| Call Number | 13781 | 
|---|---|
| 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 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 | 20 students (50 max) as of 9:07PM Monday, November 3, 2025 | 
| Subject | Statistics | 
| Number | GR5243 | 
| Section | 001 | 
| Division | Interfaculty | 
| Open To | GSAS | 
| Note | STAT MA Students only. | 
| Section key | 20253STAT5243W001 |