Course Description
This course is for students to build up a solid mathematical foundation in order to thrive in the rapidly growing field of data science. Students will learn essential mathematical topics in data science, including linear algebra, matrix decomposition, probability, stochastic process, analysis, optimization, geometry, and algebraic topology. Prerequisite: Grade C or better in MAT 292 or MAT 296 and grade C or better in MAT 310.
Syllabus
Student Learning Outcomes, Goals, Objectives:
Students successfully completing this course will be able to:
- State and appropriately use mathematical language of the field in the areas of linear algebra, analytic geometry, matrix decompositions, vector calculus, probability and distributions, continuous optimization.
- State and explain main theorems of the areas outlined in SLO1.
- Apply theoretical knowledge to mathematical questions in the areas outlined in SLO 1.
- Apply theoretical knowledge to practical questions in areas outlined in SLO 1.
Course Grading Information:
| Activity/Performance Measure | Percentage/Points |
|---|---|
| Problem Sets | 50% |
| Micro-Project | 20% |
| Quizzes | 30% |
Grading Scale:
| Percentage | Letter Grade |
|---|---|
| 93% | A |
| 90% | A- |
| 87% | B+ |
| 83% | B |
| 80% | B- |
| 77% | C+ |
| 73% | C |
| 70% | C- |
| 67% | D+ |
| 63% | D |
| 60% | D- |
| 0% | F |
Attendance/Participation:
Students are encouraged to attend all classes. Attendance and participation on Fridays is required to receive the Discussion portion of the grade for Problem Sets.
Course Materials Purchased by the Students:
Mathematical Methods in Data Science by Sébastien Roch
Scholarly Perspectives
This course engages diverse scholarly perspectives to develop critical thinking, analysis, and debate and inclusion of a reading does not imply endorsement.