M.S. DATA ANALYTICS &
COMPUTATIONAL SOCIAL SCIENCE
An interdisciplinary program, oriented towards students who are focused on pursuing or advancing careers that rely heavily on framing, developing, and presenting data for evidence-based decision-making and communication or regularly interacting with computer scientists and other technical experts in fields such as political analysis, marketing analysis, financial analysis, product or project management, demographic analysis, social media analysis, strategic communication, and reputation management.
A graduate of this program will have developed skills relating to data-driven decision-making, manipulating and analyzing large datasets, ethical data management, evaluative data interpretation, and clear communication of data results. Possible career trajectories include analyst and consulting positions in public policy, market research, public relations, corporate communications, population studies, and survey research.
The U.S. Federal Government's STEM OPT program will allow international students to work in the U.S. for up to 36 months after graduating from this program.
CURRICULUM
Year 1 | JKLU
Micro Economics
Credits - 4
Probability & Statistics
Univariate Calculus
Spreadsheet Applications
Credits- 2
Cognition & Critical Thinking
Credits- 2
This course will be introduced in Semester 1. It will help students build the foundational ability to clearly reason through problems and to present arguments in a logical, compelling way which has become a key skill for survival in today’s world.
Multivariate Calculus
Credits - 3
This course is an extension of univariate calculus to more than one variable, key difference being that more variables mean more geometric dimensions. This makes visualization of graphs both harder and more rewarding.
Topics include partial derivatives, double integrals, vector calculus, optimization of functions with two or more variables (e.g., maximization of profit by suitably choosing the amount of capital and labor).
Seminar on Macroeconomics
Credits - 2
Research Design
Credits- 3
This course will teach the ideas of hypothesis, measurement, survey design, behavioral and experimental research.
Academic Writing
Credits- 4
Academic writing is a critical skill in the success of one's graduate studies in policy and social sciences, and the professional life thereafter. This course will cover skills such as formulating a problem, doing a literature review using various tools (databases, citation software), structuring and presenting the writing in an appropriate format etc., all geared towards formulating a “Fellowship proposal” project.
Critical Thinking for Developing Perspectives
Credits- 2
Further building on the Cognition & Critical Thinking Course of Semester I, this course will be introduced in Semester II. It will enhance skills on formulating relevant and investigative questions, evaluating information and evidence for correctness, consistency, and relevance. Students will learn to recognize their own beliefs, biases, claims and assumptions in solving problems through a medium of case studies, group simulations, debates etc.
Linear Algebra
Computer Programming
Credits- 3
Year II | UMass Amherst
Fundamentals of Data Analytics
Introduction to Quantitative Analysis
Credits - 3
Data Communication and Visualization
Credits- 3
Research Design for Social Scientists
Credits- 3
Technical Electives (3 or more)
Students will be required to take a minimum of three courses (nine credits) of advanced technical training in special data analytic methods to ensure that all graduates have cutting edge training in at least one specialized data analytic method. Examples of courses include: survey research, empirical text analysis, advanced quantitative methods in anthropology, geospatial analysis, modeling emergence and social simulation, experimental economics, political experiments, special topics in forecasting, panel data econometrics, social and political network analysis, and applied time series econometrics.
Substantive Electives (upto 3)
Credits- 9+
Specialisation Tracks
In order to help guide students through coursework that best prepares them for their workforce requirements, degree faculty will develop several specialization tracks through required and optional coursework that reflect future career goals. Examples of specialization tracks include: Data Science (Technical); Population and Policy Analysis; Behavioral Analysis; Organizational and Market Analysis; Culture, Communication and Media Analysis; and Economic and Financial Analysis.
MEREDITH ROLFE

Program Director
“The MS in Data Analytics program at U Mass Amherst leverages the widely known reputation of UMass Amherst faculty in data analytics, computer science and the Computational Social Science Initiative (CSSI), and is geared towards recent college graduates as well as professionals seeking to advance careers”.