Docs: Track D assignments + rubric (workbook)

docs/source/workbook/index.rst

@@ -18,4 +18,5 @@ PyStatsV1 Workbook
    track_d_dataset_map
    track_d_outputs_guide
    track_d_my_own_data
+   track_d_assignments
    track_d_lab_ta_notes

docs/source/workbook/track_d_assignments.rst

@@ -0,0 +1,208 @@
+.. _track_d_assignments:
+
+=========================================
+Track D assignments: labs + rubric (TA)
+=========================================
+
+This page turns Track D into a **gradeable lab sequence**.
+
+- Students produce a small set of files under ``outputs/track_d/``.
+- Students answer a few short prompts (and write one short memo).
+- TAs can grade quickly with a clear checklist + rubric.
+
+If students are new to Track D, point them to:
+
+- :doc:`track_d_student_edition`
+- :doc:`track_d_outputs_guide` (what outputs mean)
+- :doc:`track_d_dataset_map` (tables, keys, and intentional QC “warts”)
+
+Prerequisites (students)
+========================
+
+Students should start from a fresh Track D workbook folder created by::
+
+   pystatsv1 workbook init --track d --dest track_d_workbook
+   cd track_d_workbook
+
+All labs below assume the default Track D scripts in that folder.
+
+Submission format
+=================
+
+Ask students to submit **one ZIP file** containing:
+
+1) ``outputs/track_d/`` (artifacts)
+2) ``answers.md`` (short answers for Labs 1–3)
+
+Suggested ZIP command (from the workbook folder)::
+
+   # Windows PowerShell (inside track_d_workbook)
+   Compress-Archive -Path outputs/track_d,answers.md -DestinationPath track_d_submission.zip -Force
+
+   # macOS/Linux (inside track_d_workbook)
+   zip -r track_d_submission.zip outputs/track_d answers.md
+
+Lab 1 — Run + interpret outputs
+===============================
+
+Goal
+----
+
+Students can run Track D, locate outputs, and explain the key metrics in plain language.
+
+Run
+---
+
+From the Track D workbook folder::
+
+   pystatsv1 workbook run d00_peek_data
+   pystatsv1 workbook run d01
+
+Required artifacts
+------------------
+
+Students must include these files in ``outputs/track_d/``:
+
+- ``d00_peek_data_summary.md``
+- ``business_ch01_summary.json``
+- ``business_ch01_income_statement.png``
+- ``business_ch01_balance_sheet.png``
+
+Short answers (write in ``answers.md``)
+---------------------------------------
+
+Answer in 1–3 sentences each.
+
+1) **Where do outputs land?**
+   - Give the exact folder path (relative to the workbook).
+
+2) **Net income can be negative. Why?**
+   - Explain one realistic reason a startup month might show negative net income.
+
+3) **Gross margin, in plain language.**
+   - What is it, and why do analysts care?
+
+4) **What does ``pct_sales_on_account`` mean?**
+   - Explain it as if you were speaking to a non-accountant manager.
+
+5) **What good looks like (sanity checks).**
+   - List 2 checks you can run (or observe) that increase confidence you didn’t break the data.
+
+TA quick-check (optional)
+-------------------------
+
+For the stock Track D case, ``d01`` prints a small “sanity summary” to the terminal
+that includes ``n_transactions`` and whether the accounting equation balances.
+Students do **not** need exact matching numbers, but their explanations should be consistent.
+
+Lab 2 — QC detective: duplicate bank transaction IDs
+====================================================
+
+Goal
+----
+
+Students demonstrate “accounting-data analyst” habits:
+identify a QC issue, explain why it matters, and propose a defensible fix.
+
+Task
+----
+
+Using the NSO v1 bank statement table (from the Track D dataset), find a duplicated
+transaction identifier and document it.
+
+Students may use any method (spreadsheet filter, Python, etc.).
+Here is an optional Python snippet they can adapt::
+
+   import pandas as pd
+   p = "data/synthetic/nso_v1/bank_statement.csv"
+   df = pd.read_csv(p)
+   dup = df[df.duplicated("bank_txn_id", keep=False)].sort_values("bank_txn_id")
+   print(dup[["bank_txn_id","date","amount","description"]].head(20))
+
+Required artifacts
+------------------
+
+Students must include in ``outputs/track_d/``:
+
+- ``lab2_qc_report.md`` (their QC write-up)
+
+Their report must include:
+
+- the duplicated ``bank_txn_id`` value(s)
+- how many rows were duplicated
+- why the duplication matters (what could go wrong downstream)
+- a proposed fix policy (example: “drop exact duplicates”, or “keep the earliest posted row”)
+- one “before vs after” check (example: total cash inflow/outflow changes, or count of transactions)
+
+Short answers (write in ``answers.md``)
+---------------------------------------
+
+1) **What did you find?**
+   - One sentence summary of the duplicate.
+
+2) **What could break if we ignore it?**
+   - Name one analysis result that could be distorted.
+
+3) **What fix policy did you choose and why?**
+
+Lab 3 — Executive summary memo
+==============================
+
+Goal
+----
+
+Students turn outputs into a clear, decision-friendly narrative.
+
+Prompt
+------
+
+Write a **250–400 word** memo (in ``answers.md``) titled:
+
+**“Track D Executive Summary (Month 1)”**
+
+Use the Lab 1 outputs (plots + JSON summary) and include:
+
+- 2–3 key facts about sales, gross margin, and expenses
+- net income interpretation (including why it may be negative)
+- 1 balance sheet insight (example: cash position, liabilities, equity)
+- 2 “next questions” you’d ask before making a real decision
+
+Rubric (100 points)
+===================
+
+This rubric is designed to be fast to grade.
+
+Artifacts + reproducibility (30)
+--------------------------------
+
+- (10) Required Lab 1 artifacts present in ``outputs/track_d/``
+- (10) Lab 2 QC report present and complete
+- (10) Student’s ``answers.md`` is organized with headings for Lab 1–3
+
+Interpretation of outputs (30)
+------------------------------
+
+- (10) Net income explanation is realistic and correctly framed
+- (10) Gross margin explained correctly (and why it matters)
+- (10) ``pct_sales_on_account`` explained correctly and connected to business meaning
+
+QC reasoning (20)
+-----------------
+
+- (10) Duplicate ID issue clearly demonstrated with evidence
+- (10) Fix policy is defensible; “before vs after” check is included
+
+Communication quality (20)
+--------------------------
+
+- (10) Executive memo is clear, concise, and decision-oriented
+- (10) Writing uses plain language, defines terms, and avoids jargon where possible
+
+Extensions (optional)
+=====================
+
+If you want to stretch advanced students, ask for one extra artifact:
+
+- a small table (CSV) that summarizes totals by month (or by account)
+
+But the core labs above are intentionally minimal so students can complete them quickly.

docs/source/workbook/track_d_lab_ta_notes.rst

@@ -22,6 +22,23 @@ If you have 5 minutes before lab, skim these pages:
 * :doc:`track_d_dataset_map` — the table mental model + intentional QC “warts”
 * :doc:`track_d_outputs_guide` — what each generated output means and how to interpret it
 * :doc:`track_d_my_own_data` — a practical "bring your own data" bridge (30-minute recipe)
+* :doc:`track_d_assignments` — classroom-ready labs + rubric (gradeable artifacts + short answers)
+
+How to use the assignments page
+-------------------------------
+
+If you want Track D to be **turnkey as a lab**, point students at:
+
+* :doc:`track_d_assignments`
+
+That page is designed to be self-contained:
+
+* **Lab 1**: run Track D and interpret outputs
+* **Lab 2**: reconcile a teachable QC issue (duplicate bank transaction id)
+* **Lab 3**: write a short executive summary memo from the outputs
+
+The rubric is intended to reward analysis habits (contracts, checks, reconciliation, and clear writing),
+not just code execution.
 
 1. Learning goals
 =================

docs/source/workbook/track_d_student_edition.rst

@@ -53,6 +53,7 @@ These pages live inside the workbook documentation subtree (they build cleanly o
 - :doc:`track_d_dataset_map` — the **dataset mental model** (what each table is, how they relate, and why some rows are “warts” on purpose)
 - :doc:`track_d_outputs_guide` — how to read the outputs folders and key CSV artifacts
 - :doc:`track_d_my_own_data` — bring your own exports: a 30-minute "one table + one chart + one check" recipe
+- :doc:`track_d_assignments` — **student labs + rubric** (gradeable artifacts + short answers)
 - :doc:`track_d_lab_ta_notes` — a lab handout + TA notes (walkthrough + interpretation)
 
 What you are building (the pipeline)