Data Science I: Statistical Foundations
QAC 300
Fall 2025
| Section:
01
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The course bridges classical statistical theory with modern data science applications, providing students with rigorous mathematical foundations and practical computational skills. Topics progress from probability theory and hypothesis testing to predictive modeling, regularization, and overview of machine learning methods. We emphasize the transition from traditional inference to prediction-oriented data science, covering bootstrap methods, robust inference, linear and logistic regression, model selection, and ensemble methods. Ethical considerations in algorithmic bias and responsible AI are integrated throughout. Extensive hands-on work includes computational assignments with emphasis on reproducible research practices. |
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| Credit: 1 |
Gen Ed Area Dept:
SBS QAC |
| Course Format: Lecture / Discussion | Grading Mode: Graded |
| Level: UGRD |
Prerequisites: QAC201 OR QAC201Z OR QAC211 OR BIOL242 OR MATH132 OR PSYC200 |
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Fulfills a Requirement for: (Applied Data Science Certificate)(Data Analysis Minor) |
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Past Enrollment Probability: Not Available |
| SECTION 01 | | Special Attributes: CQC |
Major Readings: Wesleyan RJ Julia Bookstore
Book: James, Witten, Hastie & Tibshirani (2021) An Introduction to Statistical Learning
and primary source journal articles
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Examinations and Assignments:
lab assignments, quizzes, two tests and a final exam |
Additional Requirements and/or Comments:
Students must have a basic programming background. Statistical computing work using R on student laptops or lab computers |
| Instructor(s): Gooyabadi,Maryam Times: .M.W... 02:50PM-04:10PM; Location: SCIE103; |
| Total Enrollment Limit: 12 | | SR major: 0 | JR major: 0 | | |
| Seats Available: 5 | GRAD: X | SR non-major: 3 | JR non-major: 5 | SO: 4 | FR: X |
| Drop/Add Enrollment Requests | | | | | |
| Total Submitted Requests: 0 | 1st Ranked: 0 | 2nd Ranked: 0 | 3rd Ranked: 0 | 4th Ranked: 0 | Unranked: 0 |
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