The Program
The Advanced Data Analytics and Computational Social Science certificate is a 5-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 Advanced Certificate extends the basic certificate by providing students with additional training in one or two additional social science methods, such as text analysis, network analysis, survey design and analysis, or machine learning. Students without an existing background in social science are strongly encouraged to take a substantive social science course.
This Advanced Data Analytics and Computational Social Science 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.
Any non-degree student taking courses through UMass University Without Walls are eligible to complete the Advanced Certificate. In addition, students enrolled in a UMass graduate degree program in the College of Social and Behavioral Sciences are eligible to complete the requirements of the Advanced Certificate while enrolled in their degree program.
This certificate is a terminal certificate for UWW/CPE non-degree students not interested in a full master’s program, and a transitional certificate for students who have not decided whether to pursue a full online master. It is compatible with requirements for the DACSS master’s degree, enabling online certificate recipients to transition into the master’s degree program through the normal application process.
Note that students may pursue either the 3-course basic DACSS certificate or the 5-course Advanced DACSS certificate but may not be awarded both certificates. If you have completed the basic certificate and then continue on to pursue the Advanced Certificate, only the Advanced Certificate will appear on your transcript.
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 for Social Scientists
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.)
Technical Elective: Choose one or two technical courses such as survey research, text as data, advanced quantitative methods in anthropology, geospatial analysis, modeling emergence and social simulation, experimental economics, panel data econometrics, social and political network analysis, and applied time series econometrics.
Substantive Elective: Choose no more than one course featuring a substantive social science topic. Any graduate courses offered by a department in the College of Social and Behavioral Sciences (SBS) can be used to fulfill this requirement with the consent of the instructor and approval of the DACSS Program Director.