The MIT Master’s in Business Analytics experience from former Data Peer Consultant

Know how to talk to stakeholders and communicate what you’re doing to people who are not in analytics. If they can’t understand what you’re doing, they can’t fully trust it, and what you are doing is not valuable to them.

- Vincent Lao, former Data Peers team lead and Master’s student at MIT

Introduction

Are you an undergraduate Data Science student who is considering a Master’s program and wondered if it was worth your time? In this article, we interviewed Vincent Lao — a former Data Peer Consultant team lead who is currently pursuing a Master’s in Business Analytics at the MIT Sloan Schol of Management. In 2021, Vincent earned a double bachelor’s degree from UC Berkeley in Data Science and Statistics. Today, he is located in Cambridge, Massachusetts, and is almost one year into the program.

When and how did you come to the decision to further your studies and pursue a Master’s degree?

“It was around March 2021 when I started my application process for data science Master’s programs. I became interested in MIT’s Business Analytics program and got a better idea of what the program could offer by watching Youtube videos on student interviews, faculty members, and course content. I want to pursue a Master’s degree because it is difficult to break into the Data Science field with just a Bachelor’s degree. Typically, undergraduate students from Cal’s Data Science undergraduate program pursue an Analyst position upon graduation, which is not exactly what I want to do. I want to build models and make predictions with machine learning, which is what my Master’s degree is preparing me for.”

On a scale of 1(least) — to 5(most), how likely would you recommend the MIT Sloan Master’s in Business Analytics to Cal undergraduates? What is the difference between that and UC Berkeley’s 5th-year MIDS?

“5! I can’t speak much about MIDS as I am not enrolled in the program, but I can elaborate on what I enjoy about my current Master’s program.

In my current courses, I got a better understanding of how to apply data science skills to solve business problems. My coursemates and I worked together on projects for various companies, and those experiences gave me a better idea of how data science can be applied to real-world problems. Although Cal’s undergraduate Data Science program helped me build foundational data science skills, it takes a while to do so, and I did not get much real-world experience as an undergraduate as I do in my current program.

Ultimately, the purpose of a data science and analytics role in a company boils down to improving products, increasing sales, and forecasting trends. It’s about building a model to support your decisions. There is so much more to learn from the job, and this degree helps me get a better idea of what it’s like.

What stood out to me about my current program is that MIT has a huge emphasis on optimization. The business analytics program has one foot in the optimization world and another in the business analytics world. The professor who created this program actually works in optimization! This is unique because optimization is much more common in engineering than in data science, but there is actually so much useful knowledge in data science optimization. For example, after making a prediction [with data science], optimization can help encode decision-making and minimize costs through an optimization model. It’s not necessarily math-heavy — it’s more on using logic and proofs to construct and justify solutions.”

What do you think is the most transferable industry-level skill that you have learned while working in Data Peer Consulting?

“I worked as a Data Peer Consultant for 2 years, and the primary skill I learned was communication. Learning to work with people may seem mundane — spending time with the team, setting up and running meetings, managing internal projects — but it is crucial.

In our career, we would have to communicate with stakeholders who are most probably not from the same analytical background as us. If they can’t understand what we are doing, they can’t fully trust what we’re doing. No matter how hard we work on the project, it is not going to be a valuable asset to them.

At the same time, communication is also key among people with the same analytical background as us. In Data Peer Consulting, I learned to communicate and work with other data science members on multiple technical and non-technical projects.”

What would you suggest to undergraduate Data Science students in order to get the most out of their time at UC Berkeley?

“Spend time collaborating with your peers on projects. Courses that provide theoretical knowledge like time series, regression analysis, and NLP are cool courses, but the best long-term takeaways are hands-on group projects. Learning to scope a project is crucial to analytics and data science — we may have a lot of ideas on how to build a model, but learning how to break down a problem statement and manage a project are valuable experiences that will increase career prospects. I recommend data science courses such as DataX [Ind Eng 135] because these project-based courses give students an opportunity to work with others, communicate and create a project with a business-oriented end goal in mind. Given a problem statement, know what can be solved with the data provided within a certain time constraint — what is feasible, and what is not? Speaking from my current experience, we get to learn so much while working on actual projects rather than focusing on theoretical courses.”

Go Bears or Go Beavers?

Go Bears, definitely.

Conclusion

A huge thank you to Vincent who hopped on a Zoom call for this interview. Please feel free to reach out to the Data Peer Consulting team or Vincent’s LinkedIn for any follow-up questions. Email us at ds-peer-consulting@berkeley.edu or drop in our Office Hours on Mondays to Wednesdays from 12–4 pm!

Please be on the lookout for a future blogpost on UC Berkeley’s very own 5th-year Master’s in Data Science (MIDS) program!

Written by Mein Lee, Data Peer Consultant.