I first heard about the Universities at Shady Grove (USG) as an undergraduate at the University of Maryland, Baltimore County (UMBC) where I double majored in Biology and Economics. During my time at UMBC, I was lucky enough to participate in undergraduate research in both biology and economics, and those experiences made me realize the importance of data in finding answers to big questions. After graduation, I decided I wanted to advance my education with a master’s degree in data science. Data science is a field that combines computer science, statistics, and subject-specific knowledge in order to gain a deeper understanding of problems. I ultimately chose the Master’s of Data Science program at UMBC that is offered through USG.
I chose to attend classes at USG because the program was rigorous, but flexible at the same time. I was able to choose subject-specific electives aligned with my interests and built on the knowledge I already had. At the same time, I felt that the program was equally focused on making sure that students had a deep understanding of the fundamental principles of data science. The professors are extremely knowledgeable, and because they are working in the industry, they are able to demonstrate how these technologies and principles are applied in the real world, in real time. The program is also flexible in that if — like me — you do not have experience with coding before entering the program, there are classes available to build up that knowledge base so that you can succeed. The professors and staff are really dedicated to student success. You can approach professors with questions about both academic and professional topics, and they are always willing to offer help and guidance. Furthermore, there is even flexibility in where you take classes, as some class sections are available both at USG and at UMBC’s main campus.
The USG campus is also conveniently located, and has many unique class offerings that are not available anywhere else. I am excited to study more about machine learning and neural networks, and to apply these concepts to real world issues.