Weekly Content

Week00: AI for coding

Using AI for code. May not be covered in this class, as it had often been already covered in coding classes.

Week01: LLM Review

What are LLMs, how is the magic happening. A non-technical brief intro. How to work with LLMs? Plus ideas on applications. Includes suggested readings, podcasts, and vids to listen to.

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Which AI? See my take on current models.

Week02: Data and code discovery and documentation with AI

Learn how to write a clear and professional code and data documentation. LLMs are great help once you know the basics.

Case study: World Values Survey

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Week 03: Writing Reports

You have your data and task, and need to write a short report. We compare different options with LLM, from one-shot prompt to iteration.

Case study: World Values Survey

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Week04: Agentic AI with Claude Code

From chat to terminal - introducing Claude Code for data analysis. Students learn to use agentic AI that works directly with files, generates data, and iterates on analysis.

Case study: Austrian Hotels

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Week05: Advanced CLI Workflows

Going deeper with CLI tools: custom skills, project-specific instructions (CLAUDE.md), git integration, and autonomous execution.

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Week06: From Data to Report

Download real CPS earnings data via CLI, contrast an undirected “vibe report” with a carefully directed economics-quality report. Iterative graph refinement, OLS regressions, and constrained PDF output.

Case study: US Earnings (CPS)

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Week07: Text as data 1 – intro lecture

No course of mine can escape football (soccer). Here we look at post-game interviews to learn basics of text analysis and apply LLMs in what they are best - context dependent learning. Two class series. First is more intro to natural language processing.

Case study: Football Manager Interviews

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Week08: Sentiment Analysis with AI

Second class, now we are in action. How does LLM compare to humans?

Case study: Football Manager Interviews

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Week09: AI as research companion: Control variables

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Week10: AI as research companion: Instrumental variables

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Capstone Project (3 sessions)

Three-session team project on manager changes in football. One big research question, messy real-world data, AI as your primary teammate. — Project description

Session 1: Data collection & description

Pick a league, collect and document 10+ seasons of match and manager-change data.

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Session 2: From text to expectations (APIs)

Scrape news sources and use an LLM API to score expectations around each manager change.

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Session 3: Difference-in-Differences analysis + final presentation

Run a DiD on your panel, explore heterogeneity (including by expectation score), and present to the class.

Content

--- title: "Weekly Content" --- ### Week00: AI for coding Using AI for code. May not be covered in this class, as it had often been already covered in coding classes. [Content](../week00) ## Week01: LLM Review What are LLMs, how is the magic happening. A non-technical brief intro. How to work with LLMs? Plus ideas on applications. Includes suggested readings, podcasts, and vids to listen to. [Content](../week01/) **Which AI?** See [my take on current models](../da-knowledge/which-ai.html). ## Week02: Data and code discovery and documentation with AI Learn how to write a clear and professional code and data documentation. LLMs are great help once you know the basics. Case study: [World Values Survey](../case-studies/VWS/) [Content](../week02/) ## Week 03: Writing Reports You have your data and task, and need to write a short report. We compare different options with LLM, from one-shot prompt to iteration. Case study: [World Values Survey](../case-studies/VWS/) [Content](../week03/) ## Week04: Agentic AI with Claude Code From chat to terminal - introducing Claude Code for data analysis. Students learn to use agentic AI that works directly with files, generates data, and iterates on analysis. Case study: [Austrian Hotels](../case-studies/austria-hotels/) [Content](../week04/) ## Week05: Advanced CLI Workflows Going deeper with CLI tools: custom skills, project-specific instructions (CLAUDE.md), git integration, and autonomous execution. [Content](../week05/) ## Week06: From Data to Report Download real CPS earnings data via CLI, contrast an undirected "vibe report" with a carefully directed economics-quality report. Iterative graph refinement, OLS regressions, and constrained PDF output. Case study: [US Earnings (CPS)](../case-studies/earnings/) [Content](../week06/) ## Week07: Text as data 1 -- intro lecture No course of mine can escape football (soccer). Here we look at post-game interviews to learn basics of text analysis and apply LLMs in what they are best - context dependent learning. Two class series. First is more intro to natural language processing. Case study: [Football Manager Interviews](../case-studies/interviews/) [Content](../week07/) ## Week08: Sentiment Analysis with AI Second class, now we are in action. How does LLM compare to humans? Case study: [Football Manager Interviews](../case-studies/interviews/) [Content](../week08/) ## Week09: AI as research companion: Control variables [Content](../week09/) ## Week10: AI as research companion: Instrumental variables [Content](../week10/) # Capstone Project (3 sessions) Three-session team project on manager changes in football. One big research question, messy real-world data, AI as your primary teammate. — [Project description](../capstone/) ## Session 1: Data collection & description Pick a league, collect and document 10+ seasons of match and manager-change data. [Content](../project01/) ## Session 2: From text to expectations (APIs) Scrape news sources and use an LLM API to score expectations around each manager change. [Content](../project02/) ## Session 3: Difference-in-Differences analysis + final presentation Run a DiD on your panel, explore heterogeneity (including by expectation score), and present to the class. [Content](../project03/)