Course modules index
This folder contains sixteen standalone teaching modules built on ALT/FNDATA's alternative data platform. Each module is a complete, drop-in session: summary, objectives, prerequisites, a timed agenda, an in-class demo that runs entirely in the no-code sandbox, named datasets and queries, discussion questions, and a homework assignment.
Modules are organized by subject track, not one global 1–16 ladder. Teach a whole track, or pull a single module. Labels use Track + local number (Finance 1, Art 1, Data 1). Standalone sessions (Library, FinTech) sit outside the numbered tracks.
All modules share the same access model. Students work in the sandbox at sandbox.altfndata.com (browser-based, no install; self-register with a work or school email). Instructors who want the production API or a coding extension can request a shared class API key from info@altfndata.com. Docs and the Python client live at docs.altfndata.com.
Tracks at a glance
Finance
| Label | Title | Focus | Format |
|---|---|---|---|
| Finance 1 | Alternative data in finance | Survey of alt data and where auction/resale pricing fits | No-code |
| Finance 2 | Consumer and luxury economics | Pricing power, sell-through, and brand-level demand | No-code |
| Finance 3 | Investments and equity research | Brand-to-ticker mapping and saleroom vs public markets | No-code, optional code |
Art market
| Label | Title | Focus | Format |
|---|---|---|---|
| Art 1 | Art market fundamentals | What forms an artwork's price; comparables; primary vs secondary | No-code |
| Art 2 | The auction business model | How houses earn; estimate accuracy; value concentration | No-code, optional code |
| Art 3 | The business of the art market | League tables, artist pricing power, sell-through, concentration | No-code, optional code |
| Art 4 | Auction theory and behavioral economics | Estimates as anchors, winner's curse, reserves | No-code |
| Art 5 | Data science for art market research | Comparable sets, artist indices, demand signals | No-code, optional code |
Data & methods
| Label | Title | Focus | Format |
|---|---|---|---|
| Data 1 | Data science and SQL | Querying, aggregation, and a demand index in SQL | No-code, optional code |
| Data 2 | Data visualization and storytelling | Chart selection and communicating a market metric | No-code |
| Data 3 | Machine learning on auction data | Price prediction and sold/unsold classification | No-code, optional code |
| Data 4 | Time series and market indices | Quarterly demand index as method, not appreciation | No-code, optional code |
| Data 5 | Data engineering and the API | Discovery, query API, pagination, client, small ETL | No-code, substantial code |
| Data 6 | Data ethics, quality, and coverage bias | Coverage bias and the recency caveat | No-code |
Standalone
| Label | Title | Focus | Format |
|---|---|---|---|
| Library | Library data-literacy workshop | General-audience introduction to a real dataset | No-code |
| FinTech | FinTech and data products | How alternative data becomes a commercial product | No-code, optional code |
Suggested course fit
| Course type | Level | Recommended track / modules |
|---|---|---|
| Introduction to alternative data / fintech survey | Undergraduate, MBA | Finance 1, then FinTech |
| Database systems / data science methods | Undergraduate, graduate | Data 1 → Data 2 → Data 3 |
| Consumer behavior / luxury economics | Undergraduate, MBA | Finance 2 |
| Equity research / investments practicum | MBA, graduate finance | Finance 3 |
| Public or academic library workshop | General patrons, CE | Library |
| FinTech product design | MBA, graduate | FinTech, paired with Finance 3 |
| Art business / art market studies | Graduate, professional | Art 1 → Art 5 (full art track) |
| Auction house operations | Graduate, professional | Art 1 → Art 2 → Art 3 |
| Quantitative art market research | Graduate, analysts | Art 5, paired with Data 3–4 |
| Data visualization / analytics communication | Undergraduate, graduate | Data 2, paired with Data 1 |
| Machine learning / predictive modeling | Undergraduate, graduate | Data 3, paired with Data 1 and Data 4 |
| Econometrics / time series | Undergraduate, graduate | Data 4 |
| Microeconomics / behavioral economics | Undergraduate, MBA | Art 4, paired with Finance 2 |
| Data engineering / API integration | Undergraduate, graduate | Data 5, paired with FinTech |
| Data ethics / research methods | Any level, library programs | Data 6, paired with Library |
Midterm scope (locked): Finance 1–3 + Art 1.
Each session is designed to run in 60 to 90 minutes and assumes no prior exposure to ALT/FNDATA. For a shorter class, trim discussion first — the demo and dataset walkthrough carry the lesson.
File names (migration note)
On disk, lessons still use the legacy Module_N_*.md names until the track-slug file rename pass. Canonical IDs and portal routes already use track slugs (art/1, finance/2, library). See _deploy/module_renumber_map.json.