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PPOL 564 - Data Science I: Foundations |

This first course in the core data science sequence introduces students to the programming and mathematical concepts that underpin statistical learning. The aim of the course is to provide students with the foundations necessary to grasp the concepts and algorithms encountered in Data Science II and III. Students will cover probability theory with an emphasis on simulation, Bayes rule, and MCMC sampling; linear algebra with a focus on data decompositions and dimension reduction; and multivariate calculus with an emphasis on optimization algorithms, specifically gradient descent. Throughout the course, students will be introduced to the fundamentals of programming in Python and will learn about data structures, data manipulation, and basic data management. Students will work in Jupyter notebooks and use Git/GitHub to submit coding assignments, developing literate programming and reproducible research skills they will use throughout the program. 3.000 Credit hours 3.000 Lecture hours 0.000 Lab hours Levels: MN or MC Graduate, Juris Doctor Schedule Types: Lecture, Seminar Public Policy Department Restrictions: Must be enrolled in one of the following Levels: Juris Doctor MN or MC Graduate Must be enrolled in one of the following Majors: Data Science for Public Policy |

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