Practical Data Science II

Description from DukeHub

Practical Data Science II is a flipped-classroom, exercise and project-focused course. Building on the computational thinking skills developed in Practical Data Science I, this course introduces students to a range of computational inquiry methods, including network analysis, geospatial analysis, and natural language processing (NLP). Throughout, the focus will be on developing hands-on experience implementing these methods with messy real-world data to ensure students are prepared to deploy these tools to answer the questions they care about. Requirements: Practical Data Science I, Intro Stats.

Skills

• data and statisticsdescription: "data science" and 4 more
• programmingAI review: "computational thinking skills"
• ai and machine learningAI review: "natural language processing (NLP)"

AI-resilient: 1 of 5 1/5

• ✓Yes: Physical, lab or field work · "hands-on"
• No: Live work with people
• No: Judgment someone is accountable for
• No: A craft you practice with your hands or body
• No: Small, in-person format

The checks lean on Microsoft Research, Working with AI (2025), which found generative AI applies least to physical work and hands-on work with people. A signal, not a promise. How the score works

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