Program Overview

The Data Analytics and Computational Social Science certificate is a 3-course graduate certificate designed for working professionals, recent undergraduates, and current graduate students who want to build a solid foundation in statistical analysis using R and develop their ability to confidently understand and apply a range of social science research methods.

The graduate certificate gives students the opportunity to explore the field of Data Analytics and Computational Social Science without committing to a complete Master’s degree. Coursework will expose students to the core data management, research design, and quantitative analysis skills introduced as part of the DACSS core graduate curriculum.

All students enrolled in a UMass graduate degree program in the College of Social and Behavioral Sciences are eligible to complete the requirements of the graduate certificate while enrolled in their degree program. Additionally, non-degree students taking courses through UMass University Without Walls are also eligible to complete the certificate. Note that this certificate program is compatible with requirements for the Master’s degree, enabling online certificate recipients to transition into the graduate degree program through the normal application process.

Course Requirements

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 DACSS 601 or prior experience using R.)