Introduction

This Know-how section is a growing reference for data analysts. It covers tools, domains, and practices I use day to day.

How to use it

Look up — Use as a reference when you need a pattern or reminder (e.g. SQL, metrics).
Explore — Browse categories to see what’s covered and where to go deeper.
Build on it — Content is meant to be extended; add your own notes and examples.

Category	What you'll find
SQL	Queries, patterns, and best practices
Tableau	Dashboards and viz in Tableau
Power BI	Dashboards and modeling in Power BI
Marketing Analytics	Campaigns, attribution, and marketing metrics
Web Analytics	GA, tracking, and web metrics
Product Analytics	Usage, engagement, and in-product behavior
Payments Analytics	Cards, Stripe, and payments data
Procurement Analytics	Spend, suppliers, and procurement metrics
Risk & Assurance Analytics	Controls, compliance, and risk metrics
Sales Analytics	Pipeline, revenue, and sales performance
Financial Analytics	P&L, forecasting, and financial metrics
Location Analytics	Geospatial, territories, and location data
People Analytics	Headcount, attrition, diversity, and workforce metrics
Experiment Analytics	A/B testing, statistical significance, and causal inference
Data Pipelines	ETL, orchestration, and pipeline design
dbt	Data modeling and transformations
Python	Pandas, analysis, and data workflows
AI & Agents	LLMs, agents, and AI for analytics
Building Evals	Evaluation frameworks and metrics
Startup Metrics	North Star, KPIs, and growth metrics