PROBABILITY AND STATISTICS FOR DATA SCIENCE
An introduction to mathematical statistics and computation for applications to data science. Topics include probability, random variables expectation, sampling distributions, estimation, confidence intervals, hypothesis testing and regression. A weekly lab will cover the statistical package, R, and data projects. Cross-list: STAT 315 . Recommended Prerequisite(s): MATH 212 . Mutually Exclusive: Cannot register for DSCI 301 if student has credit for ECON 307 / STAT 310 / STAT 311 / STAT 312 .
Prerequisites & course information
Short Title: STATISTICS FOR DATA SCIENCE Department: Data Science Grade Mode: Standard Letter Course Type: Lecture/Laboratory Distribution Group: Distribution Group III Credit Hours: 4 Restrictions: Enrollment is limited to Undergraduate, Undergraduate Professional or Visiting Undergraduate level students. Course Level: Undergraduate Upper-Level Prerequisite(s): MATH 102 or MATH 106
Sources
- Course catalogue ↗2026–2027