Track D Workbook: Business Statistics for Accounting Data

Start here (students)

If you’re a student, begin with Track D Student Edition (Workbook Landing). 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 Track D Lab 0/1 (PyPI-only).

Track D is a Business Statistics & Forecasting track built around a realistic accounting running case (North Shore Outfitters, “NSO”).

The Track D Workbook is a PyPI-first student experience. You can install PyStatsV1, create a Track D workbook folder, and run Track D only (no repo clone required).

If you haven’t seen the Track D overview yet, start here:

• Track D overview (main docs)

What you get

When you run pystatsv1 workbook init --track d, PyStatsV1 creates a local folder containing:

Big picture map (recommended)

If you feel like you are learning lots of commands but losing the “why”, read:

• Track D Playbook: Big Picture

• convenience runner scripts (d01 … d23) that map to Track D chapters

• a reproducible, pre-installed dataset under data/synthetic/ (seed=123)

• an outputs/track_d/ folder where results are written

• a small smoke test so you can quickly confirm everything is working

Quickstart (PyPI-only)

1. Create a virtual environment and install the workbook extras.

python -m venv .venv
# Windows (Git Bash):
source .venv/Scripts/activate

python -m pip install -U pip
pip install "pystatsv1[workbook]"

2. Create a Track D workbook folder.

pystatsv1 workbook init --track d --dest track_d_workbook
cd track_d_workbook

3. See what you can run.

pystatsv1 workbook list --track d

4. Peek the data (recommended first step).

pystatsv1 workbook run d00_peek_data

This prints the row counts and column names for the key CSV files and writes a short markdown summary into outputs/track_d/.

5. Run the first chapter.

pystatsv1 workbook run d01

6. Run a quick self-check.

pystatsv1 workbook check business_smoke

Tip

You can re-run any chapter as many times as you want. The scripts overwrite outputs deterministically as long as you keep the dataset unchanged.

What data you are working with

Track D ships two canonical datasets. They are synthetic but realistic, and are the same every time (seed=123).

1. LedgerLab (Ch01) — a small “starter ledger”

• purpose: introduce the accounting equation, debits/credits, and how a general ledger becomes an analysis-ready table

• location: data/synthetic/ledgerlab_ch01/

• key files:

  • chart_of_accounts.csv

  • gl_journal.csv

  • monthly outputs like trial_balance_monthly.csv and statements_*_monthly.csv

2. NSO v1 running case — a richer business dataset

• purpose: practice the real analyst workflow: reconcile, validate, reshape, summarize, model, and forecast

• location: data/synthetic/nso_v1/

• key files include event logs (AR/AP/payroll/inventory), a bank statement feed, a generated general ledger, and monthly statements and trial balances

Before you run deeper chapters, skim:

• Track D Dataset Map — what each table is, how tables relate, and why some rows look “weird” on purpose

If you want to see the full data dictionary for NSO v1:

• NSO v1 data dictionary cheat sheet (main docs)

Resetting (or regenerating) the datasets

Datasets are installed automatically during workbook init.

If you edit anything under data/synthetic/ and want to return to the canonical seed=123 version:

pystatsv1 workbook run d00_setup_data --force

This restores the canonical dataset files and re-runs a few lightweight checks.

Where your results go

By default, Track D scripts write artifacts to:

• outputs/track_d/ — tables, memos, and small machine-readable summaries

• outputs/track_d/figures/ — charts created by chapters that plot results

For a practical walkthrough of the most common output files (CSV/JSON/PNG) and how to use them in a write-up, see Track D Outputs Guide. To apply the same workflow to your own exports, see Track D: Apply what you learned to your data.

A typical chapter produces:

• one or more .csv artifacts (tables you can open in Excel)

• a short .md memo describing what the script did and what to look for

• a “manifest” CSV listing outputs (useful for grading or reproducible reports)

How Track D helps you become a better accounting-data analyst

Track D is not “statistics in isolation.” The goal is to practice the full loop:

• Measurement + integrity: understand what the numbers mean and what can go wrong

• Quality control: detect issues early (duplicates, broken keys, imbalances)

• Summarization: build monthly statements and diagnostic KPIs from transactions

• Explanation: connect changes in performance to drivers (mix, price, volume)

• Forecasting: build baselines, add drivers, run scenarios, and communicate uncertainty

Most chapters are designed to leave you with a concrete artifact you can show:

• an analysis table (CSV)

• a chart

• a short memo with the “story” and the assumptions

Suggested workflow by week

A simple way to work through Track D:

• Start each week with d00_peek_data to remind yourself what tables exist.

• Run the chapter wrapper (dNN).

• Open outputs/track_d/ and read the newest memo first.

• Use the artifacts for your write-up.

To connect the workbook runs to the textbook pages, use the Track D chapter docs as your “book”:

• Track D chapter docs (main site)

Troubleshooting

• If a chapter fails, try running pystatsv1 workbook check business_smoke to confirm your environment is healthy.

• If you modified data and things look “weird,” reset with pystatsv1 workbook run d00_setup_data --force.

• For general workbook workflow tips, see Workflow: Run → Inspect → Check.