Master of Science in Data Analytics and Computational Social Science

2 Years On Campus Masters Program

University of Massachusetts Amherst

Program Overview

The Master of Science in Data Analytics and Computational Social Science (MS DACSS) at the University of Massachusetts Amherst (UMass Amherst) is an interdisciplinary graduate program designed to develop advanced expertise in data analytics, computational methods, statistical analysis, and social science research. Offered through the School of Public Policy within the College of Social and Behavioral Sciences, the program prepares students to collect, analyze, interpret, and communicate complex data to support evidence-based decision-making across government, business, public policy, research, and other professional sectors.

The program is STEM-designated and requires the completion of 34 graduate credits across 12 courses. Students can pursue the degree through full-time or part-time study, with options for both on-campus and fully online learning. Full-time students generally complete the program in 18–24 months, while accelerated pathways may allow completion in approximately 10 months, depending on the course schedule and study plan.

Curriculum Structure

Year 1

Students begin by developing a strong foundation in data science, research design, quantitative analysis, programming, and computational methods. The core curriculum introduces essential analytical techniques and prepares students to work with complex datasets and communicate data-driven findings effectively.

Example coursework includes:

Mathematics for Applied Data Science (DACSS 600)
Data Science Fundamentals (DACSS 601)
Research Design for Social Scientists (DACSS 602)
Introduction to Quantitative Analysis (DACSS 603)
Ethics in Data Science & AI (DACSS 621)
Polishing Your Professional Presence (DACSS 691P)

Students also develop practical skills in R programming, Python, SQL, statistical analysis, data management, research methodologies, and data visualization. They begin exploring technical electives in areas such as social network analysis, computational text analysis, and geospatial data analysis.

Year 2

During the second year, students advance their analytical knowledge through specialized technical electives and substantive social science courses. They select subjects aligned with their professional interests, developing expertise in advanced statistical techniques, machine learning, data interpretation, and computational research.

Example coursework includes:

Advanced Statistical Methods (DACSS 713)
Machine Learning for Social Scientists (DACSS 756)
Advanced Data-Driven Storytelling (DACSS 684)
Research Lab (DACSS 698R)
Social Network Analysis
Geospatial Analysis and Geographic Information Systems (GIS)
Computational Text Analysis
Survey Research and Econometrics

Students complete advanced technical and substantive electives and apply their knowledge through a capstone or research-lab experience. They also gain opportunities to explore subjects such as public opinion, digital labor, media and politics, and the social implications of algorithms.

Focus Areas

Data Analytics; Computational Social Science; Data Science; Artificial Intelligence; Machine Learning; Statistical Analysis; Social Network Analysis; Computational Text Analysis; Geographic Information Systems (GIS); Data Engineering; Econometrics; Survey Research; Public Policy Analysis; Data Visualization; Research Design; Data Ethics; Predictive Analytics.

Learning Outcomes

Develop expertise in data collection and management, R and Python programming, SQL, quantitative analysis, statistical modeling, machine learning, computational text analysis, social network analysis, geospatial analysis, data visualization, research design, predictive analytics, data interpretation, evidence-based decision-making, and communication of complex data insights.

Professional Alignment (Accreditation)

The program prepares students for professional careers in data analytics, public policy, market research, consulting, corporate communications, population studies, and social science research. Its interdisciplinary curriculum combines advanced computational techniques with social science research methods, enabling graduates to apply data-driven approaches to real-world economic, political, organizational, and social challenges.

Students gain professional experience through:

Practical Data Analytics: Training in R, Python, SQL, statistical modeling, data management, and computational analysis.
Research and Analytical Projects: Opportunities to apply advanced computational methods to real-world research questions and datasets.
Professional Development: Career preparation through professional development seminars, communication training, and networking opportunities.
Interdisciplinary Learning: Opportunities to explore electives across public policy, computer science, business, regional planning, and other social science disciplines.
Career and Academic Advising: Dedicated academic and career guidance, professional networking, alumni connections, and internship opportunities.
Advanced Research Preparation: Development of research and analytical competencies for students interested in pursuing PhD programs and other advanced graduate studies.

The program is STEM-designated, reflecting its emphasis on scientific, technical, quantitative, and computational approaches to data analysis.

Reputation (Employability Rankings)

The University of Massachusetts Amherst offers the MS in DACSS through its School of Public Policy, combining computational data science with interdisciplinary social science education. The program emphasizes industry-relevant analytical skills and prepares graduates to address the growing demand for professionals who can interpret complex datasets and communicate meaningful insights.

Students benefit from:

An interdisciplinary curriculum integrating computer science, statistics, and social science.
Flexible on-campus and online learning options.
Faculty guidance and dedicated academic and career advising.
Professional development seminars and alumni networking opportunities.
Research and internship opportunities across multiple disciplines.
Exposure to practical applications of data science in government, business, public policy, and research.

Graduates can pursue careers as Data Analysts, Data Scientists, Business Intelligence Analysts, Market Research Analysts, Policy Analysts, Research Analysts, Statistical Analysts, GIS Specialists, Data Consultants, Computational Social Science Researchers, Survey Researchers, and Data Analytics Specialists. They can also explore opportunities in government agencies, consulting firms, research institutions, public policy organizations, technology companies, and private-sector businesses.

Experiential Learning (Research, Projects, Internships etc.)

At the University of Massachusetts Amherst, the Master of Science in Data Analytics and Computational Social Science program is designed to provide students with hands-on experience that is crucial for success in today’s data-driven world. Students engage in practical learning through various projects, internships, and access to state-of-the-art facilities and tools. This experiential learning approach not only enhances theoretical knowledge but also equips students with the skills needed to tackle real-world challenges.

Here are some key aspects of the experiential learning opportunities available in this program:

- Collaborative Projects: Students work on group projects that simulate real-world data analytics scenarios, fostering teamwork and problem-solving skills.
- Internships: The program encourages internships with local businesses and organizations, allowing students to apply their skills in professional settings and gain valuable industry experience.
- Software and Tools: Students gain proficiency in industry-standard software such as R, Python, SQL, and Tableau, which are essential for data analysis and visualization.
- Research Laboratories: Access to dedicated research labs enables students to conduct experiments and projects that contribute to the field of data analytics and social science.
- Field Trips: The program includes field trips to tech companies and research institutions, providing insights into the latest trends and practices in data analytics.
- Libraries and Resources: The UMass Amherst libraries offer extensive resources, including databases and journals, that support research and learning in data analytics.
- Institute for Social Science Research: Students can collaborate with the Institute for Social Science Research, which provides additional opportunities for research and networking.

Progression & Future Opportunities

The Master of Science in Data Analytics and Computational Social Science at the University of Massachusetts Amherst equips graduates with the skills to thrive in a rapidly evolving job market. Alumni often find themselves in roles such as Data Analyst, Social Data Scientist, and Business Intelligence Consultant, leveraging their expertise to drive data-informed decision-making across various sectors. With a strong foundation in both data analytics and social science, graduates are well-prepared for diverse career paths:

- University Career Services: UMass Amherst offers robust career support, including resume workshops, interview preparation, and networking events tailored to connect students with industry professionals.
- Employment Statistics: The program boasts an impressive employment rate, with over 90% of graduates securing jobs within six months of graduation, often with starting salaries ranging from $70,000 to $90,000.
- University–Industry Partnerships: UMass has established partnerships with leading companies in tech and social research, providing students with internship opportunities and real-world projects that enhance their learning experience.
- Long-term Accreditation Value: The program is accredited by relevant professional bodies, ensuring that the education you receive meets high standards and is recognized by employers globally.
- Graduation Outcomes: Graduates have successfully transitioned into influential roles in various sectors, including technology, healthcare, and government, demonstrating the versatility of their degree.

Further Academic Progression: After completing the Master of Science in Data Analytics and Computational Social Science, students have the option to pursue a Ph.D. in related fields, such as Data Science, Sociology, or Public Policy. This advanced study can open doors to academic positions, research roles, and higher-level consultancy opportunities, allowing you to further deepen your expertise and impact in the field.