A Typical Week as a Master’s in Data Science Student
August 16, 2022
Are you curious about what a week’s schedule likes like in the M.S. in Data Science program? Now that I’ve wrapped up my summer term and am about to start the fall semester, here’s a snapshot of my typical week in this 30-credit hour, 11-month master’s program.
The eight-week summer term is very fast-paced for the residential Master of Data Science students. The workload is similar from day to day and once the rhythm of the semester is established, the work gets easier to manage. The summer comprises three classes: Programming for Data Science (CS 5100), Linear Models for Data Science (STAT 6021), and Practice and Application of Data Science (DS 6001). They are all three credits each for a total of nine credits taken over the summer.
The table above is the summer course matrix. The master’s program kicks off in late June and students are enrolled in classes throughout the summer. Programming for Data Science is a class that students attend every day for the first four weeks of the summer term. They then transition to Linear Models for Data Science for the final four weeks. The Practice and Application of Data Science class is stretched across the eight weeks and students only attend class Tuesday and Thursday afternoons.
Based on the schedule above, the busiest days of the week are Tuesdays and Thursdays since students have class in the morning and afternoon. Mondays, Wednesdays, and Fridays tend to be a little lighter since students only have one class. However, the afternoons of Mondays, Wednesdays, and Fridays are when students work on assignments for class. This ensures they have free time on the weekends, can hang out with friends, and catch up on work if necessary.
Summer Career Series
The schedule also includes the Summer Career Series presented by the School of Data Science career support team. Sessions are held every Wednesday and hosted by Associate Director for Career Connections and Community Engagement Reggie Leonard. They are a great introduction to career resources, timelines, and what to expect during recruiting season. Students usually just walk over from morning class together to attend the summer career series because lunch is provided before the sessions. However, some students opt to bring their own lunch or grab a quick bite at The Corner.
The fall term for residential Master of Data Science students is familiar territory because it’s similar to what a traditional undergraduate semester looks like. Students take five courses this term: Foundations of Computer Science (CS 5012), Ethics of Big Data (DS 6002), Capstone (DS 6011), Statistical Learning (DS 6030), and Bayesian Machine Learning (DS 6040). These classes range between 1-3 credits, and students will a total of 12 credits in the fall.
As you can see from the course matrix, Mondays are the busiest day with most of the classes stacked that day. Wednesdays and Fridays are the lightest since there are only two classes on Wednesdays and no classes scheduled on Fridays. Not having class on Friday is a bonus, but students are still expected to use the time for individual or group study, their career search, and job interviewing.
One of the classes in the fall—one I’m particularly excited about—is the Capstone course where students work with a government or company client to try and solve a data-related problem or question. The biggest challenge to the Capstone course is finding a regular meeting time to work with your group and set project expectations and timelines. Throughout the semester, students meet with their project sponsors to share their progress, ask questions, and receive feedback. The project is not completed in the fall but a lot of the groundwork is expected to be done.
Many students take advantage of the career events hosted by various companies and sponsors. These events include coffee chats, career fairs, resume reviews, and much more. Both the University School of Data Science career teams offer personalized support and job search advice and programming. They also put you in touch with UVA and Data Science alumni to expand your professional network and learn about possible career paths in data science. With the program moving at such a fast pace, it is never too early to start looking for a job or getting prepped for recruiting season which kicks off in the fall.
OUTSIDE OF CLASS
Places to Study
Finding a great place to study that works for you is critical. There are a lot of places for data science students to find the place that best fits their needs. One place that’s popular with students is a School of Data Science space located on the Corner. Located above Ragged Mountain Running & Walking Shop, the Elliewood Avenue location is designed for data science students to study and collaborate. The building features group study rooms, a large collaborative space, and a meeting room. Another popular place to study is up the street at Grit Coffee. The upstairs is usually a great spot for students that need a little pick-me-up before getting to work on their assignments. Another good study location is 1515, also located on the Corner and a satellite student center.
After reading this, the summer term can seem overwhelming. Not to worry though! There is plenty of time to still have fun with friends and get away from all the work. Many of the students find small getaways during the week and on the weekend. Some popular things to do include exploring the Charlottesville area, going for hikes in the nearby Blue Ridge Mountains, and competing in trivia night at a local bar or restaurant. Students hang out together and the cohort is a tight community, so it is very easy to make plans with classmates.
Chris Longchamp (MSDS ’23) is currently a student in the residential master’s program at the UVA School of Data Science.
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