Introduction to Data Analytics and Data Science

Catalog description

An introduction to data analytics and builds upon the introduction to statistics course. This course provides students with an understanding of some concepts that underline data analysis and familiarize students with some more advanced statistical techniques and math skills for social scientists. The course starts with essential notions of probability theory and statistics such as descriptive statistics, probability distributions, Bayes’ Rule, and sampling distributions. It then introduces advanced concepts of quantitative analysis such as point estimation, confidence intervals, hypothesis testing, causal inference, statistical significance, and data mining. Next, the course covers techniques in modern data analysis focusing on bivariate and multivariate ordinary least squares regressions and data visualization. Also discussed is how to deal with different types of variables and data generating process. These concepts are illustrated with applications drawn from frontier research. Additionally, predictive methods and forecasting are covered. Finally, instructions are provided on the use of R language and opportunities for students to perform hands-on, self-directed empirical analysis. Prerequisite(s): SECR 7601 and SECR 7603 Lecture Hours: 3 Lab Hours: 0 Other Hours: 0 Contact Hours: 3 Grade Mode: Normal (A, B, C, D, F) Degree Restrictions: Doctor of Philosophy Schedule Type (Primary): Lecture Schedule Type (Additional): Seminar Small Group Click here for the Schedule of Classes.