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Michael Ma C’20, G’21 on Using his Skills Gained at Drew University in the Real World

The Master of Science in Data Analytics alum wanted marketable skills that would transcend across industries

May 2022 – Michael Ma C’20, G’21 graduated from Drew University in 2020 with degrees in business, computer science, and Chinese language and literature, but he wasn’t ready to part ways with Drew just yet.

Ma continued at Drew’s Caspersen School of Graduate Studies to earn his Master of Science in Data Analytics and he hasn’t looked back. Ma has been able to use the valuable skills he obtained at Drew to further his career as a business intelligence developer at American Financing.

We recently sat down with Ma to learn more on how his education at Drew is setting him up for success.

Why did you choose Drew University?
I chose Drew mainly because of how the program was structured. The program was presented to me as a great opportunity to learn important analytical tools necessary for high-paying analytical jobs. Plus, an internship is required so you graduate with valuable corporate experience.

Why data analytics? What do you love about it?
I chose data analytics because it applies to just about every field. I didn’t really know what type of industry I wanted to get into after I completed my undergrad degrees—there were so many fields I could have gone into. I realized data is important and relevant across every industry in the world. I could go into sports, music, tech—basically anything I was interested in I could theoretically contribute and provide value by using analytical tools. Plus, there is a huge market for analytics and the jobs associated with analytical work are high paying on average.

How has your Master of Science in Data Analytics helped your career and your position at American Financing?
It has helped me tremendously. During my time at Drew, my professors and I worked a lot with predictive modeling for time series data. I’m using that experience to build a model for American Financial that can accurately predict the amount of customers the company will receive based on our historical data as well as using the seasonality of mortgage rates.

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