| Call Number | 13757 |
|---|---|
| Day & Time Location |
T 4:10pm-6:40pm 702 Hamilton Hall |
| Points | 3 |
| Grading Mode | Standard |
| Approvals Required | None |
| Instructor | Bianca Dumitrascu |
| Type | LECTURE |
| Method of Instruction | In-Person |
| Course Description | Prerequisites: Pre-requisite for this course includes working knowledge in Statistics and Probability, data mining, statistical modeling and machine learning. Prior programming experience in R or Python is required. This course will incorporate knowledge and skills covered in a statistical curriculum with topics and projects in data science. Programming will be covered using existing tools in R. Computing best practices will be taught using test-driven development, version control, and collaboration. Students finish the class with a portfolio of projects, and deeper understanding of several core statistical/machine-learning algorithms. Short project cycles throughout the semester provide students extensive hands-on experience with various data-driven applications. |
| Web Site | Vergil |
| Department | Statistics |
| Enrollment | 16 students (25 max) as of 11:06AM Saturday, November 1, 2025 |
| Subject | Statistics |
| Number | GU4243 |
| Section | 001 |
| Division | Interfaculty |
| Open To | Barnard College, Columbia College, Engineering:Undergraduate, Engineering:Graduate, GSAS, General Studies, Professional Studies |
| Section key | 20253STAT4243W001 |