Danielle Su studied environmental science at Duke University and worked with the Environmental Defense Fund for two years before graduate school. She chose the M.S. in data science program through the Data Science Institute (DSI) at Columbia University for its reputation, location, and curriculum.Today, the 2020 alumna is a data scientist with IBM Marketing.
Congratulations! Can you tell us about any of your current projects?
I’m working on a data science model that scores clients’ marketing responses based on propensity for that response to become an opportunity for one of our sellers to pursue. It’s exciting for me to work in marketing because of how much data there is. I’m also fully realizing the weight and impact of a data scientist’s work. Even in the four months that I’ve been working, many executive decisions have been made based on analyses generated from the model. It’s great to work at such a data-driven company, but it’s also a good thing that all of us DSI alums know how to properly handle data, detect biases, and be meticulous when it comes to building models because it really matters.
How did you become interested in data science?
When I graduated from college and started working, I realized that I had spent a lot of my undergrad years thinking about the field I wanted to work in, but less time thinking about what exactly I’d be doing at my job. After working for a couple years, I took machine learning Coursera classes and fell in love with data science as a discipline. It was something that challenged me intellectually and I am passionate about applying it to answer the important questions that businesses and policymakers have.
What was your favorite course at DSI?
Machine Learning for Data Science with John Paisley. It provided such a great foundation for machine learning and made me feel confident to read any research paper in the future to understand anything that wasn’t taught in that class. Also, the slides from class were fantastic and I refer to them often.
What was your capstone project?
My capstone project was a research project with John Paisley and Marianthi Kioumourtzoglou on using a novel Bayesian nonparametric ensemble method to more accurately predict PM2.5 levels, or atmospheric particulate matter with a diameter less than 2.5 micrometers. It was great to work on a project with environmental relevance because it felt like going full circle in being able to apply what I learned in my master’s program to my undergrad major.
Which internship opportunities did you have during your DSI studies?
I had a part-time spring internship at SiriusXM and a full-time summer internship with IBM Marketing, where I now work. The internships gave me confidence to apply all that I had learned at Columbia to real world business problems.
What are three things you have gained from your DSI experience?
- The knowledge foundation for a successful career in data science
- A network of amazing data scientists and friends
- All sorts of data science skills and jupyter notebooks from assignments and projects that I still refer to when I need some inspiration
— Sharnice Ottley