Data Analytics and Computation 603 - Intro 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).

Open to DACSS masters students and SBS PhD students only DACSS 601 This is a required core course for the DACSS Master's degree and graduate certificate.

This course assumes a working knowledge of R. If you do not have sufficient background in R (e.g., have not already taken and passed DACSS 601 Data Science Fundamentals or an equivalent), please contact the instructor to discuss strategies for preparing for this course.

Open to DACSS Master's and SBS PhD students; others by instructor permission. Please contact the instructor and DACSS@UMass.edu if you would like to enroll in this class but are not in one of these groups.