Being a part of the 2022-2023 University of Virginia Master of Data Science Residential program has been an amazing experience for me. Our cohort is a dynamic group of students who love to collaborate; everyone is bright, engaging, and confident – great students and even better people. We are a tight-knit group that loves to hang out and have fun.
Our journey started in June 2022 with a week of Orientation at Wilson Hall. Teams from Admissions, Student Affairs, Career Services and Faculty shared resources and information with us. We began making connections and building friendships within the cohort. And we learned about the various technologies and tools we would be using throughout the year. Then, it was time to get to work.
Our first class was Programming for Data Science with the Program Director, Professor Rafael Alvarado. This class created more opportunities for us to connect through the classroom setup and the exercises. We won’t forget the memories made in that class! From the dating app for programmers where you could “Git Commit” to a person, to the Top Gun Call Sign generator. The class brought us together, and we helped each other when we were struggling.
Next, we took Practice and Application of Data Science with Professor Kropko, who is the cohort’s favorite professor. His class was engaging from start to finish, and around every corner was another unique challenge to conquer with a classmate.
Our third class (and last of the Summer 2022 term) was Linear Models for Data Science with Professor Woo. The class helped refresh the foundations of models that were some of the simplest in Data Science. By the end of the class, you could recite the four assumptions of linear regression, but now heteroskedasticity is common knowledge. With great data sets in this class—yes, I am looking at you Palmer Penguins and King County House Price data—it made our learning and collaborating a lot of fun. This class was embodied by “Gary the Gentoo Penguin,” whose favorite perch was the whiteboard in Elliewood.
And what is summertime without a little extracurricular fun? As a cohort, we went on early morning hikes, played trivia Wednesday nights, and played volleyball or golf. In the summer, Grounds is quieter without the usual hustle and bustle of students during the Fall and Spring semesters. Summer 2022 was a time of harmony for M.S. in Data Science students; some call it “the calm before the storm.”
The Fall 2022 semester was an all-out sprint for us, as it was the most academically challenging. The semester featured Foundations of Computer Science, Ethics of Big Data, Statistical Learning, and Bayesian Machine Learning. Surviving the gauntlet that was this semester sealed the fate of our cohort. It gave us the self-confidence to know that we can do this!
The previous cohorts had warned us about Bayes, but it was not until that first day of class that we realized just how tricky it would be. Bayesian Machine Learning provided a new perspective to many of us. As it turns out, most of the Data Science experience that we had up to this point had been disguised as Bayesian Machine Learning (just with harsh assumptions made about the distributions of the data). Once the pieces started coming together, we began to understand the tools and how to use them.
Statistical Learning took us on a deep dive into the more complex methods of Data Science. “Trees” and “Clustering” are just two of the methods, but this class took us beyond that as we learned how the methods worked and what was “under the hood" of each method. We also learned how to execute the methods and when to use each of the methods.
Ethics gave us a break from the traditional STEM class track. In Ethics, we got to use our creative sides to think of just how the algorithms we deploy might not be used for good. We also delved deeper into the accountability and transparency that governs our ethical data science practices. We even created “ethical superheroes” who fought the enemies of unjust and unethical data science practices.
The Spring 2023 semester was filled to the brim with stimulating classes, exciting conferences, and our capstone projects and presentations. We juggled many competing responsibilities and deadlines while still managing our schedules, getting good grades, and being present for one another. There were many memorable moments from Spring semester.
The classes in the Spring semester were fascinating and included Deep Learning, Capstone Part II, and two electives (Big Data Systems, Exploratory Text Analytics, Data Visualizations, Digital Signal Processing, and other exciting courses). The variety allowed us to explore different areas of data science and remain engaged. Of course, with more courses came more memorable assignments; from the land guzzler to the Jane Austen corpus, to some weird computer-generated noises, we experienced a little bit of everything in Spring 2023.
We attended several conferences; from DC to Boston, my cohort went everywhere to gain knowledge outside the classroom. The conferences spanned a wide variety of topics from sports analytics to the environment, climate change, and more. We were able to network with many professionals and gain valuable insight into diverse domains.
The final focus of Spring 2023 was our Capstone Projects. The process began early in the Fall semester, but the last semester is when everything comes together, and the project cumulates with presentation day. Each Capstone group gets seven minutes to present their findings with three minutes of question and answer. All the groups did an exceptional job and the audience had great questions for us.
While my M.S. in Data Science Residential cohort accomplished a lot in a short amount of time, we will continue to do even more as we enter the working world. Whether we are jumping right into a new role or taking some time off to think about our next move, the program changed the trajectories of our lives. We met new people, we bonded over shared struggles, and we found a way to come together and have a momentous year. While our time as classmates has ended, we can look forward to the future as the next cohort follows in our footsteps.