ALT/FNDATA academic program
A complete, ready-to-teach package so professors, instructors, and librarians can adopt ALT/FNDATA's luxury-asset pricing dataset in courses and in library data-literacy programs. Everything here is built to be used as it stands. Students work in a no-code browser sandbox with zero setup and no API key, and every example rests on stable measures rather than on recent-period totals.
What ALT/FNDATA is, in one paragraph
ALT/FNDATA is institutional-grade pricing data on the secondary market for physical luxury assets, the auctions and resale marketplaces where watches, jewelry, gems, handbags, fine art, works of art, wine and whisky, automobiles, and more actually change hands. It holds more than 15 million transaction records across roughly 7,000 brands and more than 850 vendors, with history back to 1949. Each record carries the pre-sale estimate, the realized price in US dollars, the sale date, the vendor, the brand and model, the sold or unsold status, and a stock ticker from a company mapping of more than 2,000 firms.
How access works
- Students use the no-code sandbox at sandbox.altfndata.com. They self-register with a work or school email and are auto-approved. No key, no setup.
- Instructors who want the Python code track can request a single shared class API key from the ALT/FNDATA team. Keys are issued manually, not self-serve.
- Documentation, a reusable Python client, and a runnable notebook live at docs.altfndata.com.
- Questions or a class key: info@altfndata.com.
How to navigate this package
| Folder | What is inside | Start here if you are |
|---|---|---|
| 01_Instructor_Overview | A one-page overview of the dataset, its teaching value, access, and support | New to ALT/FNDATA |
| 02_Course_Modules | Six syllabus-ready modules with timed session plans and sandbox-first demos | Planning a course or a single session |
| 03_Tutorials | Step-by-step tutorials in two tracks, no-code and Python, plus a runnable notebook | Ready to put students on the data |
| 04_Assessments | Problem sets, quiz banks, a midterm, a final, and a capstone, with separate answer keys | Grading the work |
| 05_Librarian_Pack | Catalog positioning, a LibGuide draft, a data-literacy session plan, and an access FAQ | An academic librarian |
| 06_For_Educators_Docs_Outline.md (instructor only, request at info@altfndata.com) | A proposed public "For educators" section for docs.altfndata.com (stretch) | Thinking about publishing this |
| 07_Outreach (instructor only, request at info@altfndata.com) | A CRM placeholder list and re-engagement email drafts (prepare only, send nothing) | Re-approaching contacts |
Modules by subject track
Modules are grouped by track (not one global 1–16 ladder). Labels are Track + local number.
Finance — Finance 1 Alternative data in finance · Finance 2 Consumer and luxury economics · Finance 3 Investments and equity research
Art market — Art 1 Fundamentals · Art 2 Auction business model · Art 3 Business of the art market · Art 4 Auction theory · Art 5 Data science for art market research
Data & methods — Data 1 SQL · Data 2 Visualization · Data 3 Machine learning · Data 4 Time series · Data 5 Data engineering / API · Data 6 Ethics and coverage bias
Standalone — Library data-literacy workshop · FinTech and data products
See 02_Course_Modules/README.md for the full tables and course-fit guide. Canonical IDs use track slugs (art/1, finance/2); migration map: _deploy/module_renumber_map.json.
The concepts these materials teach
- Pricing power is the median of realized price over the high estimate. Above 1.0 means buyers pay over estimate, as Van Cleef and Arpels does at roughly 1.36 times.
- Sell-through is sold lots over offered lots, a direct read on demand.
- A demand index buckets a brand's cleared prices by quarter. Treat it as a lesson in method, since recent quarters may be under-ingested.
- Brand-to-ticker maps each brand to the listed group that owns it, so a student can lay the saleroom beside the public equity, for example Richemont as CFR.SW.
Ground rules baked into every file
- Figures stay conservative and consistent: more than 15 million records, roughly 7,000 brands, history to 1949.
- Examples rest on stable ratios, counts, and medians. A dip in the latest quarter reflects ingestion coverage, not the market cooling, and the materials say so.
- API examples use the real shape: POST /v1/tables/{name}/query with an X-API-Key header, filters as an array of {field, op, value}, and a {table, result_count, data} response envelope. Only documented fields appear in examples.
- The AI natural language search feature is in development and is not featured anywhere.
- The only public contact is info@altfndata.com.
Answer keys
Every answer key is a separate file from the student version, clearly labeled for instructors. Distribute keys to instructors only; do not publish them or include them in student handouts. See 04_Assessments/README.md for the full student-versus-key file map.
Prepared by ALT/FNDATA. For adoption support, a class API key, or a walkthrough, write to info@altfndata.com. Sharon Obuobi, Founder, ALT/FNDATA.