DATA 205 - Math for Data Science 2

Units: 3
Introduces numerical linear algebra through applications in data science. Discusses matrices and their decomposition techniques, solving systems of linear equations, and eigenvector decomposition. The course will also cover advanced topics such as principal component analysis and support vector machines.

Prerequisites: MATH 105
Possible Instructional Methods: On-ground.
Grading: A-F grading only.
Cross-listed: MATH 205
Course Typically Offered: Spring ONLY

Student Learning Outcomes - Upon successful completion of this course students will be able to:

Apply concepts from numerical linear algebra to solve problems in data
science.
Implement techniques from numerical linear algebra using appropriate technology and communicate solutions effectively.
Use matrix decomposition techniques and tools like principal component analysis and support vector machines to solve linear systems and real world data problems.