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 An introductory course to provide conceptual foundation for economic analysis and thinking. Topics Covered: Gains from Trade; Supply, Demand and Equilibrium; Elasticity & its Applications; Supply, Demand and Government Policies; Costs; Perfect Competition; Monopoly; Pricing and market segmentation; Oligopoly, Game Theory; Market Failures.




Probability & Statistics


Credits - 4 This course will introduce various statistical topics such as probability, descriptive statistics, statistical inference, sampling distributions, point and interval estimates, regression analysis with applications drawn from diversified areas including economics, business, public policy and governance, health etc. Statistical computing includes calculations using the Microsoft Excel.




Univariate Calculus


Credits - 2 This course is aimed to learn and understand the fundamental concepts of functions, Differentiation and integration, and apply these concepts in real life problems of rate of change, approximation and maxima-minima.




Spreadsheet Applications


Credits- 2 The course will focus on applications and analysis with spreadsheets.




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 This Seminar will provide an overview of selected macroeconomic issues such as employment, interest rates, inflation, monetary and fiscal policies, and international economic issues.




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


This course introduces matrix theory, basic Linear Algebra Principles and Linear Programming Problem. Students are also expected to gain an appreciation for the applications of linear algebra and LPP to area such as economics, social sciences, business, public policy and governance, health etc.




Computer Programming


Credits- 3 This course will cover pseudocode, basic programming logic, and data structures.





Year II | UMass Amherst

Fundamentals of Data Analytics


Credits- 3 This course will introduce fundamental data science concepts and the world of big data, while continuing to help students develop their programming skills in R. It will also provide students with a solid grounding in general data management and data wrangling skills that are required in all advanced quantitative and data analysis courses.




Introduction to Quantitative Analysis


Credits - 3 This course will provide students with an introduction to quantitative analysis in the social science, often referred to as econometrics or statistical analysis. The course will include a brief review of college-level statistics (required for all students prior to degree matriculation), and will then introduce students to stastical techniques including ordinary least squares (OLS), limited dependent variable analysis, and other regression analysis models. There will be two tracks through this requirement, one of which will more heavily emphasize the computational and interpretive aspects of quantitative analysis, while the other will require a stronger mathematical background and is a prerequisite for taking advanced econometrics courses in Resource Economics.




Data Communication and Visualization


Credits- 3 This course will provide students with hands-on experience writing about and visualizing a range of data types with different communication goals. Data Visualization components will include the theory/concepts of visualization and hands-on work with ggplot2 package in R. The course is expected to be organized as a workshop type experience and may include one or more live client project experiences in the future.




Research Design for Social Scientists


Credits- 3 This course will provide an introduction to fundamentals of behavioral research including hypothesis testing, measurement, internal and external validation, along with an introduction to a range of data collection and analysis methods used in social research (e.g., experiments, surveys, text analysis, econometrics). Students will learn how to design research that can address specific issues encountered in business and policy settings, and how to effectively use data analysis to address those questions and support effective managerial decision-making.




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+ Students may take up to three courses that provide substantive background in a range of topics (such as health, race, inequality, social media, and/or immigration) and analytical approaches (such as public policy, public opinion, organizational theory, cultural theory, and/or economics). Specific courses will be identified as part of pre-defined specialization tracks and be listed as permanently approved electives for this degree. Students may also petition for additional courses to fulfill the substantive elective requirements. Examples of substantive courses that may interest degree students include: policy evaluation, demography, networks and health, political behavior, and industrial organization.




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”.

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