Course Director: Faculty
This one-semester course provides an introduction to statistical computing and modern data analysis using R and Python. Students will learn the complete data analysis workflow, including data import, cleaning, visualization, exploratory data analysis, statistical inference, and model building.
The first part of the course introduces fundamental statistical concepts, including probability, statistical distributions, estimation, confidence intervals, hypothesis testing, and methods for comparing categorical and continuous data, such as chi-square tests, t-tests, analysis of variance (ANOVA), and nonparametric methods. These methods are implemented through hands-on programming exercises in both R and Python.
The second part focuses on statistical modeling and computing, covering simple and multiple linear regression, model diagnostics, variable selection, prediction, and model interpretation. Throughout the course, students will develop practical programming skills for data analysis, visualization, reproducible research, and effective communication of statistical results.