DATA 201 - Intermediate Data Science

FC NSMA QFR

4 credits
An introduction to the principles and practices of crafting statistical learning models and informative/effective data visualizations that together can summarize and describe patterns in potentially large and complex datasets. Supervised and unsupervised statistical learning approaches will be covered from an applied perspective, including: regularization, tree-based methods, and clustering. Tools for data manipulation (e.g., merging data from multiple sources; cleaning, filtering, and transforming data) will also be covered.

Prerequisites: DATA 101 and (DATA 113 or DATA 205).