Instructor: Raquel Prado, BE 365C
Teaching Assistant: Daniel Kirsner
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 1:30-3:05pm N. Sci Annex 103
Lecturer Office Hours (Tentative): Tu 10-11am and Tu 3:30-4:30pm
TA Office Hours: Wed and Thu 10-11am BE 312 C/D
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 (40%); Exam (35%); Final Project (25%)