What We're Looking For
Not sure if you are prepared for a computationally intensive curriculum? Concerned that you won't be a good candidate if you have already covered similar material as part of your undergraduate degree? We are looking for candidates with a range of backgrounds and skills. Some of our students have undergraduate degrees in Engineering and Math, while others didn't take any math or statistics courses in undergraduate. Some students have been working for years and are looking to change directions or to advance their careers, while others have just completed their undergraduate degree. We consider having students with a mix of backgrounds and skillsets to be a feature, not a bug.
What distinguishes students we admit?
Strong problem-solving mindset. Working with data requires thinking carefully and creatively about research questions and the data and methods of analysis that will be used to answer the question. Real-world problems are rarely as neat and easy to answer as those found in a textbook, and our curriculum is designed to ensure that all students have ample opportunity to work directly with confusing research questions, incomplete or poorly specified raw data, and other problems likely to be encountered as a professional data analyst.
Substantive background and interest in social science. Unlike data science or data analytics degrees offered by a computer science or statistics department, DACSS emphasizes a distinctly social scientific approach to data analysis and social data science. Students do not need to have majored in Social Science or a closely affiliated discipline (e.g., Political Science, Sociology, Economics, Anthropology, Psychology, History), but some prior exposure to substantive social scientific topics and concepts is desirable. Students with a background in engineering, math, computer science, or the natural sciences are encouraged to apply—just be sure to highlight your relevant experience and the reasons why you are pursuing graduate training in computational social science.
Strong communication skills. This program emphasizes all aspects of the research process, including research design, data collection, data analysis, and communication and visualization of results. Our curriculum emphasizes the challenges specific to communicating scientific results and analysis clearly and effectively to a general audience but assumes that students are already able to communicate clearly and effectively about other topics.
Willingness to engage with mathematical and logical concepts. Core courses and many technical electives are designed to allow students without a strong mathematical background to participate fully. The ability to engage with mathematical concepts can be demonstrated by a strong score on the GRE quantitative section; prior coursework in math, statistics, or research design; or prior work, research, or other experience described in the personal statement. Linear algebra is required to enroll in some courses, although faculty may accept students who can learn linear algebra concepts online and demonstrate adequate proficiency in the subject.
Some programming experience (optional). We assume no prior knowledge of programming languages used in data science (such as R, Python, or Java), but some prior experience with programming will make it easier to keep up with weekly computer-based course assignments. We encourage all students without programming experience to consider working through an introductory programming module online, such as DataCamp's Introduction to R. Also, DACSS 601: Fundamentals of Data Science is designed to make sure all students are well prepared for the rest of their coursework.