Assessments index
This folder contains all graded and practice assessments for ALT/FNDATA's academic program. Every assessment is built on the same three access surfaces used throughout the course: the no-code sandbox at sandbox.altfndata.com, the documentation and tutorials at docs.altfndata.com, and the production REST API at api.altfndata.com. Students complete coursework in the sandbox unless a problem explicitly calls for the API, in which case an instructor supplies a shared class API key (students never self-serve a key).
Every graded item that has a scoring answer ships as two files: a STUDENT version with no answers, and a separate KEY (or KEY_and_Rubric) file for instructors. Keep the two files apart when distributing coursework so students never receive the key by accident.
Folder contents
04_Assessments/
README.md
Problem_Sets/
Problem_Set_1_Alternative_Data_STUDENT.md
Problem_Set_1_Alternative_Data_KEY.md
Problem_Set_2_Data_Science_SQL_STUDENT.md
Problem_Set_2_Data_Science_SQL_KEY.md
Problem_Set_3_Consumer_Luxury_Economics_STUDENT.md
Problem_Set_3_Consumer_Luxury_Economics_KEY.md
Problem_Set_4_Investments_Equity_Research_STUDENT.md
Problem_Set_4_Investments_Equity_Research_KEY.md
Problem_Set_5_Library_Data_Literacy_STUDENT.md
Problem_Set_5_Library_Data_Literacy_KEY.md
Problem_Set_6_FinTech_Data_Products_STUDENT.md
Problem_Set_6_FinTech_Data_Products_KEY.md
Quiz_Banks/
Quiz_Bank_STUDENT.md
Quiz_Bank_KEY.md
Exams/
Midterm_STUDENT.md
Midterm_KEY_and_Rubric.md
Final_STUDENT.md
Final_KEY_and_Rubric.md
Capstone/
Capstone_Brief.md
How assessments map to the six modules
| Module | Title | Assessment |
|---|---|---|
| 1 | Alternative data in finance | Problem_Sets/Problem_Set_1_Alternative_Data_STUDENT.md and KEY |
| 2 | Data science and SQL | Problem_Sets/Problem_Set_2_Data_Science_SQL_STUDENT.md and KEY |
| 3 | Consumer and luxury economics | Problem_Sets/Problem_Set_3_Consumer_Luxury_Economics_STUDENT.md and KEY |
| 4 | Investments and equity research | Problem_Sets/Problem_Set_4_Investments_Equity_Research_STUDENT.md and KEY |
| 5 | Library data literacy workshop | Problem_Sets/Problem_Set_5_Library_Data_Literacy_STUDENT.md and KEY |
| 6 | FinTech and data products | Problem_Sets/Problem_Set_6_FinTech_Data_Products_STUDENT.md and KEY |
| All | Cumulative review, any module order | Quiz_Banks/Quiz_Bank_STUDENT.md and KEY (topic tagged, not module tagged) |
| Modules 1 to 3 | Midpoint checkpoint | Exams/Midterm_STUDENT.md and KEY_and_Rubric |
| Modules 1 to 6 | End of term | Exams/Final_STUDENT.md and KEY_and_Rubric |
| Modules 3 and 4, optional across any | Independent applied project | Capstone/Capstone_Brief.md |
Assessment descriptions
Problem sets (Problem_Sets/). One pair of files per module, each problem set holding four to six problems worth a stated point total (60 to 100 points depending on module). Every problem is sandbox first: students write SQL against the named table and fields, or in Modules 4 and 6 optionally construct an API request body. Problems reference only the documented fields (designer, model, item_title, sale_date, usd_price_decimal, sale_estimates_high_usd_price, status, vendor, stock_ticker) and instruct students to confirm exact table names with GET /v1/tables before querying, since the schema browser in the sandbox and the API discovery endpoint are the source of truth, not a memorized table list. Key files give full worked SQL, worked API JSON bodies where relevant, and the expected shape of the result (what columns come back, what the aggregate should represent, how to sanity check it) rather than fabricated exact row counts or dollar figures.
Quiz bank (Quiz_Banks/). A single reusable bank of at least 25 multiple choice questions and 10 short answer questions, grouped into seven topics that cut across all six modules: access and schema, SQL and filters, pricing power, sell through, demand index, brand to ticker, and data literacy and caveats. Instructors can draw a subset for a five minute warm up quiz, a review session, or a randomized online quiz pool. The KEY gives the correct choice and a one to two sentence rationale for every item, plus model short answers.
Midterm and final (Exams/). The midterm covers Modules 1 through 3 (alternative data concepts, SQL fundamentals, consumer and luxury economics) and runs 75 minutes. The final is cumulative across all six modules and runs 120 minutes, adding investments, library data literacy communication skills, and FinTech product thinking. Both exams mix three sections: conceptual short answer, a query writing section (sandbox SQL and, in the final, an API JSON body), and an applied mini analysis that asks students to interpret a result rather than just produce one. Each KEY_and_Rubric file gives point allocations per section, model answers, and partial credit guidance for the query writing section (since minor syntax variants that reach the same correct SELECT should still earn most of the credit).
Capstone brief (Capstone/). An optional independent project for students who want to go beyond the graded problem sets. A student chooses either a brand or a listed company and builds a short demand analysis using the sandbox and the tutorials notebook, culminating in a two to three page brief plus supporting queries. Includes a grading rubric and three worked example directions (Richemont via CFR.SW, Van Cleef and Arpels pricing power, and a sell through comparison across two competing brands) to show the range of acceptable projects without dictating a single answer.
Shared conventions across every assessment in this folder
- Access model. Students query the no-code sandbox at sandbox.altfndata.com, which they reach by self-registering with a work or school email address (auto-approved, no waiting on an instructor). No API key is required for sandbox work. Where an assessment calls for the production API, the instructor supplies one shared class API key; students never request their own.
- Discovery first. Any problem that touches the API opens with GET /v1/tables and GET /v1/tables/{name}/schema before writing a query, so students confirm exact table and column names rather than guessing.
- Documented fields only. All problems draw from the fields called out in the course materials: designer, model, item_title, sale_date, usd_price_decimal, sale_estimates_high_usd_price, status, vendor, and stock_ticker. No assessment invents a field that is not in that list.
- Stable metrics, not year over year trend claims. Ingestion of the most recent quarters lags, so recent-period aggregates should never be read as a market appreciation or depreciation signal. Every problem, quiz item, and exam question that touches a demand index or a time bucketed metric frames it as a methodology exercise (building a demand index for learning purposes) and calls out this caveat explicitly rather than asking students to conclude that a brand is "up" or "down" year over year.
- No natural language search. ALT/FNDATA's AI natural language query feature is still in development and does not appear in any assessment; every query exercise uses SQL in the sandbox or a structured JSON filter body against the API.
- Sentence case titles, no em or en dashes. All assessment files in this folder follow the same house style as the rest of the course materials.
- Contact. Any assessment that references support routes students and instructors to info@altfndata.com.