Six months of clean, categorized expense data; a stable categorization scheme you can defend; your three-month rolling savings rate computed from real numbers (not aspiration); your real hourly wage computed; and a written one-paragraph honest answer to: "Which two categories of spending are buying me the least life-satisfaction per unit of money?" Backfill is data engineering: six months of categorized expense data is cleaning the warehouse before running analytics. The 5-7-category rule is the cardinality discussion you'd have about a star schema — the "perfect" granular budget is the enemy of the working monthly review. And the real hourly wage is the unit economics of self. Nominal wage is gross revenue per hour; real wage is contribution margin per hour — fee minus effective tax minus work-related costs minus the unpaid recovery hours, divided by all hours the work touches. Most engineers operate on gross revenue forever and wonder why the raise never seems to land in life satisfaction.
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I want to start by saying what's strange about this module, which is that it's the easiest to describe and the hardest to actually do, and the curriculum will not be honest if it …
The savings rate is the only personal-finance variable that empirically dominates everything else over multi-decade horizons. The math is unambiguous: at a 10% savings rate, finan…
Approach: Essential
Approach: Essential
Approach: Important
Approach: Important
1. Set up the tracking infrastructure (spreadsheet + chosen ingestion tool) and backfill the last 6 months from bank and card statements. Dirty data is the most common reason engi…
7 lessons. Read in order; spiral back when you need to. By the end you'll have used the core ideas twice — once on the abstract, once on something you'll meet at work next week.