Courses

Core Courses

DACSS 601 Data Science Fundamentals
This course provides students with an introduction to the R programming language that will be used in all core courses and many of the technical electives. There is a growing demand for students with a background in generalist data science languages such as R, as opposed to more limited software such as Excel or statistics packages such as SPSS or Stata. The course 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. (Prior exposure to introductory-level college statistics recommended but not required.)

DACSS 602 Research Design
This course introduces students to the basic language of behavioral research, with an emphasis on designing valid social science research. An emphasis is placed on measurement reliability and validity, internal research design validity, and generalizability, or external research design validity. Students will become familiar with a range of techniques used to gather social science data and measure and analyze different aspects of individual and social behavior, including experiments, surveys, semi-structured interviews, focus groups, coding of online and archival text sources, and social network analysis. Students will learn to identify threats to research validity and reliability associated with these different research approaches. All data analysis will be conducted in R. Students will also use Qualtrics and mTurk to collect data. This course is a required core course for the graduate certificate and the Master's degree in Data Analytics and Computational Social Science (DACSS).

DACSS 603 Introduction to Quantitative Analysis
This course serves as a rigorous introduction to quantitative empirical research methods, designed for doctoral students in social science and master's students with a data analytics or computational social science focus. The material covered will include a brief introduction to the problem of causality, followed by modules on (1) measurement, (2) prediction, (3) exploratory data analysis (discovery), (4) probability (including distributions of random variables), and (5) uncertainty (including estimation theory, confidence intervals, hypothesis testing, power). Along the way, we will encounter linear regression and classification as tools of descriptive data summary, prediction and inference, and as part of a broader strategy of causal analysis. Simulations and data analysis will be conducted in the R statistical environment. This course is a required core course for the graduate certificate and the Master's degree in Data Analytics and Computational Social Science (DACSS). (Requires prior experience using R.)

604 Advanced Data-driven Storytelling
How can social scientists convey data through narrative and reports geared toward general audiences or specific stakeholders? How can they convey those data through visuals geared toward non-scientists? This hands-on course provides students with the knowledge and skills needed to generate strong, data-driven communication.

Technical Courses

Faculty in departments across the College of Social and Behavioral Sciences and other UMass colleges offer a wide range of advanced research methodology courses with a grounding in or application to the social sciences. Students enrolled in M.S. DACSS are required to take a minimum of three courses (nine credits) of advanced technical training in methods of data collection and analysis to ensure that all graduates have cutting edge training in multiple methods. At least one course from each of the following categories is available on-campus and through UWW each year.

Network Analysis
Survey Research
Text and Content Analysis
Data Communication and Visualization
Statistics
Experiments
GIS & Spatial Analysis
Formal Models
Econometrics
Time Series

Substantive Courses

The DACSS program isn't a traditional data science program that you might find in a computer science department - the program is firmly grounded in social science. We believe that students need to also be familiar with what we know (and don't know) about substantive economic, social, and political phenomena.

DACSS faculty offer a wide range of courses that provide evidence-based answers to questions such as: How do people form opinions? How do judges make decisions? How do NGOs affect the policy process? How can policy makers best encourage economic development? How do media outlets choose to selectively allocate attention and coverage? How does a change in one part of the economic system affect other economic outcomes?

Courses from each of the following categories are available on-campus and through UWW each year.

Psychology and Public Opinion
Regional Planning and Development
Global Studies
Media and Communication
Technology and Society
Policy, Law, and Organizations
Economics