CS 171 - Linear Algebra for Data Analysis

Credits: 5
Variable Credit Course: No

Lecture Hours: 55
Lab Hours: 0
Worksite/Clinical Hours: 0
Other Hours (LIA/Internships): 0

Course Description: This course uses a high-level programming language as a vehicle to discuss aspects of linear algebra that are important in data analytics. Develop an understanding of how to use linear algebra to solve problems. Topics include basic matrix operations, linear transformations, ranges, linear combinations and spans, systems of linear equations, symmetric matrices, inverses, determinants, triangular matrices, trace, eigenvalues and eigenvectors.

Prerequisite: CS 121 with grade C or higher or Dept. Chair permission.
Meets FQE Requirement: No
Integrative Experience Requirement: No

Student Learning Outcomes

Solve problems using concepts and methods of linear algebra including topics such as Gauss-Jordan elimination, vector spaces, eigenvalues, eigenvectors, vector spaces, linear transformations.
Apply linear algebra concepts to common engineering and mathematical problems.
Write a program using linear algebra to solve an appropriate data analytics problem.
Describe the relationship between computer-based activities and application of linear algebra concepts.
Identify possible uses of linear algebra in various career fields.

Course Contents

Concepts and methods of linear algebra including topics such as Gauss-Jordan elimination, vector spaces, eigenvalues, eigenvectors, vector spaces, linear transformations.
Linear algebra concepts to common engineering and mathematical problems.
A program using linear algebra to solve an appropriate data analytics problem.
The relationship between computer-based activities and application of linear algebra concepts.
Possible uses of linear algebra in various career fields.

Instructional Units: 5