khuyen-tran-2020-04-29.mp3

Khuyen Tran: [00:00:00] Whatever ambitious goal you have, give it. Have the goal in front of you, have the goal in your mind, have the  specific task and approach that how you want to tackle it. I will say if you'll have that mindset in your mind, a lot of things you want to achieve will be be done.

Harpreet Sahota: [00:00:32] What's up, everyone? Welcome to another episode of the Artists Have Data Science. Be sure to follow the show on Instagram @theArtistsofDataScience and on Twitter @ArtistsOfData. I'll be sharing awesome tips and wisdom on Data science, as well as clips from the show. Join the Free Open Mastermind Slack channel by going to bitly.com/artistsofdatascience. Where I'll keep you updated on biweekly open office hours that I'll be hosting for the community. I'm your host Harpreet Sahota. Let's ride this beat out into another awesome episode. And don't forget to subscribe, rate, and review the show.

Harpreet Sahota: [00:01:17] Our guest today, has set a goal for herself to break into the field of Data science and has been working at it with such persistence and intensity that it has been such an inspiration to witness.

Harpreet Sahota: [00:01:27] She's an undergraduate mathematics major at Southern Illinois University who chose Data science because of the uncomfortable and rewarding feeling you get when you solve a challenging problem. She's driven by a desire to obtain an in-depth understanding of the problem statement so that she can leverage her love for advanced statistical models to extract and uncover insights that are hidden and locked away in uninformative raw data and build data products that have practical use and applications. She's a voracious learner who is unafraid to take concepts she's learned and immediately put everything together into a project or article. She has a passion to learn by sharing knowledge and helping others. And she's done this through some amazing articles and blog posts written in such a way that her readers can understand her explanations clearly. So please help me in welcoming our guest today. - someone who is unafraid with ambiguity and loves to find meaning in Data - Khuyen Tran.

Harpreet Sahota: [00:02:20] Khuyen, Thank you so much for taking time out of schedule to be here. I are really, really appreciate it.

Khuyen Tran: [00:02:24] I'm happy to be here. Thank you for having me on the show.

Harpreet Sahota: [00:02:27] Oh, definitely. It's my pleasure.

Harpreet Sahota: [00:02:28] Hey, so tell me about how you got interested in Data science and machine learning. What kind of drew you to the field?

Khuyen Tran: [00:02:35] Yes. So I always interesting in combination between math, programming, and application. All through my course I major in applied mathematics. But I'd find anything like machine learning. How I could use the concept of mathematical equations to apply in something really useful as such as, like predict the heart disease. That made me fascinated at the first time seeing machine learning.

Harpreet Sahota: [00:03:02] So I went through a blog and get some really cool, interesting articles that you've written. One in particular out I was hoping you talk to us about is your struggle to dedicate time for Data science. Can you share some of the struggles and strategies that you've used to enable yourself to boost your learning rate and accomplish more?

Khuyen Tran: [00:03:23] So with my circumstance, I'm a full-time student. So I have - I'm participating in full-time course at school, and I also have a new internship and with my research, a couple of other projects. It's really hard for me to be able to accomplish any of them well, without prioritizing my time well. So, I create a system of finished three task per day. Before the week happens, I will see okay, What are the deadlines? What task I need to finish this week. And I will prioritize the task for that day - and make sure it's short but important. And I also create a time when I should check the email, and when I should check the phone. Because I want to minimize the clutter and the distraction as much as possible when I do the work.

Harpreet Sahota: [00:04:11] That's a very, very good and disciplined approach. I really like that approach. On your same blog, the same website, where you've got a really interesting post where you utilize the Eisenhower decision matrix and then talk about how to maximize your productivity with Python. Can you tell us more about this project and how it's helped you?

Khuyen Tran: [00:04:35] Yeah, sure. So, I wanted to talk about a little bit about Eisenhauer Matrix. It's about the concept of maximize important task over the urgent but not important task, which is checking email, replying to emails, something that is urgent but not important. It's hard to do that, knowing that, but  it's really hard to apply that. Because we have like, let's say, 20 tasks a day. It's really hard to know with task should you prioritize, which job is important and urgent. So I was asking myself a question, can I do this with a tool that I know - python and math. And that's how I started the project. I used optimization with Python, with the input as the importance for each task, how the duration of each task, and with those Data my objective is to  maximize productivity. Specifically the importance score. And that's how I achieve that resource that I got in the article, which she's having the list of task. Let's say I have 20 tasks a day. It will just be for me at four or five tasks that are important.

Harpreet Sahota: [00:05:52] That's really cool. Do you do this for yourself every morning or is this kind of just a one off project that you had made?

Khuyen Tran: [00:05:58] Actually, I combine this and other tools for me to do this. But what I carry out from this project is a concept of prioritizing the important task.

Harpreet Sahota: [00:06:11] Yeah. That's a really, really good skill and good trait to have. Because oftentimes we get so distracted by our devices, like you mentioned, you feel the urge to check social media, you feel the urge to check email. So when you're when you're confronted with these urges to go and get distracted. How do you keep yourself focused on the task? What do you tell yourself?

Khuyen Tran: [00:06:34] It's more about the will. We all have a limited amount of willpower. So I tried to create that environment that I'm not distracted, because I am like any other people. Really easy to get distracted if there are any notifications on my phone, so what I do is put my phone on airplane mode and throw it some where. Sometimes it's really hard to find my phone. And also I tell myself that just work on this for one hour and then, I finish. Because I tell myself a really short amount of time that would give me the really intense focus. Instead of saying, oh, I had the whole day to do this so I can just relax and do the work. So by shifting my mindset. I can work much more efficiently for a shorter amount of time.

Harpreet Sahota: [00:07:17] I like how you mindset here. So did you always have this kind of mindset or did you have to cultivate and train yourself to think in this type way?

Khuyen Tran: [00:07:25] Yeah, that's a good question. So I think the condition trained me to be more efficient. Because I'm really like I like to achieve a lot. And with the limited amount of time that we all have. I will not be able to attend to all of those things without finding a better way to work more efficiently. And those strategies are from reading a lot books about productivity and also asking a lot of people how their style of working.

Harpreet Sahota: [00:08:03] Are you an aspiring Data scientist struggling to break into the field? Then check out dsdj.co/artists to reserve your spot for a free informational webinar on how you can break into the field that's gonna be filled with amazing tips that are specifically designed to help you land your first job. Check it out, dsdj.co/artists

Harpreet Sahota: [00:08:28] So talk to you about some of these books that you read for productivity. Can you name a couple?

Khuyen Tran: [00:08:33] Yeah, sure. Recently I read a book called Deep Work by Cal Newport. Yeah, it is a really good book about different ways that you can focus on the work in that distracted world. And other books that I come across are UltraLearning that has different strategies to learn new skills quickly in a short amount of time.

Harpreet Sahota: [00:08:56]  Very, very good books.

Harpreet Sahota: [00:08:58] I have both books too, sitting here in my shelf. I'm happy that you've got introduced to these books so early in your career. You know, unfortunately, these are books that I didn't get introduced to and techniques that I didn't get introduced until I was much later in my life and in my career. So good for you for for exposing yourself to these concepts and these ideas and really implementing them into your life so that you can become more productive. So that's really, really good.

Harpreet Sahota: [00:09:23] So, you know, you've written a lot of awesome posts on Towards Data Science. Can you talk to us about the inspiration behind writing these posts?

Khuyen Tran: [00:09:34] I first start this writing because I read a lot of things and I tend to forget things. So I want to find a way that I could record whatever I write, because it would help me to reinforce my understanding. And second is later if I forgot, I could come back to it. That is my initial purpose of writing. When I share I start to see a lot of positive feedback from readers, that's my new motivation. The way that I find ideas for my article is just, you know, from looking around the things that I often do. What I learn, and I'm sure that even the small pieces that I learn, can be helpful to some others. Because different people are in different phases. I feel like I'm a part of the community that share and learn.

Harpreet Sahota: [00:10:23] That's a good approach when you teach something. You get to learn it place. And then you also get that positive feedback when you contribute to the community. Obviously, that's that's how I came across you and your work. That's how you ended up on this podcast, right. Is through the awesome contributions you're making.

Harpreet Sahota: [00:10:40] So I know there's a lot of our audience who are breaking into Data science as well. And they're juggling their full coursework while trying to learn all the extra things that you need to know to be a data scientist. Because, you know, in school, there's not a lot of overlap between the two. 

Harpreet Sahota: [00:10:55] Do you have any tips for notetaking, for our listeners out there?

Khuyen Tran: [00:10:59] I would say I try to capture just the most important thing in the courses. Let's say I take a course that's online for Data science. I would just try to capture the most important thing and they try to convert whatever I have from the paper to start using it on a particular project because I know the best way to learn anything now from taking notes, but from make it yours by using it.

Harpreet Sahota: [00:11:32] Tell me more about how you go about building your ideas for your projects and for your posts. How do you how do you kind of go through that process?

Khuyen Tran: [00:11:42] Projects that I have are those that I just start from, let's say, thinking what if I could apply the things that I know to answer this question. For example, I have a project on finding the correlation between the sun and depression. That is something that I heard before, but I ask myself - So, I am able to test with Data? That inspired me to find a Data and to use my knowledge that I possess to answer the question.

Harpreet Sahota: [00:12:17] I like that approach a lot because I think a lot of times when students are building projects, what they tend to do is they tend to say, oh, what Data set could I find? What algorithm can I use?

Harpreet Sahota: [00:12:28] But instead you're coming for a place where it's, let me work on something that I find interesting. And let me pursue a problem statement that I think is going to be fun and interesting to solve. Which I think is definitely the best way to go about doing personal projects.

Harpreet Sahota: [00:12:46] So let's talk about some of the challenges that you have to overcome while you're creating the process. Like, you know, how do you find the right Data? How do you organize your thoughts? How do you structure your project? How do you kind of overcome these challenges?

Khuyen Tran: [00:13:00] Yes. So let's talk about the challenges that I have recently about re-structuring

Khuyen Tran: [00:13:06] my Data. I want to make it so that I can be able to use the code in the future. Because, as you know, a lot of Data science project, many of them you could find a similar structure. So I try to figure out the best way that I can reproduce it, and also how to write it so that in the future when I read it again, I can understand it. So those are the challenges. And how I overcome it is I ask a lot of questions and I did a lot of research. I did a lot of research by reading books, Google.

Khuyen Tran: [00:13:38] I also ask a lot of questions through messaging the experts who have worked on the field for a long time. What are their strategies and how can I take their pieces of strategy for my own project?

Harpreet Sahota: [00:13:51] So when you're messaging the experts, how do you get in contact with them? Are you finding them on LinkedIn? Are you talking to people on your school campus? What's your approach for that?

Khuyen Tran: [00:14:01] It's both. The most valuable relationship I have right now - Contact I have it right now - is with a person through LinkedIn. I just message him because of my curiosity to learn from his experience. And we happen to have a talk outside of messages in a coffee shop and we keep a contact, for like four or five months. And it was really valuable for me because he works at IBM, I learned a lot from him. And also I have two best friends who also are really interested in Data science that keep me motivated. Whenever I have anything that I have question  about, I always find good resources, a good place for me to ask.

Harpreet Sahota: [00:14:41] So there is a lot of noise, fun on Google and on the Internet. There's so many resources everywhere. Do you have a specific strategy for finding the right resources when you're Googling?

Khuyen Tran: [00:14:56] Yeah. Actually, I really like to -  So, for the coding, you know, some short coding things, I would just Google. But let's say for analyzing the data, how to analyze this data. Like what the steps? Something involving the step or involving like a whole structure, I would use some book instead. Because I feel like the author who already wrote the book, they dedicate a lot of time on fixing and using their best resources to put in the book.  

Khuyen Tran: [00:15:31] Let's say want to structure the code. I would love to find a book on that topic.

Harpreet Sahota: [00:15:36] When you're first starting out with a project, how do you kind of develop a plan of attack? Do you have like certain milestones that you set for yourself that you want to hit? What's what's the process like for that?

Khuyen Tran: [00:15:53] Yeah, I'm sure. So first of all, I want to set for myself a deadline.

Khuyen Tran: [00:15:59] Because, you know, the project can go for months if you don't set yourself deadlines and so many things always are coming up. So I will set myself a short deadlines. And Make - Sub-task the big task so that I have small, approachable, small task that I could finish in one or two days. And then I would star with understanding the Data and asking the right questions, because the foundations are really important. If I just going through the Data without understanding what do I want to get out of the Data. I would get lost. It would take more time for me in the long, in the long run.

Harpreet Sahota: [00:16:49] So when you're understanding the Data, what's your process look like. What are you doing with you're understanding data?

Khuyen Tran: [00:16:56] So first of all, I will see like. Okay. I will use data visualization and some data analysis approach to see it. With data visualization, I don't make it look fancy, but I try to use like just, you know, simple visualization tools that give me straight away the things that inform the pieces of information that I need. From that, it gave me that direction for where I should process the data.

Harpreet Sahota: [00:17:22] So you bring in your dataset, do some simple visualization, and then from wherever you see, as you're slicing, dicing and visualizing the data, you let that kind of guide the rest of your process, right. So, yeah, that's really good. I know a lot of our listeners are going to benefit a lot from all the tips you've provided them.

Khuyen Tran: [00:17:39] So I'm curious, when you're in an interview setting and let's say you come across a technical question that you don't know how to answer right off the bat. How would you how would you handle that? What would you do? How would you proceed that kind of situation?

Khuyen Tran: [00:17:55] I would start with - can you be more specific about that? Because when I ask that question, they would ask another kind of question. And if I still don't know about the questions, I will be honest with them that this is not something that I have come across before. But I know something similar to this, that maybe I could use this approach to solve that problem.

Harpreet Sahota: [00:18:18] That's a good approach. Like kind of, you know, just showcasing your thought process. Walking them through how you would answer a question. And, you know, asking for more feedback, asking for more clarification, and taking it from there. Talk to me about what your job search process has been like. You know, how do you - we're in the middle of a pandemic now, so things have been kind of interesting. Have you been consistently applying for jobs? Have you been getting call backs? What's your, what's you...

Khuyen Tran: [00:18:49] Yeah. So I got the internship before the pandemic. So I didn't recently applied for a job, but it was really hard. Like Data science - It's really hard to get internship. Like I tried to find an internship in data science. It was really difficult. I mean before I created for myself schedule applying for five internship a day which I reached to about more than one hundred applications, but I rarely - I got like one or two reply. They didn't turn out to become a job.

Harpreet Sahota: [00:19:29] Yeah, it's definitely challenging. So when you're applying for internships, are you looking for a particular industry or particular type of role, or do you kind of just apply for the roles that, you know, in no specific industry, but it looks like it it would be an interesting kind of role.

Khuyen Tran: [00:19:45] Yeah. And I think, like, apply for industry that valuable to Data science. Is it really change when I start to apply more, because at the beginning I didn't know what I was looking for. But the more I apply and the more I do more projects, the more I know what kind of things that I would like to work on. So right now, if you ask me the kind of job that I would want to apply when looking at a list of the job opportunities, I would have a better idea of which one I would like to apply. And I think that would be better than apply to something that is not really relevant is a good strategy

Harpreet Sahota: [00:20:26] Yeah. My my general advice to students who are looking for internships or looking for roles, I tell them to kind of pick an industry that you are trying to focus on, that you find interesting. And then in your extra time, whatever extra time you manage to find as a student, then what you can do is go research that industry and then learn the vocabulary, the terminology that they're using in that industry, and then also look up case studies for that industry so they can gain an awareness for the type of problems and challenges that a Data scientist in that industry is working on. That does a couple of things for you. What that will do, as you know, rather than taking a shotgun approach and applying to everything, you're really focusing. If your energy's on to one industry and you're focusing your self directed learning path towards something that's going to end up in a a favorable kind of let me rephrase that. You're focusing here energies in one direction rather than spreading it everywhere, right? Yes. So, you know, you start becoming accustomed to the vocabulary. So becoming accustomed to the type of problems that are working so that when you go to interview for these roles, for these internships, you just have a greater awareness and you will be able to convey that during the interview. One last question here before we jump into a lightning round. What's the one thing you want people to learn from your story?

Khuyen Tran: [00:21:51] Whatever ambitious goal you have, give it. Have the goal in front of you, have the goal in your mind. Have specific task and approach that how you want to tackle it. I will say if you'll have that mindset in your mind, a lot of things you want to achieve will be done.

Harpreet Sahota: [00:22:09] Let's go ahead and just jump into a lightning round. So first question here. Python or R,

Khuyen Tran: [00:22:15] Python

Harpreet Sahota: [00:22:16] Where do you see yourself in five years.

Khuyen Tran: [00:22:18] I'm a  Data scientists working with a lot of papers and I guess I love implementation, which is what I like.

Harpreet Sahota: [00:22:24] So what's your favorite question to ask during an interview.

Khuyen Tran: [00:22:28] What attributes of a candidate are you guys looking for in this position?

Harpreet Sahota: [00:22:34] Another question that you could try asking, towards the end of an interview is try to ask the interviewer - so that you end on a positive note - What do you enjoy most about working here?

Khuyen Tran: [00:22:44] That's a good one. So it gave them a positive mood

Harpreet Sahota: [00:22:47] Yes. So its like the recency effect, right. So, you know, if they end the interview talking about everything they love about the company, they're going to be in a good mood and they'll attribute and associate that with you. So that's a little psychology hack there for you.

Harpreet Sahota: [00:23:03] So what is the weirdest, strangest or hardest question that you've been asked to interview?

Khuyen Tran: [00:23:10] It's a funny one. If you could be a cartoon character. Which one would you be?

Harpreet Sahota: [00:23:14] Which one would you be?

Khuyen Tran: [00:23:15] I say Winnie the Pooh.

Harpreet Sahota: [00:23:17] Winnie the Pooh, ok, why is that? Why Winnie the Pooh?

Khuyen Tran: [00:23:21] I don't know. He just I guess he just don't pay attention much about the things around him. And he's like really focus on his honey. Which is who I am. I like, like, aside my work, I didn't really pay attention to the things around me. And sometimes the people around me just say, hey, pay attention to that. So I just think he's like me.

Harpreet Sahota: [00:23:39] All right. Say you're you're Khuyen The Data Bear.

Khuyen Tran: [00:23:42] Yep.

Harpreet Sahota: [00:23:43] There You go. So what's the best advice you have ever received?

[00:23:48] One minute organizing will give you back hours in the future.

Harpreet Sahota: [00:23:53] Very good advice. So this next question, I usually ask my guests if they can go back in time and tell their 18 year old self something. But I think 18 year old for you is not that long ago. So let's take it back a little bit further. If you can go back in time and tell 15 year old Khuyen anything. What would it be?

Khuyen Tran: [00:24:14] I would say to learn to love whatever you are doing and you will start to do it really well, because anything that you want to master, you should start with learning how to love it first so that you had the motivation to push your forward through the challenges.V. 

Harpreet Sahota: [00:24:30] Very, very wise advice. That's very good. So have you read the book by Cal Newport So Good They Can't Ignore You?

Khuyen Tran: [00:24:37] It is a book that I am reading.

Harpreet Sahota: [00:24:39] Yes. So that's, that sounds exactly like the premise of that book.So that's very good. Very good book.

Harpreet Sahota: [00:24:44] What is your favorite book. Fiction, Nonfiction, or both. That you'd recommend to our audience. And what was your biggest takeaway from it?

Khuyen Tran: [00:24:51] I would say nonfiction and the book in my mind right now is Outliers, because I really like the concept of 10000 hours. Because that give me that belief that if I want to be that kind of expert, if I want to be that Data scientists, I could achieve it with enough hours of dedication.

Harpreet Sahota: [00:25:15] What's up, artists? Be sure to join the free, open, Mastermind Slack community by going to bitly.com/artistsofdatascience. It's a great environment for us to talk all things Data science, to learn together, to grow together. And I'll also keep you updated on the open biweekly office hours that I'll be hosting for our community. Check out the show on Instagram @TheArtistsOfDataScience. Follow us on Twitter at @ArtistsOfData. Look forward to seeing you all there.

Harpreet Sahota: [00:25:44] The work that Malcolm Gladwell references in Outliers is based. The 10000 hour rule that he calls it is based on work done by a psychologist from, I believe, the University of Central Florida. Anders Ericsson. So that book was called Peak. Yes. So that's a great one that you should check out. I think you'll really enjoy it because it's going to be in line with a lot of the books that we've talked about here. So what else is on your reading list? How many books? How many books do you go through in a month? Are you doing audible, are you actually reading?

Khuyen Tran: [00:26:19] Yeah. So before I do more audible. But now since I stay at home I read a lot. So I create like a habit of reading at least thirty minutes a day. I think I go through,  and I just really fast, because of my habit of reading, trained me to read quickly on the things that are important in the book. I would say about four books per month.

Harpreet Sahota: [00:26:40] So do you have any tips for our listeners on how to read a bit faster?

Khuyen Tran: [00:26:43] Yeah, sure. I will say well, you - because a lot of people, when they're reading a book, they would read from the, you know, every pages from them. Like starting from the beginning, I would say they should read a book and having fun with it. First of all like, you know, read like...does this book worth my time or not? You know, just read through like the top, and the middle, go for different section that you find interesting. And read through them. That will make you more interested in the book. And if doing that does not make it interested in the content of the book, I think you should just skip that book and go into the next one. Because there are a lots of resources of books out there. So if you waste your time on something that is is good but not excellent, and does that provide you a lot of pieces of knowledge. I think you should skip it and go to the thing that you are interested in. And that is also better in the way that if you read something that you are interested in, it is more likely to stick.

[00:27:45] Very good advice that there is a he's a CEO, founder of Angel List. His name is Naval Ravikant. And he has a quote that that is pretty much read what you love until you love to read. And he also expounds on the philosophy that, you know, actually you don't have to read a book from cover to cover. I think a lot of times people get a book and they feel obligated to read it from first word to last word. And then somewhere in the middle, they get tired and bored of it and they stop reading altogether. But your approach is very good, right? Just you know, you don't have to read the entire book. You can skip through and go through parts that you like and pick up information there. So I'm a terribly slow reader, so I consume a lot of my knowledge through an auditory fashion. So I go through audible a lot. So I just read my books on, you know, 2.5x to 3x speed and just blast through books. So I think last year, 2019, I went through about 100 books that way.

Khuyen Tran: [00:28:47] Wow, that's that's amazing. Maybe later. I should ask you about your reading list because I feel like you have read a lot of interesting book.

Harpreet Sahota: [00:28:57] Yeah, definitely. I read a ton and you know, I make use of Audible's very generous return policy. They say that if you don't like a book, you can't return it and get your credit back. So I've done that. I've done that a lot. Well, see the essay I read through a book, read a book, and I return to get my credit back. I guess now audible will probably never sponsor this podcast, if more audible heard that.

Harpreet Sahota: [00:29:24] What song do you have on repeat right now?

Khuyen Tran: [00:29:28] How about this song that I sing a lot every morning? I will say Winnie the Pooh. Which is funny. But I'm like, I don't know, it's really cute and I keep like singing it. And some time I turn it on when I study. I like a lot of Disnet songs while I doing my work and studying.

Harpreet Sahota: [00:29:49] So where can people find you? How could they connect with you online?

Khuyen Tran: [00:29:53] People would find me. Through my articles link which is

Khuyen Tran: [00:30:11]  medium.com/@khuyentran1476

Harpreet Sahota: [00:30:12] Awesome. Do you want to shout out your website as well.

Khuyen Tran: [00:30:16] It is mathdatasimplified.com/.

Harpreet Sahota: [00:30:28] Thank you so, so much Khuyen for taking time out of your schedule to sit here and talk with me today. I think a lot of up and coming Data scientists are going to learn a lot from the advice and tips that you've given and today. As well as, you know, visiting your blog and visiting your medium posts and your towards science posts. So thank you again for taking time out of your schedule I really appreciate it.

Khuyen Tran: [00:30:52] Thank you for having me on the show