Harpreet Sahota is known for his Podcast The Artists of Data Science through which he interacts with various successful people in the Data Science industry to help fellow Data Science enthusiasts get motivated. Harpreet also is a mentor of many students and helps the community by holding office hours every week.
Harpreet’s journey of becoming a lead Data Scientist is inspiring. He answered all the questions with honesty and that’s what makes him so amazing.
- How he began in Data Science
- Best books to read for motivation and self-development
- 3 Most important skills in Data Science that can’t be taught
- His motive behind starting the podcast AODS
Learning, problem-solving, and communicating are the most critical skills you need to succeed as a data scientist. And these skills can’t be taught.-Harpreet Sahota
There are times where I feel like I’m not cut out to be a mentor, then I read the messages from my mentees and it reminds me that I am helping people get to where they want to go. And I guess that in itself is the only motivation I need.-Harpreet Sahota
CK: How did you first get into data science?
HS: When I was working my way through grad school I had the aim of becoming an actuary with a specialization in predictive modelling. This was around 2011 and I wasn’t too familiar with the job title of “data scientist” back then. I worked for a year and a half as an actuary and took a bunch of exams and was set to move into a Predictive Modeller position. But life happens, and sometimes you take unexpected turns. I ended up in biostatistics for almost 5 years, and right around 2018 is when I started to get into data science.
Check out what books helped 20+ successful data scientists grow in their career.
CK: What are the key skills that you use every day as a data scientist, and how did you develop them?
HS: Learning, problem-solving, and communicating.
These are the most critical skills you need to succeed as a data scientist. Not Python. Not R. Not deep learning. None of that matters unless you can learn effectively, make your way through ambiguous problem statements to find the best solutions, and communicate your results to a wide variety of audiences.
And here’s the kicker: I don’t think these skills can be taught. But they can be learned. For me it was a combination of books like Smarter Faster Better, Mastery, To Sell Is Human, Pragmatic Thinking and Learning, and Influence.
CK: 3 words that best summarize how you learned ML and data science:
HS: “On my own!”
CK: You were the first Data Scientist at your current company Price Industries. How challenging has it been to build teams from scratch and solving problems with Data Science like they never had been before?
HS: Building teams from scratch isn’t hard, managing expectations from people who haven’t worked with these types of skillsets and working on these types of problems is. When you’re the first data scientist in an organization, no one can tell you how to do your job, and you end up managing upwards a lot.
A big portion of the work we do as data scientists is research-oriented, and it’s tough to do agile methodology when you’re doing research. That was a challenge. Explaining to people that even though I don’t have anything to show for what I have researched and this time and effort is helping solve the problem.
CK: Books: which books have helped you the most in your journey and why?
HS: There are three books that have changed my worldview and belief system in ways that I can’t begin to articulate: Mindset by Carol Dweck, The Power of Habit by Charles Duhigg, and Linchpin by Seth Godin.
The ideas in these three books have collided in my mind in such a way that they shook me to the core of my existence and caused me to update my entire belief system and worldview.
CK: Your podcast, The Artists of Data Science has been running for more than a year now. How did it start off initially? What process do you follow in getting the podcasts out regularly and marketing them?
HS: It started off as a random idea in my head. I wanted to do something creative, but I didn’t know what that something was. I was sitting deep in thought one day and the idea of hosting a podcast just kept echoing in my mind. And it felt like the most perfect thing to do.
I think part of it was that I had reached a point in my career where I didn’t have any mentors of my own. And I didn’t know what I needed to know to make it to the next level as a data scientist. So, I figured I would reach out to leaders in the industry and ask them questions that I had when I was making the transition into data science, and slide in questions that I also had in the current moment. And slowly, I started evolving the show into a self-development podcast for data scientists.
I am intensely passionate about and committed to becoming the best version of myself. And I know that me becoming the best version of myself doesn’t have anything to do with me learning the latest tools, algorithms, or techniques – or anything related to data science for that matter. And based on the conversations I was having during my office hours with my mentees, I knew that there was a hunger in our field for people wanting to become more. People felt the same way I did.
But for some reason, data scientists get so caught up in being data scientists that we forgot we are humans. And we’re left with this agitation, coming from a deep-seated desire to fulfil our potential as complete humans. We want to feel inspired, and we want actionable tips that can help us achieve our full potential.
So, I started interviewing authors who wrote books that I found to be really interesting and in-line with self-development. I decided that my show would be the Impact Theory for data scientists.
CK: What is the biggest improvement that you introduced in the last 12 months that has considerably improved your workflow?
HS: Journaling every morning and planning my week out.
CK: You have been conducting the Open Office hours regularly and also mentor students at Data Science Dream Job. How does the experience of mentorship help you evolve as a Data Scientist? What is the motivation behind mentoring?
HS: People come up with questions that I have never thought about. Through mentorship I’ve been exposed to so many different problem statements, I feel like I have seen it all. My mentees will ask questions and I have no clue what the answer is, so I go and look it up. And along the way, I learn something new.
I mentor only through the Data Science Dream Job platform. Nowhere else. I don’t have or take any mentees outside of that platform. Mentees who join DSDJ are committed. They want to become better. They want to become more. And I want to help them get there.
I don’t really have any motivation for mentoring. It’s not easy and it is extremely emotionally taxing. Not everyone is cut out to be a mentor, and that’s ok. There are times where I feel like I’m not cut out to be a mentor, then I read the messages from my mentees and it reminds me that I am helping people get to where they want to go. And I guess that in itself is the only motivation I need.
CK: You have mentioned that you like to try out new technology by applying them to real-world problems. What challenges do you face currently while applying them on the job? And how do you overcome them?
HS: I don’t really face challenges with this. l honestly get to do and try whatever I want. It’s great.
CK: If you could choose to do only one for the rest of your life, what would it be and why – Being a Data Scientist, Mentoring, Podcast Hosting?
HS: Podcasting. Because it will force me to consistently read awesome books, talk to the authors who read them, and sharing my learning with others.
CK: Tag one or two people in your industry who you would like to see answer these questions.
HS: Sundas Khalid and Vin Vashishta
Chayan is a creative Data Scientist with an eye for details. An everyday learner and blogger, he has extreme eagerness to share knowledge and support the Data Science community. Connect with him on LinkedIn to get in touch and don’t forget to check out his Medium blogs.
Data Science | Machine Learning | Tech Blogger – upGrad
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