Step-by-step guides for SQL, tools, and practical AI — skills you can use at work. Learn by doing, not by jargon.
Series
Step-by-step paths grouped by topic. Start at part 1 and work through at your pace.
Foundations
How analytics works and how everyday data becomes structured.
Core skills
SQL, Python, data quality, charts, and metrics — skills you reuse everywhere.
Data at work
Pipelines, stewardship, and how data moves through real teams.
AI fundamentals
Practical AI for everyday work — product-agnostic judgment and workflows.
AI products
One complete path per brand — first login through deep tools.
Analytics practice
Stats, experiments, customer and finance analytics, and career paths.
BI & workflow tools
Hands-on starters for Power BI, dbt, Airflow, and similar tools.
More series
Additional paths that do not fit a section yet.
Comparing groups without fooling yourself
Group A beat Group B on the dashboard. Was it the product, the mix of users, or a baseline that moved on its own? Part 2 of Statistics…
Batch vs streaming in plain English
Real time is a latency budget, not a vibe. Learn batch, streaming, and micro-batch in plain English so you know when yesterday is fine, when minutes matter, and…
Tokens, context windows, and cost (intuition)
Context is a suitcase, not infinite storage. Part 2 of Practical AI for analytics people explains tokens, why long pastes fail, how cost scales with schemas and history,…
Autonomy: when to watch vs walk away
Part 5 of the Claude Cowork tutorial. Choose Manual, Auto, or Skip with eyes open: deletion still needs Allow, Auto screens actions (and uses more quota), Skip does…
Axes, scales, and honest baselines
Axes decide whether a chart is honest. Learn baselines, truncated scales, dual-axis traps, and when a log scale helps so small differences stop looking like emergencies in the…
Accounts, payments, and Free vs paid without hype
Free is enough to start. Paid buys limits and extra surfaces, not a personality. This part maps accounts, payment rails (web vs App Store vs API), and Free…
What open means: weights, license, not a free lunch
“Open” in AI is three different claims: open weights you can download, open source under the OSI bar, and a free chat box. This part names the gap,…
Grok chat, Build, Imagine, and the API: pick a surface
Grok is four products with four bills: chat apps, Grok Build, Imagine, and the API Console. Pick the door that matches the job so you stop buying credits…
I just want help with writing and questions
You want a chat box for drafts, explanations, and questions, not a coding agent. This part names ChatGPT, Claude, Gemini, and Grok as writing tools, what to try…
Choosing a model for work (not hype)
Leaderboards are a hobby. Work needs latency, cost, privacy, and failure modes. Here is a practical scorecard for picking a model when the output will touch real decisions.
Airflow hello: your first DAG as a ticket rail
Orchestration is a ticket rail, not a junk drawer. Learn what an Airflow DAG is, how tasks and dependencies map to real runbook stations, and how to sketch…
dbt lab: project layout that scales
A messy dbt repo ages fast. Map staging, intermediate, and marts with clear naming and one grain per model so the project scales past your first three tables…











