Master of Science in Data Analytics and Computational Social Science
The MS in Data Analytics and Computational Social Science (DACSS) is an interdisciplinary program that trains you to use and communicate data effectively to support decision-making.
You’ll study computational methods such as text analysis, network analysis, geocoding and GIS, agent-based simulation, and machine learning for the social sciences. Electives in survey research, advanced statistics, and econometrics let you tailor the program to your career goals, while courses in city planning, international law, and media and culture deepen your understanding of economic, social, and political phenomena.
Study full-time or part-time, in person or online. Full-time students take at least three courses per term and finish in 18–24 months. Part-time students move at their own pace, working with our advisors to build a course pathway that fits their schedule.
This career-focused program is ideal if you’re seeking an analyst or consulting role in public policy, market research, public relations, corporate communications, population studies, or survey research, or if you’re pursuing a PhD or other advanced study.
Benefits list
Curriculum
The degree curriculum has been carefully designed to prepare students for the workforce and reflects current industry standards for data science professionals. The MS in DACSS requires satisfactory completion of 11 courses, totaling 32 credits.
Application and Admissions
Want to know more about applying to the DACSS program? Our graduate admissions page outlines admissions requirements and application deadlines, as well as providing answers to our most frequently asked questions about the admissions process.
Financial Arrangements
The MS program offers a superior education at a competitive rate. Our 10-month accelerated track allows students to spend fewer months in full-time graduate education and more rapidly join the workforce.
Featured classes
This course provides students with an introduction to the R programming language that will be used in all core courses and in many of the technical electives.
This course covers theory and methods of social network analysis, including data collection and management, network visualization and description; and statistical methods for analyzing relational data.
Students acquire foundational knowledge in Python and SQL, learn how to construct APIs, and delve into advanced topics such as optimization, cloud computing, analytics databases, and the construction of data pipelines.
Data Analytics and Computational Social Science
At UMass Amherst, you can undertake intensive courses in advanced data science taught from a social science perspective.