, I said thank you so much. I really appreciate. Speaking of Paolo, I have an exciting announcement to make for our listeners because just before this podcast, I had a chat with Randy and Randy agreed to come as a speaker to Data Science Go Randy,

23 I'm so excited about this. This is going to be epic. Thank you so much for agreeing to that. No, thank you. When I heard that invite, I got really excited. I've been a fan of your first Super Data Science Go last year, but I unfortunately couldn't attend, so I have to return that thank you to and your team so thank you so much. For audience out there, I think it's going to be a great event. I really recommend you to check it out, Super Data Science Go, and if you do get a chance to see us, please come over and give us your hello. Yeah. Well, that's definitely going to be epic and looking forward to catching up with you, Randy, in person [inaudible 00:35:43] and of course, everybody who's going to be attending. But yeah, so that's going to be coming up some time soon. Tell us a bit more in general your speaking events because you mentioned you were speaking in an event just recently and this is going from a year or just over a year ago when you weren't comfortable speaking. I remember that you said you went to this meetup and you weren't comfortable meeting with people and now, you're traveling around, speaking at events. You seem totally comfortable with words. People are loving what you're sharing. How do you make that transition from being somebody who's not comfortable doing that to thriving on speaking at events and presenting to others about data science? I'm still questioning myself on that. But to keep it short, it's with any skill, so talking in general is a skill

24 and with all skills, it requires practice. There's no shortcut. It's just a lot of practice and as you keep on practicing, you're going to build the confidence and you're going to see what works and what doesn't. For me, I still get the jitter bugs in the beginning, I get a little nervous, but not to an extreme point as when I first started. So my recommendation is try talk on the phone with yourself or do a recording, see how you talk. See what needs to be worked on. Get constant feedback from your friends, your family members because that allows you to tailor your speak and allows you to be more confident and speak in a way where you're not too nervous, which just goes all [inaudible 00:37:29] practice. It takes a lot of practice, and a lot of confidence, and a lot of smiling. That's my three takeaways on that. Yeah, got you. What would you say is the reason why? Let's go back to the why. Why would somebody get into speaking? What's the benefit for a data scientist to actually not just attend an event or a meetup but actually present at one? Yeah, that's great. So the why is the most important thing you can ask yourself. Why am I doing this? Why am I giving you this talk? Why is this so important? Why do people need to know about this? The why sets you up... it sets up your whole presentation and in life, you're going to have to deal with people no matter where you're going and whenever you're dealing with people, you're going to have to talk. So by practicing storytelling, by practicing public speaking, this is one

25 of the most valuable assets that you can have in your entire life. I know Warren Buffett he was an introvert as well and I'd watched the video film, he said if he had one skill that helped him out throughout his whole life, his whole career, it was public speaking and the book that he recommended was How to Win Friends and Influence People by Dale Carnegie and that talks about how you as a person should learn how to talk and communicate with people because that's how human nature is. We're a people that talks for a living and to transfer our knowledge, our information to another person, that's all through talking. Especially with data science, when you're working with all these algorithms, you're working with all this data, all of these complex terminologies, you're going to have to dumb it down to your boss or your employee so they can have a good understanding because in the end, if you're giving a talk and you're not tailoring it to your audience and they don't know what you're saying, it's pointless. It's like you're giving a talk to a dog, they don't understand. So that's the whole point and that's the power of learning how to public speak. Love it. You know how interesting it is when all the dots connect or you have these things that line up in life? Because literally today, in the car, we're talking with Paolo, the even manager for Data Science Go, about books that we're reading and he's reading exactly that book, How to Win Friends and Influence People.

26 Really? Yeah. Wow. Yeah, I love Paolo already. Yeah, it's so funny. I know you mentioned on the podcast, I'm like, "Wow." It's rare that two different people on the same day mentioning the same book. Oh, it was today? Yeah, today. Literally like two hours ago. Oh, wow. Okay. It's a good book. It's a great book, actually. For listeners out there, if you haven't read it yet. I think it recommended it once before in the podcast. It helps you understand your priorities from the other person's perspective and how you can add value to that person as a main thing rather than just me, me, me all the time and people will be much more open to you in that way. Yeah, I agree. That's one of my favorite books so I highly recommend it. Yeah, okay. We're over halfway through the podcast. How about we do some quick rapid fire questions about your experience in data science. You ready for this? Yeah, let's do it. All right. So question number one, what tools do you use on a daily basis as a data scientist?

27 I use Excel, Python, and MySQL. Why MySQL? Why not other types of SQL? For some reason, our company, we're using MySQL at the moment and also, for our bootcamp at USC, we're teaching that as well it's just a compound of MySQL at the moment. Just for some reason. Got you. What techniques do you use most commonly? Techniques. Define techniques. Is it like...? Like algorithms, methods. Oh, got it. My favorite one is Random Forest and Logistic Regression. One project I did that was pretty fun, I used Logistic Regression to predict employee turnover. So that was a data science [inaudible 00:42:12], loved it. I would say for the people out there, stick with the simple algorithms and work your way up because those are the foundations. You don't need to learn deep learning real quick of the hype. Learn Linear, Logistic Regression, and move your way up. Mm-hmm (affirmative). Makes sense. Totally. What's the biggest challenge you've ever had as a data scientist? My biggest challenge is talking to your clients. Talking to the stakeholders, talking to our boss, making sure the problem that you're trying to solve is actually the problem that you're to solve. This is back to again communicating because as a data scientist, the worst thing you can do is work on a problem that you're

28 trying to solve but you're solving the wrong problem. To fix this, you have to over communicate. You got to constantly communicate, make sure you understand from heads down what you're trying to solve and what that person wants so you can actually solve it in the first place. I'll probably add to that that for a long time, I always considered that data science has four steps - data preparation, data analysis, visualization, and presentation. But then about over a year ago, probably two years ago, I was refining that for myself, I realized that a crucial initial step is definition of the project scope and the problem because, as you say, if you screw that part up, you might spend the rest of the project solving the problem. Yes. There are actually some people that do it or I heard stories about it. It's just a time waster and just don't do it. Yeah, for sure. What is a recent with us that you can share with us that you had in your role, something that you're proud of? What I'm really proud of is my LinkedIn connections, so shout out to everybody who's been following me. Because all my audience has made who I am, it made me realize the power of networking, the power of providing value. I get a lot of good positive messages that makes my day and I wake up every morning feeling very appreciative. So shout out to everybody. That's what I'm really proud of, having my connections.

29 Woo hoo! I can actually feel 45,000 screaming right now, "Randy! Yay!" Awesome. That's really cool. In a book, I think, I was reading about the power of gratitude. I've read it about a couple of times now, but it's coming up that it's important to be grateful because it helps you see what else you can do in life. See the good side of life. See the glass half full rather than half empty. Totally appreciate that. What about you? You say when you wake up, it makes you feel appreciative. Is there anything you do every morning when you wake up? Any kind of morning routine that you have apart from feeling grateful for all the things that you [crosstalk 00:45:37]? My morning routine is very boring. I'm more of a night guy. I know a lot of people they wake up early in the morning and they work out, they'd be very productive. I totally agree on that, but for me, it's just my schedule on my jobs. I just wake up at 6 am, I make two cups of coffee, I'm a coffee addict, and then I drive to work and I'd work 'til maybe 9 pm at night and then another hour to drive back home. I use that time to do a lot of workouts and I also plan my schedule for the next day because whenever I wake up, I don't want to waste that mental energy thinking to myself, "What am I doing today?" I always pre-plan that the day prior. Okay. That makes sense. But how do you find time to work on articles and give value back if you're working from 6 am to 9 pm? That's something with the obsession or the habit. Again, back to what I do on LinkedIn, I write a lot of

30 articles, I write a lot of post, and I write a lot of motivational quotes. Again, this is back to building habits and this is what I recommend to everybody listening. Initially, you are what you do. 40% of your life you're in zombie mode, AKA, you're in habit mode. Whether you wake up brushing your teeth, whether you wake up to go to your day-to-day job, eating at a certain time, these are all based off of your normal clock, your habit. If you can build that habit of positive habits, of doing something that's very productive and that can build your career, I would highly emphasize you to work on that. My habit is writing posts, reading creative articles, reading early in the morning, getting inspiration and that allows me to be very productive throughout the day and just gives me more insights on... keeps me updated with the news, stuff like that. Love it. A good book on that is The Power of Habit by Charles Duhigg. Oh, yeah. I haven't read that. I started. I read like a fifth and speaking of habits, I should get into the habit of finishing books. Yeah, that's a good one. But it is a good book. It talks about when you don't have a habit and you force yourself to do something, it's like you need to use your willpower for it, but you do it for a certain period of time, like 60 days or so. It becomes a habit and it doesn't drain your willpower anymore. Exactly, yes. I totally agree on that.

31 But that's a really cool layout of your day. It seems like you're keeping yourself very busy. Do you have even time for hobbies, and sports, and going out with friends? Yeah. That's normally for the weekends and also, I don't work every Monday to Friday. I do have Wednesday off a little bit. It's very flexible. On my Wednesdays, and Saturdays, Sundays, that's normally for family or friends or any spontaneous activities that come my way. Funny, I can totally relate to that. When I was in high school, I made sure I would have Wednesday off and when I was at my recent job or my latest job in [inaudible 00:49:07], at some point, I also negotiated to have Wednesdays off just for family or for my other projects and so on. Wednesday seems like a good day. It's the middle of the week, which I like. Yeah, totally agree. Okay, back to our questions. What is your one most favorite thing about being a data scientist? This is what I said earlier. My number favorite thing is the people. I'm a guy and I just realized that I get a lot of my energy talking to people and as a data scientist, you're there to solve problems, you're there to talk to people. One aspect of that is communicating, meeting people, building relationships, all this good stuff and that's something that I really like. That's an unusual answer. I don't usually get that on the show. Mostly, it's like people talk about how

32 they're solving the problem or they're planning creativity, which I think they're all fair answers, but this is a great one that you... if you see data science from that perspective, not just that you're sitting in your office or at home by yourself and just you against the problem, if you see it as a communal thing, as what can I learn from these people today, what can I share with those people tomorrow, it becomes a very different type of playing field. I'll say much more exciting because when you do have those downtimes or when you have those periods when you're faced with challenges that are too difficult [inaudible 00:50:49] at that moment in time, you have other things to do. You have people to chat with, knowledge to share, and things to learn. Yeah, definitely. Also too, again, that's your work. It's never going to be about you. It's always about what you're doing for the community or your job or the people around you. That's what makes this field so rewarding and exciting. You're solving problems to help other people and that's what I like. Yup. My most favorite rapid fire question that I love hearing answers to, from what you learned, from what you've seen in the field of data science and what you're seeing happening now, where do you think this field of data science is going and where should our listeners look to in order to prepare for the future that's coming ahead? I know for a fact that it's booming and if you're onto this field, you're in the right path. It's not going to go

33 anywhere because if you think of it, I know you already read all the buzz words, but everything you're doing right now you're getting monitored by companies and they're getting data from you like through this talk right now, some data is being gathered from myself. If we're using Netflix, Netflix, it's getting data from there. Google, people are getting data from Google. Next thing you know, we're going to have IoT, internet of things. Refrigerators can get data by yourself, vice versa, cars. Everything is going to be visualized. Everything is going to be virtualized and whenever something is virtualized or digitalized, that means more data. In that sense, more data means more problems to be solved and more problems mean more opportunities. I think it's a great field to be in and if you're really interested, start learning it. Got you. Exactly. Thanks a lot for sharing. Couldn't agree more. Everybody should be jumping into data science and as Mark Cuban said, the quote that you shared, "You have three years before you'll become a dinosaur." Well, Randy, thank you so much for coming on the show. Before we finish off, share with us please where is the best place or places for our listeners to get in touch with you, contact you or follow you in our career. Got it. The most obvious and the best one I can say is LinkedIn. Just type in Randy Lao. It should be an Asian guy, I think, the first one. Randy Lao in LinkedIn and also, if you can visit my website called cloudml.co. Cloud as in C-L-A-O-U-D.co. That website is where I host all of my free resources that I've

34 learned throughout the year and I constantly update it with our webinars, new resources that I provide and some articles. So you can use that. I get a lot of good feedback from that and a lot of people have learned from that site. Yeah, got you. Actually, before the podcast, you mentioned that you run webinars. Tell us a bit more about that before we finish up. Oh, yeah. For the company that I've worked for, it's a nonprofit called IDEAS, we do bi-monthly webinars. So twice a week, it'll be me as host and another data scientist and we just talk about anything data related. I've talked to a lot of the LinkedIn influencers before and they've all been invited to your talk. If you're familiar with Fabio Vasquez, Eric Weber, Sarah Nooravi, Nick Ryan, Tape, and some various others, we use that time to call out to the data community for one hour of your day on a Saturday, you'll spend some time data science. That's the main goal and that's what we do twice a month. Got you. Fantastic. We'll definitely recommend for everybody to check that out. Okay, so we've got the LinkedIn where people can follow you, your website. One final question before I let you go today. What is a book that you can recommend to our listeners to help them enhance their career? Yeah, I just said that a while ago, it's the book called How to Win Friends and Influence people by Dale Carnegie. This book is the book about life and this book actually helped me in my career, in my personal

35 life as well because it provides so much information about how you see yourself as an individual but not only that, it gives you the sense of perspective that life is not just about you, it's about the people around you and knowing how they think, how they react, how to cope with different situations allows you to better prepare yourself and handle these situations. I love that book. Especially if you're introverted, this opens your eyes and this book actually brought my mind into being more open with the public, so highly, highly recommended. Wonderful. Well, thank you again, Randy, for coming on the show. I love our chat today and I can't wait to meet you in person in October at Data Science Go. Definitely. Thank you, Kirill, and thank you so much for inviting me. Really appreciate it. So there you have it, that was our data science mentor, Randy Lao, making data go viral as his LinkedIn shows and of course, so many interesting and valuable points shared today. How Randy went from zero to 45,000 followers in a year is just crazy and if you look at his LinkedIn profile now, it's even higher than that. He's gone over 50,000 just recently, so he's continuing on his journey. Of course, I loved a lot of the things that Randy shared. My personal favorite was the quote that he shared, which was the value of teaching, when one teaches, two learn. I personally go by that philosophy as well that when I teach something about data science, when I share my knowledge, I reinforce it for myself. I

36 relearn it, I learn it better. This is something that I can definitely recommend to everybody listening. If you want to become even better at data science and grasp certain topics to an even deeper level, then I highly recommend teaching people and one great way to doing that is by sharing your knowledge through LinkedIn, for example, or other social media. Through Twitter, through videos, through blog posts, through podcasts and so on. That way, you'll reinforce your own knowledge but not just that, you'll actually help other people get into the space of data science and see what experience you're going through. So on that note, highly recommend checking out Randy's blog post. Great examples of how even simple concepts and simple challenges can be turned into amazing, elaborate blog posts which can help people walk in through this process of learning data science. Also, make sure to connect with Randy on LinkedIn. You can find his LinkedIn URL at the show notes which are on or you can just search LinkedIn for Randy Lao. Make sure to connect with Randy and follow his updates, and blog posts, and other things that he'll be sharing. Of course, as we mentioned during the podcast, Randy will be joining us as one of our speakers for Data Science Go 2018 which is happening this October in San Diego and we can't wait to see you there. The tickets are on sale now. This is the last week where you can get them at the special price, so make sure to secure your seat today and come to Data Science Go in a month from now and meet Randy and lots of other

37 data science influencers and mentors in person. The website to do this at is That's datasciencego.com. I can't wait to see you there personally and until next time. Happy analyzing.