.. _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` — the Track D workbook page (run list + where to start) - :doc:`track_d_chapter_index` — a chapter map (D00–D23): what to run + what to look at - :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) ==================================== 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). If you are not sure what a file is for, use :doc:`track_d_outputs_guide`. 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`. For Track D specifically: - Start with :doc:`track_d_my_own_data` (what to analyze + what to check). - Use :doc:`track_d_byod` to turn a **real export** into Track D’s canonical tables (the BYOD pipeline). Next page ========= When you’re ready, jump to: - :doc:`track_d` (Track D workbook quickstart + dataset map)