The DACSS program regularly offers four core courses (required for MS in DACSS students) that provide students with a solid grounding in data collection, programming and data management, statistical data analysis, data visualization and communication, and effective evidence-based decision-making.
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 required in all advanced quantitative and data analysis courses.
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 602: Research Design
This course introduces students to the basic language of behavioral research, with an emphasis on designing valid social science research, including measurement reliability and validity, internal research design validity, and generalizability, or external research design validity. Students will become familiar with various techniques 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 provides a rigorous introduction to quantitative empirical research methods, designed for doctoral students in social science and master’s students with a focus on data analytics or computational social science. The material covered includes 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, students 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).
DACSS 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.