Spring 2024

Data Science

Listed in: Mathematics and Statistics, as STAT-231


Nicholas J. Horton (Section 02)
Amy S. Wagaman (Section 01)


Computational data analysis is an essential part of modern statistics and data science. This course provides a practical foundation for students to think with data by participating in the entire data analysis cycle. Students will generate statistical questions and then address them through data acquisition, cleaning, transforming, modeling, and interpretation. This course will introduce students to tools for data management, wrangling, and databases that are common in data science and will apply those tools to real-world applications. Students will undertake practical analyses of large, complex, and messy data sets leveraging modern computing tools.


STAT 111 or STAT 135 or STAT136 and COSC 111 or consent of the instructor. Limited to 24 students. Fall and Spring semesters. The Department. 

How to handle overenrollment: For the Fall, priority for rising sophomores and Statistics majors. For the Spring, priority for sophomores and Statistics majors.

Students who enroll in this course will likely encounter and be expected to engage in the following intellectual skills, modes of learning, and assessment: quantitative work, problem sets, projects, group work, use of computational software, may include quizzes or exams

STAT 231 - LEC

Section 01
M 8:30 AM - 9:50 AM WEBS 102
W 8:30 AM - 9:50 AM WEBS 102

Section 02
Tu 1:00 PM - 2:20 PM WEBS 102
Th 1:00 PM - 2:20 PM WEBS 102

Section(s) ISBN Title Publisher Author(s) Comment Book Store Price
01 Modern Data Science with R, 2nd Edition Boca Raton, FL: CRC Press, 2021 Baumer, Benjamin S., Daniel T. Kaplan, and Nicholas J. Horton Book available for free online at https://mdsr-book.github.io/mdsr2e/ TBD


Other years: Offered in Fall 2022, Spring 2023, Fall 2023, Spring 2024, Fall 2024