Instructor: Raquel Prado, E2-521 

Teaching Assistant: Chunyi Zhao.  

Course Description

This course presents tools for exploratory data analysis (EDA) and statistical modeling in R. Topics include: numerical and graphical methods for EDA, linear and logistic regression, ANOVA, PCA, and tools for acquiring and storing large data. No R knowledge is required. Enrollment is restricted to graduate students. 

Classroom, Lecture Time & Office Hours

Lectures: Tu-Th 8:00-9:35am Soc Sci 2 171 

Lecturer Office Hours (Tentative): Th 10-11am 

TA Office Hours:  Th 2-4pm BE 151. Please use https://czhao204.youcanbook.me/ to book 10 min sections as individual or group of 2 for the first hour. The second hour is open to all students. 

Textbook and other recommended books 

R by Example (2012) by Jim Albert and Maria Rizzo, Springer Use R! Series

Other recommended books:

Modern Applied Statistics with S (2002, Fourth Edition) by W.N. Venables and B.D. Ripley, Springer. 

R for Data Science (2017) by Garrett Grolemund and Hadley Wickham 

ggplot2 (2016) by Hadley Wickham, Springer Use R! Series

Modern Data Science with R (2017) by B.S. Baumer, D.T. Kaplan and N.J. Horton

Course evaluation: Homework Assignments (30%); Exam (35%); Final Project (35%)