Docs: Track D Student Edition landing page

docs/source/workbook/index.rst

@@ -13,5 +13,6 @@ PyStatsV1 Workbook
    my_own_data
    troubleshooting
    track_c
+   track_d_student_edition
    track_d
-   track_d_lab_ta_notes
+   track_d_lab_ta_notes

docs/source/workbook/quickstart.rst

@@ -85,7 +85,7 @@ Pick a folder you want to work in, then run:
 
       pystatsv1 workbook init --track d ./track_d_workbook
 
-   Then see :doc:`track_d` for the Track D workflow and dataset map.
+   Then see :doc:`track_d_student_edition` (student path) or :doc:`track_d` (full Track D workbook page).
 
 This creates a ready-to-run Workbook folder (scripts + tests + data).
 

docs/source/workbook/track_d.rst

@@ -1,6 +1,16 @@
 Track D Workbook: Business Statistics for Accounting Data
 =========================================================
 
+Start here (students)
+---------------------
+
+If you're a student, begin with :doc:`track_d_student_edition`.
+It explains the case story, the dataset contract, and the recommended “what to run” path.
+
+.. tip::
+
+   If you’re in a lab section, your TA may also assign :doc:`track_d_lab_ta_notes`.
+
 Track D is a **Business Statistics & Forecasting** track built around a realistic
 accounting running case (North Shore Outfitters, “NSO”).
 

docs/source/workbook/track_d_lab_ta_notes.rst

@@ -375,6 +375,15 @@ Show them the contribution entry in ``gl_journal.csv`` and compare to sales line
 constraints, and generate a first business summary. That’s the workflow: make data trustworthy before analyzing it.
 Next labs build on this foundation toward statistical reasoning and decision support.”
 
+
+Track D Lab + TA Notes (PyPI-only)
+==================================
+
+.. tip::
+
+   If students are new to the Track D case, have them read :doc:`track_d_student_edition` first.
+
+
 Appendix A: Command block (TA slide)
 ====================================
 

docs/source/workbook/track_d_student_edition.rst

@@ -0,0 +1,184 @@
+.. _workbook_track_d_student_edition:
+
+==========================================
+Track D Student Edition (Workbook Landing)
+==========================================
+
+This page is the **front door** for Track D (Business Statistics with an accounting case study).
+
+If you do only one thing first, do this:
+
+1) Follow the workbook quickstart.
+2) Run ``d00_peek_data`` to *see the data*.
+3) Run ``d01`` to *see the accounting invariants*.
+4) Use the “skill map” below to keep your bearings.
+
+.. note::
+
+   Track D is designed to make you a better analyst of accounting data.
+   You will learn how to **trust** the numbers (quality control), **summarize** them (statements and KPIs),
+   and **make decisions** with them (inference, regression, forecasting, scenarios).
+
+Where to start (PyPI-only)
+==========================
+
+Install and create the Track D workbook:
+
+.. code-block:: bash
+
+   python -m venv .venv
+   # Windows (Git Bash):
+   source .venv/Scripts/activate
+   python -m pip install -U pip
+   pip install "pystatsv1[workbook]"
+
+   pystatsv1 workbook init --track d --dest track_d_workbook
+   cd track_d_workbook
+
+Then run these two “confidence builders”:
+
+.. code-block:: bash
+
+   pystatsv1 workbook run d00_peek_data
+   pystatsv1 workbook run d01
+
+Helpful pages in the Workbook docs
+==================================
+
+These pages live inside the workbook documentation subtree (they build cleanly on their own):
+
+- :doc:`quickstart` — first-time setup and commands
+- :doc:`workflow` — how “run” vs “check” works, outputs conventions, troubleshooting
+- :doc:`track_d` — Track D workbook quickstart + dataset map (seed=123)
+- :doc:`track_d_lab_ta_notes` — a lab handout + TA notes (walkthrough + interpretation)
+
+What you are building (the pipeline)
+====================================
+
+Track D is a **repeatable analysis workflow**. You are not just “running scripts.”
+You are learning how to take messy accounting-like events and turn them into a decision-ready story.
+
+Here’s the mental model:
+
+::
+
+   Events (sales, bills, payroll, inventory, loans)
+       ↓  (recording rules + checks)
+   General Ledger (journal entries)
+       ↓  (postings → trial balance)
+   Financial Statements (IS, BS, CF)
+       ↓  (descriptive stats, visual checks)
+   Decisions (risk, sampling, tests, regression)
+       ↓  (forecasts, scenarios, governance)
+   Communicate (clear memo + reproducible outputs)
+
+The goal: **trustworthy numbers + clear decisions**.
+
+Why Track D makes you a better analyst
+======================================
+
+Track D trains three “analyst superpowers” that matter in real accounting and finance work:
+
+1) **Data integrity (trust the numbers)**
+   - You learn the invariants that must hold (balanced entries, consistent statements).
+   - You learn how to spot red flags (duplicates, missingness, impossible values, broken joins).
+
+2) **Decision discipline (answers with uncertainty)**
+   - You learn how to quantify risk (probability).
+   - You learn how to estimate and test (sampling + hypothesis testing).
+   - You learn how to model drivers (regression) without overclaiming.
+
+3) **Communication (results people can act on)**
+   - You learn how to tell a coherent story from the data.
+   - You learn to separate “signal” from “noise” and explain limits honestly.
+   - You learn reproducible workflows that other people can audit.
+
+Skill map (D00–D23)
+===================
+
+Use this map to understand *why* each group of chapters exists.
+
+Phase 0 — On-ramp (see the data)
+--------------------------------
+- **D00**: Setup/reset datasets and **peek** at the datasets (what tables exist, what they look like)
+
+Phase 1 — Accounting foundations (what the numbers mean)
+--------------------------------------------------------
+- **D01–D06**: journal entries, chart of accounts, statements logic, reconciliations, and quality control
+
+Phase 2 — Data preparation (make the dataset analysis-ready)
+------------------------------------------------------------
+- **D07**: build analysis tables, document joins/keys/grain, verify quality checks
+
+Phase 3 — Describe performance + report responsibly
+---------------------------------------------------
+- **D08–D09**: descriptive statistics and reporting conventions (what to compute, how to present it)
+
+Phase 4 — Statistics for decisions (business lens)
+--------------------------------------------------
+- **D10–D14**: probability/risk, sampling/estimation, hypothesis testing, correlation vs causation, regression drivers
+
+Phase 5 — Forecasting + governance
+----------------------------------
+- **D15–D23**: forecasting hygiene, seasonality, drivers, cash flow, integrated scenarios, and communication/governance
+
+How to use Track D week-to-week
+===============================
+
+A good weekly rhythm:
+
+1) Run the chapter script (``pystatsv1 workbook run dXX``).
+2) Open what it writes in ``outputs/track_d/`` (tables + summaries).
+3) Answer the chapter questions *in words* (what changed, why, what action follows).
+4) Run the smoke checks (``pystatsv1 workbook check business_smoke``).
+
+.. code-block:: bash
+
+   pystatsv1 workbook run d08
+   pystatsv1 workbook check business_smoke
+
+Common “student mistakes” and what to do
+========================================
+
+**“I ran it, but I don’t know what it means.”**
+- Start at :doc:`track_d_lab_ta_notes` and follow the interpretation prompts.
+- Re-run ``d00_peek_data`` and read the previews slowly.
+
+**“My outputs differ from the handout / screenshots.”**
+- Confirm you are using the canonical datasets (seed=123) under ``data/synthetic/``.
+- If you edited anything in ``data/synthetic/``, reset:
+
+  .. code-block:: bash
+
+     pystatsv1 workbook run d00_setup_data --force
+
+**“I want to apply this to my own data.”**
+- That’s the endgame. After you complete the basics, you’ll use a “bring your own data” playbook (coming next in the workbook docs) that shows how to map real exports (QuickBooks/bank/invoices) into the same workflow.
+
+What “good” looks like by the end
+=================================
+
+By the end of Track D you should be able to:
+
+- Explain how accounting events become analysis tables (and what can go wrong).
+- Produce a monthly trial balance and statements and sanity-check them.
+- Compute KPIs and explain what drives changes (not just “the number changed”).
+- Use estimation, tests, and regression to support a recommendation.
+- Produce a simple forecast and scenario analysis with clear assumptions.
+- Write a short memo that a manager could actually use.
+
+How to apply Track D to your own data
+-------------------------------------
+
+For a general starting point, see :doc:`my_own_data`.
+
+Track D will also include a Track D-specific “Bring Your Own Data” playbook that shows how to map real
+exports (QuickBooks / bank / invoices) to the same **dataset contract** used in the NSO synthetic case.
+
+
+Next page
+=========
+
+When you’re ready, jump to:
+
+- :doc:`track_d` (Track D workbook quickstart + dataset map)