DS 623 Math & Statistics for Data Science
This course is designed to provide discrete mathematics concepts and applied statistics useful for data science with a statistical programming language. The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability, and statistics. This applied statistics covers topics ranging from syntax basics of the chosen statistical programming language, descriptive statistics, and data visualizations to inferential statistics and regressions. Students completing this course will have an understanding of building intuition and practical experience with applying mathematical and statistical concepts to data science.