Listed in: Mathematics and Statistics, as STAT-231
Brittney E. Bailey (Sections 01 and 02)
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.
Requisite:
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
Section 01
Tu 8:30 AM - 9:50 AM WEBS 102
Th 8:30 AM - 9:50 AM WEBS 102
Section 02
Tu 11:30 AM - 12:50 PM WEBS 102
Th 11:30 AM - 12:50 PM WEBS 102
| Section(s) | ISBN | Title | Publisher | Author(s) | Comment | Book Store | Price |
|---|---|---|---|---|---|---|---|
| All | 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/ | Amherst Books | TBD |
These books are available locally at Amherst Books.