Major: Data Science & Economics 2021
Role: Global Adoption Team Lead
When Amal Bhatnagar interned at a venture capital firm a few years ago, he began to notice how data science could be utilized within finance and business. Through this experience, he realized that a number of things in the venture capital space could be made more efficient through data science. Amal’s passion for these fields led him to write Data-Driven, a book exploring the intersection of data science and venture capital, set to come out in August 2021.
Question: How did you become interested in data science?
Answer: The summer after my freshman year I was working at a venture capital firm. I’ve always been interested in investing and business and I saw that there was a lot of room to be more technical. At the same time, I was taking Data 8, and Data 8 taught me how to be technical without having to code like a software engineer so that was really motivating and encouraging. That class really inspired me to try pursuing data science
"I think a lot of the time when students first get into data science, they’re a little intimidated because they feel that they have to learn math, stats, and CS, which is a lot, but I think doing personal projects in the domain that they really like can be really encouraging because they can see what they can do with data science versus without it."
Q: How are you participating in data science at Berkeley and/or in the community?
A: I lead the adoption team, and I’ve been with the adoption team ever since it started back in 2018. There, we help other institutions across the world start data science programs and help their instructors as well.
The [other] thing I do is I teach a class called Data 88E: Economic Models where we teach economics using data science techniques. That’s been really cool because through the position of an educator you’re able to inspire other students to pursue this intersection of econ and data science.
I am [also] part of a statistics club (SAAS). I’m a PM there so I lead different data science projects. Every semester, we work with a different tech client and come up with a machine learning solution to their business problem. So that’s been really cool because, over the past few semesters, I’ve gotten to work on a lot of different ML concepts that I wouldn’t have otherwise.
Q: How do you envision using data science in your future career?
A: I think data science is a way to really scale different solutions to business problems, coming up with different products or algorithms that are able to solve business problems deeper than Excel traditionally can.
Q: Any advice for students curious about data science?
A: I think the number one thing that students should do is work on personal projects. There are a lot of really great online resources (towards data science, medium, youtube) that show step by step what you need to do. And doing this before or even during or after Data 100 or CS 189 is very helpful because it helps you see a lot more of the real-world applicability. I think a lot of the time when students first get into data science, they’re a little intimidated because they feel that they have to learn math, stats, and CS, which is a lot, but I think doing personal projects in the domain that they really like can be really encouraging because they can see what they can do with data science versus without it.