Final exam
Cumulative across all six modules: alternative data in finance, data science and SQL, consumer and luxury economics, investments and equity research, library data literacy, and FinTech and data products. Time: 120 minutes. Total: 130 points. Format: sandbox for Sections 1 through 3; Section 2 also includes one API problem using a shared class API key from your instructor.
Read all four sections before starting. Use only the documented fields: designer, model, item_title, sale_date, usd_price_decimal, sale_estimates_high_usd_price, status, vendor, stock_ticker. Confirm exact table and column names in the schema tab, and confirm exact table names via GET /v1/tables before any API problem.
Section 1: Conceptual short answer (40 points, 5 points each)
1.1. Name the three ALT/FNDATA access surfaces and briefly state what each is for.
1.2. Explain why point-in-time correctness (using sale_date, and correctly handling status) matters when building any metric from this dataset.
1.3. Define pricing power and sell-through rate, and state one thing each reveals about consumer demand that a listed company's earnings report would not directly show.
1.4. Explain the recency caveat that applies to a quarterly demand index, and what an analyst should do before drawing a conclusion from one.
1.5. What does the stock_ticker field represent, and roughly how many companies does the underlying mapping cover?
1.6. Describe, briefly, the fact pattern of the Phillips and Movado divergence example, and what it illustrates about the relationship between secondary market results and listed company share prices.
1.7. In plain language suitable for a general, non-technical reader, explain why an unsold lot should not be treated the same as a sold lot when someone is trying to understand what an item is really worth.
1.8. Explain why building discovery endpoints such as GET /v1/tables and GET /v1/tables/{name}/schema into an API is good product design.
Section 2: Query writing (50 points)
2.1 (15 points): Write a SQL query that computes pricing power for one designer of your choice, correctly restricted to sold records with a valid, non-zero high estimate.
-- your query here
2.2 (15 points): Write a SQL query that computes sell-through rate for the same designer, correctly including both sold and unsold records in the denominator.
-- your query here
2.3 (20 points): Using the shared class API key your instructor has provided, write a JSON request body for POST /v1/tables/{name}/query that retrieves the raw fields (designer, status, sale_date, usd_price_decimal, sale_estimates_high_usd_price) needed to compute both figures above for the same designer, using the eq or like operator on designer. Then, in two to three sentences, explain how you would confirm you had retrieved every matching row rather than just the first page.
POST /v1/tables/<table_name>/query
Header: X-API-Key: <class key>
Body:
{
...
}
Section 3: Applied mini analysis (25 points)
Choose either a brand (using your Section 2 designer) or a publicly traded luxury company (using its stock_ticker). Write a 250 to 350 word applied analysis that states your Section 2 figures (or, if you choose the ticker route, figures computed the same way but filtered by stock_ticker instead of designer), interprets what they suggest about demand, and explicitly discloses at least one limitation of your analysis (sample size, single time snapshot, or the recency caveat if you reference any time-bucketed figure). If you choose the ticker route, briefly note one reason a cross-category roll-up (the same ticker appearing in more than one category table) would strengthen the analysis further.
Section 4: Plain language communication (15 points)
Rewrite your Section 3 analysis's core finding as a single short paragraph, no more than 100 words, suitable for a general audience with no background in finance or data (for example, a library workshop attendee or a family member). Avoid jargon such as "pricing power," "sell-through rate," or "median"; instead, describe the same finding using plain, concrete language.
Submission. Turn in this file with all four sections completed.