Born from the advent of the tech industry, data science combines statistics, business, communication, and programming to optimize massive amounts of data. The Wharton Undergraduate Data Analytics Club (WUDAC) and Wharton Customer Analytics (WCA) collaborated to create the first-ever Data Days Conference, an event with the goal of demystifying this up-and-coming field.
Here are some tips about interviews, networking, and more from the Student Data Science Internship panel, featuring Wharton students who have worked with Facebook, Comcast, and Quicken Loans.
1. Learn the technical languages.
“I did get grilled a lot on my technical abilities [in interviews]. One of the single biggest things that everyone should be starting to learn is SQL. SQL is a language that works with databases. It basically answers how you pull data, how you organize it, how you answer questions with a large set of data. Almost every company uses it, and almost every data science position requires it at some point — maybe not up front, but you have to learn it eventually.
In terms of language for coding, you use either Python or R. Some companies have a preference, but I had the choice to choose between them. It’s good to know at least one of them.”
Sarah Ye, W’22 (Data Science Intern 2021 @ Facebook, 2020 @ Shopify)
2. Have business sense.
“The main focus of the interview was not drilling me on the technicals — it was seeing how I think within the product space as well. Being a data scientist isn’t just about engineering and modeling — you have to have a pretty solid product understanding. Know how you’re going to build the project, have some business sense in how all of these departments are going to integrate with each other, and see how you’re going to be able to move the product forward.”
Rohil Sheth, W’23 (Data Science Intern 2021 @ Comcast)
3. Reach out.
“I got to know many data scientist students who were older than me, and I know alumnae who I worked with. At the end of the day, they gave me referrals. In terms of networking, what you need to recognize is that it’s good if you get a referral. Get to know people older than you. Most people are willing to help as long as they know you. Just reach out! What’s the worst that can happen? They might not reply to you, and then you can call it a day from there. You never know — sometimes they end up helping you a lot.
If you don’t have a referral, it’s not the end of the world. Referrals get you through the zeros round of the recruiting process, which is not getting deleted by one of the bots that reads the resumes. If you have a referral, it guarantees that your resume gets looked at by a recruiter. But if you don’t have a referral, write a solid resume, and know how to write one — that’s really important. As soon as you pass the screening and get your resume in front of a recruiter, it’s not that different from having a referral and not having one.”
Sarah Ye, W’22
4. Just get started.
“With something that’s technical like data science, what deters people from getting into it are thoughts like, ‘I don’t know anything,’ ‘I don’t know where to start,’ and ‘I don’t know where to go.’ That’s very valid — it’s not a very simple thing to approach. Data science is so broad with multiple languages, a whole lot of technology and frameworks that you can be using. But get started. Taking that first step is probably the hardest part of it. Everyone in the field started not knowing anything about data science. Start wherever you can, and take it from there.”
Lasya Mudigati, W’24 (Data Analyst Intern 2021 @ Quicken Loans)
— Gemma Hong
Posted: October 29, 2021