How I Chose The Right Data Degree for Me

I am currently a master’s student in data science, but I was not always sure which degree I wanted to pursue. While not all positions require advanced degrees, data-related graduate study is growing in popularity, and more industry positions are listing these advanced degrees as a preferred qualification. With increased popularity comes increased specialization, so which degree is right for you?

Everyone’s decision is (and should be) made differently, but I wanted to share my own experience with selecting whether and what type of graduate degree I would pursue. After taking a few massive open online courses (MOOC), participating in an incubator program, and researching certificate programs, I realized that I still wanted to pursue an advanced degree program that would offer more structure. In the meantime, I enjoyed working within research and business, which gave me a deeper understanding of why data science might be the right fit for me.

Since I majored in statistics during my undergraduate studies, I thought an M.S. in Statistics might be a natural choice to continue developing a deeper understanding of the field. With its emphasis on the theory behind statistical methods and probability, graduate coursework could be invaluable to the deployment of practical applications today. Because data career paths are still being developed there may be an added benefit to statistics degrees that allow for more varied roles involving data.

Meanwhile, my experience in business roles between my undergraduate and graduate studies helped spark my interest in using data to tell a story for broad audiences. Therefore, I also researched more applied, analytics degrees in both data analytics and business analytics. While experience can vary by company, data analysts are generally expected to be slightly more autonomous and technical compared to business analysts who work directly with other departments. Compared to other careers in data, analytics often involves using data to find useful information and reporting findings in a digestible way often using visualization.

Data science sits somewhere in the middle of statistics and analytics with an additional component involving computer science. A data science degree program would offer me a taste of the different possible career paths of a data scientist: data engineer, machine learning engineer, research scientist, etc. Even though data science is a new field and we do not yet know exactly how the field will develop and what career opportunities it will provide, taking courses in a balanced mix of statistics, programming, and machine learning would provide me with a diverse toolkit, both business and technical, to tackle practical problems.

Of course, there is a lot of overlap between each of these programs, with skills in software, programming, mathematics, and statistics required by each of them. The great thing about the ubiquity of data today is that choosing one program over another does not automatically mean your career path is locked-in.

In summary, I considered the amount of business involvement as well as theoretical understanding I would be interested in learning and went from there!

Want to become a Noodle contributor? Email: contributor@noodle.com