This course will provide students with a strong foundation in mathematical tools relevant to data science. Selected topics from calculus, vector spaces, matrix algebra, numerical optimization, and probability theory will be covered. These tools will help students understand and solve data science problems and work with emerging methods and techniques in this rapidly growing field. A refresher in calculus will provide a foundation in mathematics necessary to understand data science concepts. Topics will include matrix algebra and vector spaces, which are essential for understanding mathematical models and statistical methods used in data science, and numerical mathematics and optimization, which are integral for understanding model training and efficiency. Additionally, skills in this area can help detect overfitting by providing a way to assess the quality of a models fit to data. Finally, instructional units on basic probability theory will lay the foundation for the subsequent classes on statistical models for data science
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