5 Takeaways from the Career Panel on Data Science Master’s Programs

Takeaway 2

Test the waters and boost your data science skills with free online resources

For anyone who’s seeking to trial some data science courses before applying to a graduate program or looking to boost some of their data science skills in preparation for one, the panelists shared several free resources, including data science classes on Coursera and MIT Open Courseware, Harvard University’s CS50 Introduction to Computer Science class on edX, and Brandon Foltz’s YouTube channel. Matt also recommended DataCamp which offers limited content for free or the full library for $25/month.

👀The ADSA CDN has curated a list of educational resources that are freely available here.

Takeaway 3

Shop around before settling on a program

All three panelists returned to their alma mater for their data science master’s degree: the University of Virginia, University of Texas San Antonio, and Willamette University. The decision was driven by a combination of factors, including proximity, familiarity and trust, and compatibility with work schedules. The panelists knew from experience that they could rely on their undergraduate institutions to provide them with high-quality graduate education in data science.

In Matt’s and Colleen’s case, they also went back to their undergraduate institutions because the programs were designed for working professionals. Colleen, however, noted that this may not be desirable for some prospective students who would like to attend a program right after their undergraduate education. Colleen recommended shopping around for a program that works best for your needs and interests, since some programs may be designed for different stages of one’s career. On that note, Matt recommended asking about the program’s job placement record. Libby added that it’s also important to compare prices, if you’re not constrained by location.

Takeaway 4

Pursue meaningful projects

Colleen mentioned that she and many of her classmates made the most of their master’s program by using data from their jobs, which made the projects particularly meaningful and impactful. If you are going into a program straight from your undergraduate program, Colleen recommended pursuing projects that you’re truly passionate about and not just ones that enable you to practice data science skills. Libby echoed this sentiment and encouraged students to go one step further and to share their work with others online. “Doing your work out loud” will attract employers who want you to do that same work for them.

Takeaway 5

Connect with others

Matt and Libby emphasized the importance of establishing relationships with your classmates as quickly as possible. They will eventually become your collaborators and friends. In particular, Matt expressed immense gratitude for his classmates and believes he wouldn’t have graduated without them. He also appreciated that UVA encouraged collaboration and he recommends that all prospective students inquire about a program’s stance toward collaboration before enrolling.

If your school of interest hasn’t established a way for students to communicate and collaborate, with permission from the program, you could follow in Libby’s footsteps and create a Discord or Slack channel for new students. You could then ask the program to share the channel with incoming students.

You can also join existing communities, like ADSA’s Career Development Network or student groups that are part of professional associations like the American Statistical Association and the Association for Computing Machinery. For those who are specializing in research software, we also highly recommend checking out The US Research Software Engineer Association and The Carpentries.