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 students. This course will fulfill a core course requirement for the MS or graduate certificate in Data Analytics and Computational Social Science. In order to be successful in this course, it is strongly recommended that students have completed DACSS 601 or otherwise gained extensive experience using R, prior to taking this course.