STAT155: Methods in Data Science

Explores methods used in Data Science (DS), assuming a strong foundation mathematical and analytical methods relevant to DS. To gain a deeper understanding of the complexities and limitations of these methods, this class focuses on real-life datasets and on challenging assumptions. Students work on practical projects that require creative problem-solving and critical thinking skills. Class provides a valuable opportunity to bridge the gap between theoretical knowledge and real-world applications in the field of Data Science. Topics covered include exploratory data analysis, linear regression, support vector machine, cluster analysis, principal components analysis, and neural networks. Also offered as CRWN 155. Prerequisite(s): CRWN 85 and STAT 131, and CSE 30 or STAT 266A.

5 credits

Year	Fall	Winter	Spring	Summer
2024-25			Section 01 Marcela Alfaro Cordoba (macordob)

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