Data Science II: App Stat Lng

3 Credits · Level: Graduate, Undergraduate

This second course in the core data science sequence offers students an applied understanding of three key data science skills: data collection, data wrangling, and machine/statistical learning. Students will learn to gather raw data (using web scraping techniques and APIs); clean, structure, and manipulate data in a variety of formats; effectively explore and visualize data; and analyze datasets using a variety of machine learning models including regression, naive Bayes, K-nearest neighbors, decision trees and random forests, and support vector machines. Throughout the course, emphasis will be placed on effective visualization, model refinement and validation, and ethics. Students will engage with a number of policy-relevant data case studies throughout the course and will work on a policy-focused data science project.