Track D Playbook: Big Picture

Track D is about one idea: use statistics to understand accounting data. The loop is: export → normalize → validate → analyze → communicate.

The case study (NSO) gives you realistic, messy numbers—but the goal is transfer: you should be able to take your own accounting exports and run the same kind of analysis with PyStatsV1.

This playbook is a short “map of the territory.” Each chapter is an outline (for now), meant to be filled in gradually.

How to use this playbook

1. Read Orientation: what Track D is and how to use it once (it explains the full Track D workflow).

2. Use Core analysis recipes (what students actually do) as your “what do I do next?” page while working.

3. When you bring your own data, jump to BYOD in the real world (adapters, exports, privacy) (and see pystatsv1 trackd byod daily-totals).

Where to find the commands and file paths

• Student entry point: Track D Student Edition (Workbook Landing)

• Track D chapter list: Track D chapter index (PyPI)

• Dataset map + outputs: Track D Dataset Map, Track D Outputs Guide

• Bring your own data (BYOD): Track D BYOD: Bring Your Own Data

• This playbook: Track D Playbook: Big Picture

• Orientation: what Track D is and how to use it
  • Learning objectives
  • Outline
  • The Track D workflow in one page
  • What you should have at the end
  • Common mental model mistakes (and fixes)
  • Where this connects in the workbook
• Accounting data as a dataset pipeline
  • Learning objectives
  • Outline
  • From events to reports
  • From reports to analysis
  • Where BYOD fits
  • Where this connects in the workbook
• The Track D dataset contract (what scripts expect)
  • Learning objectives
  • Outline
  • Inputs vs normalized outputs
  • Column naming and why it matters
  • What pystatsv1 trackd validate does conceptually
  • Where this connects in the workbook
• NSO case study: why these numbers exist
  • Learning objectives
  • Outline
  • The business story we’re modeling
  • The analysis questions Track D repeats
  • Transfer to your own data
  • Where this connects in the workbook
• Core analysis recipes (what students actually do)
  • Learning objectives
  • Outline
  • Recipe: daily totals
  • Recipe: monthly P&L by category
  • Recipe: concentration and outliers
  • Where this connects in the workbook
• Time series + forecasting for accounting data
  • Learning objectives
  • Outline
  • Start with baselines
  • Add explanatory variables
  • Keep it business-grounded
  • Where this connects in the workbook
• Risk, controls, and data quality checks
  • Learning objectives
  • Outline
  • Practical checks that scale
  • Sampling mindset
  • When to stop and ask for accounting context
  • Where this connects in the workbook
• BYOD in the real world (adapters, exports, privacy)
  • Learning objectives
  • Outline
  • The BYOD workflow
  • Adapters and tradeoffs
  • Privacy + classroom sharing
  • Where this connects in the workbook
• Reporting: turning outputs into decisions
  • Learning objectives
  • Outline
  • A simple reporting template
  • What makes a good figure
  • Common reporting failures
  • Where this connects in the workbook
• Capstones: applying Track D to your own accounting data
  • Learning objectives
  • Outline
  • Capstone scope options
  • Deliverables checklist
  • Rubric outline (draft)
  • Where this connects in the workbook
• Appendix: Track D + BYOD CLI cheatsheet
  • Learning objectives
  • Outline
  • Install + create workbook (PyPI)
  • Run Track D scripts
  • BYOD: normalize + validate
  • Where this connects in the workbook
• Glossary (draft)
  • Accounting terms
  • Analytics terms
  • Track D + BYOD terms