Data & Analytics
Data pipelines, engineering, BI, and the ML basics - turning raw data into answers you trust.
Watch it animated → Interactive, click-through walkthroughs for Data & Analytics concepts.Basic
What "Data Engineering" Even Is Data engineering is the plumbing that turns messy raw data into clean, trusted data people can make decisions on - here's the pipeline mental model and the pieces that make it up.
Spreadsheets → SQL → Pipelines The natural progression most data work actually follows - start in a spreadsheet, graduate to SQL when the sheet breaks, and build a pipeline when the work has to run itself.
Metrics That Lie How plausible-looking numbers mislead: averages hiding skew, survivorship bias, Simpson's paradox, and vanity metrics. Read a dashboard without being fooled.
A/B Testing, Explained How randomized splits let you measure which variant actually performs better, and the three ways teams accidentally fool themselves with the results.
Intermediate
ETL & ELT Pipelines, Explained What a data pipeline actually is, the difference between transforming before or after you load, and what it takes to make a scheduled pipeline run reliably instead of just running.
Data Warehouses vs Lakes, Plainly What a data warehouse actually is, what a data lake (and lakehouse) actually is, and how to choose or combine them without ending up with an expensive bill or a data swamp.
Building a BI Dashboard That's Actually Useful A dashboard is an answer to a recurring question, not a pile of charts. Start from the decision, pick metrics that change minds, and design something people actually open.
ML Basics for Data People What machine learning actually is from a data perspective - learning patterns from history instead of hand-writing rules, the workflow from features to evaluation, and why ML lives or dies on your data.
SQL Window Functions The analyst superpower: running totals, rankings, and row-to-row comparisons without collapsing rows. OVER, PARTITION BY, and lag and lead.
The Star Schema, Explained The dimensional-modeling pattern behind most data warehouses - one fact table surrounded by dimension tables - and why it's shaped that way on purpose.
Power BI, From Zero Take a spreadsheet from your desktop to a live, scheduled, trustworthy Power BI report - Power Query, the data model, DAX, real visualizations, and publishing done right.
Advanced
Data Quality & Pipeline Observability How to trust the numbers your pipelines produce: why a green job can still ship wrong data, the checks that catch silent breakage, and the observability that finds it before a human makes a decision on it.
DAX, Deep Dive Why does the same DAX measure show a different number on a card, in a table, and next to a slicer? This guide teaches the real reasoning underneath DAX - row context, filter context, CALCULATE, and the patterns and performance habits that come from actually understanding it.