Master's of Science in Data Analytics and Computational Social Science (DACSS) Curriculum
The Master's of Science degree in Data Analytics and Computational Social Science (MS in DACSS) 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 degree requires satisfactory completion of 12 courses, totaling 34 credits.
Degree requirements are the same for all three MS in DACSS modalities (4+1 accelerated, in-person/hybrid, or online). Review the suggested timelines for all three MS in DACSS programs: DACSS Advising Form & Suggested Timelines
Students who began the DACSS program prior to Summer 2026, please see your degree requirements on the Academic and Career Advising page.
DACSS Curriculum
Core courses include one preparatory course, three foundational courses, one advanced methods course, two professional development courses and a capstone research project course.
DACSS students will develop advanced technical skills through taking our technical elective courses, and we also encourage students to deepen their subject matter expertise and content knowledge through optional substantive elective courses taken across campus.
- One preparatory course, DACSS 600 Essential Math for Applied Data Science, which sets students up for success by reviewing key algebraic skills and providing a gentle background in probability, matrix algebra, and basic calculus concepts. Students with sufficient mathematical preparation may request to replace this with an elective of their choice.
- Three foundational core courses, DACSS 601, 602, and 603, which cover essential data science skills in programming, research design, and statistical reasoning, ensuring that students have a shared knowledge base and are set up for success in their chosen advanced core course and technical electives.
- DACSS 601: Data Science Fundamentals
This course provides students with an introduction to the R programming language. Sample Syllabus - DACSS 602: Research Design
This course introduces students to the basic language of behavioral research. Sample Syllabus - DACSS 603: Introduction to Quantitative Analysis
This course provides a rigorous introduction to quantitative empirical research methods. Sample Syllabus
- DACSS 601: Data Science Fundamentals
- One advanced core course provides students with cutting-edge training in advanced analytical methods, either through DACSS 713 Advanced Statistical Methods or DACSS 756 Machine Learning for Social Sciences.
- Two professional development seminars, DACSS 691P Polishing your Professional Presence (2 credits) & DACSS 621 Ethics of Data Science and AI (2 credits).
- One capstone course, either DACSS 684 Advanced Data-Driven Storytelling, an online course that provides students with an opportunity to sharpen their skills in providing clear, professional, data-driven communication, or DACSS 698R Practicum - Research Lab, an in-person collaborative course with possible faculty-mentored, client-based, and/or team projects. Please note that DACSS 684 and DACSS 698R are only offered in fall and spring semesters.
Two technical electives, of which at least one must be from our advanced technical list, allowing students to build advanced, specialized skills appropriate for their career path.
Below is the list of advanced technical courses:
- DACSS 690AB: Agent Based Modeling for Social Complexity Research
- DACSS 690D: Spatial Data Analysis
- DACSS 690E: Experiments for the Social Sciences
- DACSS 695N: Social Network Analysis
- DACSS 713: Advanced Statistical Methods
- DACSS 756: Machine Learning for the Social Sciences
- DACSS 758: Text as Data
- DACSS 790C: Causal Inference
- DACSS 790D Temporal Dynamics
- DACSS 790N Network Inference
DACSS 790T: Large Language Models
Other technical courses could include the following:
- DACSS 585: Intro to GIS
- DACSS 611: Intro to Python for Data Science
- DACSS 690C: Computational Social Science Methods
- DACSS 690F: Building Data Dashboards
- DACSS 690R: Data Preprocessing
- DACSS 690V: Data Visualization
- DACSS 695SR: Survey Research Methods
Two additional technical or substantive electives that may be taken within or outside the DACSS program that allow students to further their technical skills or substantive knowledge as appropriate for their professional goals.
Departments in which students often take classes are: Public Policy, Biostatistics, Computer Science, Business Management, Regional Planning, and Education. However, you are not limited to these options and can explore classes from any department on the UMass campus.
Here are some examples of courses that have already been pre-approved as substantive elective courses:
- PUBPOL 540 Internet Governance & Info Policy
- PUBPOL 632 Public Budgeting & Finance
- PUBPOL 690F Financial Management for Nonprofits and State and Local Governments
- PUBPOL 621 Using the Past to Create Effective Policy
- PUBPOL 590P Tech Policy & Innovation to Serve the Common Good
- PUBPOL 597G The Future of Government
- PUBPOL 690G Technology Law and Policy Governance Trends
- PUBPOL 690GE Governing the Energy Transition: Policy, Power & Comparative Methods
- PUBPOL 615 Environmental Economics and Policy Analysis
- PUBPOL 627 Fixing Social Media
- PUBPOL 651 Social Inequalities, Technology & Public Policy
- PUBPOL 690T Technology Design via a Public Interest Technology Values Lens