Computational Mathematical Methods for Data Science

This course introduces the mathematical and computational foundations used in data science.

Course Overview

The course covers:

• Linear algebra for data representation
• Numerical methods and optimization
• Probability and statistics for data analysis
• Basic programming tools for scientific computing

Learning Objectives

By the end of the course, students will be able to:

• Apply mathematical methods to real data problems
• Implement computational solutions using programming tools
• Interpret results from numerical and statistical analysis

Suggested Topics

1. Vectors, matrices, and linear transformations
2. Numerical differentiation and integration
3. Least squares and optimization
4. Probability distributions and inference
5. Data visualization and scientific computing workflows

Next Steps

To continue exploring the course materials, return to the main course index.