| Call Number | 15148 |
|---|---|
| Day & Time Location |
S 11:00am-1:30pm 207 Mathematics Building |
| Points | 3 |
| Grading Mode | Standard |
| Approvals Required | None |
| Instructor | Franz Rembart |
| Type | LECTURE |
| Method of Instruction | In-Person |
| Course Description | Prerequisites: (STAT GR5701) working knowledge of calculus and linear algebra (vectors and matrices), STAT GR5701 or equivalent, and familiarity with a programming language (e.g. R, Python) for statistical data analysis. In this course, we will systematically cover fundamentals of statistical inference and modeling, with special attention to models and methods that address practical data issues. The course will be focused on inference and modeling approaches such as the EM algorithm, MCMC methods and Bayesian modeling, linear regression models, generalized linear regression models, nonparametric regressions, and statistical computing. In addition, the course will provide introduction to statistical methods and modeling that addresses various practical issues such as design of experiments, analysis of time-dependent data, missing values, etc. Throughpout the course, real-data examples will be used in lecture discussion and homework problems. This course lays the statistical foundation for inference and modeling using data, preparing the MS in Data Science students, for other courses in machine learning, data mining and visualization. |
| Web Site | Vergil |
| Department | Statistics |
| Enrollment | 29 students (125 max) as of 4:06PM Thursday, April 18, 2024 |
| Subject | Statistics |
| Number | GR5703 |
| Section | 002 |
| Division | Interfaculty |
| Campus | Morningside |
| Note | DSI students only |
| Section key | 20231STAT5703W002 |