Orientation: what Track D is and how to use it

Why this exists: Track D can feel like “a lot of scripts.” This chapter shows the workflow that ties everything together.

Learning objectives

  • Explain Track D in one sentence (statistics on accounting data).

  • Describe the Track D workflow: export → normalize → validate → analyze → communicate.

  • Know the three kinds of Track D work: case study, labs, and BYOD.

  • If you forget a command, run pystatsv1 --help or pystatsv1 trackd byod --help.

Outline

The Track D workflow in one page

  • Start from an accounting export (or the NSO case study dataset).

  • Get the data into the Track D dataset contract (either already canonical, or via BYOD normalization).

  • Run a chapter script to answer a question (and write outputs).

  • Use the artifacts (CSV/PNG/JSON) to write a short business interpretation.

What you should have at the end

  • A reproducible folder with inputs + scripts + outputs (so you can rerun later).

  • A small set of charts/tables that tell a story about revenue, costs, or risk.

  • A written summary that a manager could act on.

Common mental model mistakes (and fixes)

  • Mistake: treating accounting data as “just categories.” Fix: it’s a time-stamped database with structure.

  • Mistake: skipping validation. Fix: always run a quick check before believing results.

  • Mistake: staring at raw rows. Fix: aggregate into daily/monthly totals and compare periods.

Where this connects in the workbook

Note

This page is intentionally an outline right now. Expand it incrementally as we refine Track D narrative.