Core analysis recipes (what students actually do)

Why this exists: This is the practical chapter: recurring tasks and the patterns behind them.

Learning objectives

  • Compute daily/monthly totals and compare periods.

  • Build a simple sales proxy from ledger data.

  • Create a small set of plots/tables that answer one clear question.

Outline

Recipe: daily totals

  • Start from normalized/gl_journal.csv (canonical) and choose a revenue (or cash) account group.

  • Group by date, sum signed amounts.

  • If you’re using BYOD, you can generate daily totals with pystatsv1 trackd byod daily-totals --project <BYOD_DIR>.

  • Plot a time series; note spikes and missing days.

  • Write one sentence about the pattern you see.

Recipe: monthly P&L by category

  • Start from normalized/gl_journal.csv and map accounts to categories (revenue/COGS/opex).

  • Aggregate by month and category; compute shares and changes.

  • Identify the top 3 drivers of change month-over-month.

  • Sales proxy = sum of signed amounts for revenue accounts by day/month.

Recipe: concentration and outliers

  • Start from normalized/gl_journal.csv and find the largest transactions and their accounts.

  • Compute the share of total explained by the top N rows.

  • Flag unusual values for follow-up documentation.

Where this connects in the workbook

Note

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