Call Number | 14051 |
---|---|
Day & Time Location |
T 4:10pm-6:40pm To be announced |
Points | 3 |
Grading Mode | Standard |
Approvals Required | None |
Instructors | Galen A McKinley Tian Zheng - e-mail, homepage |
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 | 10 students (15 max) as of 4:05PM Saturday, December 21, 2024 |
Subject | Statistics |
Number | GR5243 |
Section | 003 |
Division | Interfaculty |
Section key | 20251STAT5243W003 |