Data Science Course Listings

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234.       Foundations of Data Science.

An introduction to fundamental data science concepts using modern statistical programming languages and software. The course assumes no prior knowledge of programming but a familiarity with basic statistics is required. The course focuses on building essential data science skills such as data manipulation and visualization, basics of programming, string manipulation, and modern data sources (such as web and databases). Emphasis will be placed on building skills applicable to large scale projects. Students may receive credit for only one of DATA 234, STAT 234, STAT 3007, or  STAT 201. This course fulfills the Computing Requirement of the Statistics Major. Prerequisite: STAT-113 or ECON-200.

334.       Data Visualization.

A continuation of DATA/STAT-234 with a focus on deepening data visualization skills with heavy usage of a modern statistical programming language. Class discussion will include how to select an appropriate visualization and what characteristics make a visualization "good," with an emphasis on building these skills to apply them to projects. Topics may include statistical model visualization, mapping data, visualizing text data, expressing uncertainty appropriately, and interactive visualizations. Prerequisites: STAT-213 and DATA/STAT-234 or permission of instructor.

345.       Database Systems.

An introduction to fundamental data science concepts using modern statistical programming languages and software. The course assumes no prior knowledge of programming but a familiarity with basic statistics is required. The course focuses on building essential data science skills such as data manipulation and visualization, basics of programming, string manipulation, and modern data sources (such as web and databases). Emphasis will be placed on building skills applicable to large scale projects. Prerequisite: CS 256, with either CS 220 or DATA 234 strongly recommended. Offered in fall semesters. Also offered as CS 345.

352.       Statistical & Machine Learning

Introduces techniques for developing advanced models from datasets, for the purposes of better understanding the data and making predictions about future data. Techniques include linear and regularized regression, nearest neighbor classification, support vector machines, decision tree ensembles, and neural networks. Examines real-world applications, both successes and failures, the latter of which often involve data with embedded biases. Students will develop both technical and ethical competence in using some of the most powerful computational tools in data science. Prerequisites: STAT 213,  CS 219, and  MATH 217. Offered in spring semesters. Also offered as CS 352 and STAT 352.

289, 389.       Independent Study.

Permission required.

450.       SYE Seminar

Permission required.

489.       Independent SYE

Permission required.

498.       Honors SYE Permission required.