Category: Analytics
This shelf is the job, not a tool manual. You are here if someone expects a number, a chart, or a “what should we do,” and the spreadsheet is starting to argue with itself. The articles cover the question before the query, a metric with a written definition, a chart that answers something, data that is fit to ship, and the messy middle of pipelines and stewardship.
It is not the SQL category. SQL is how you ask a table. Analytics is what you are trying to decide, and whether the number is even the right one. It is not the AI category either. A model can draft the email about the metric. It does not pick the metric.
Where to start: Analytics foundations if you do not yet have a clear question. Metrics if the team is arguing about a definition. Charts if the picture is pretty and the decision is still fuzzy. The SQL series when you are ready to ask the table yourself. Learn groups these as a track so you do not have to guess an order from a flat list.
- 4 min read
What is data literacy and why it matters
Data literacy is finding, reading, questioning, and using data to decide, including knowing when the data is weak. A practical ladder for readers, builders, and leaders who live…
How to choose where to store your data: a database, a warehouse, or a lake
App DB, warehouse, lake, mart, spreadsheet: each stores data for a different job. A plain map of the stack, tradeoffs, and a decision table so you stop buying…
- 5 min read
Which programming language to learn for data analytics: start with SQL
Which language should analysts learn? Start with SQL, add Python or R for heavier work, and treat BI calculation languages as real production logic. A practical map without…
Data jobs demystified: what each role actually does
Titles blur. Analyst, analytics engineer, BI, data scientist, data engineer, and ML engineer optimize for different questions and outputs. A plain map for hiring, career choices, and less…
What is the difference between analytics, business intelligence, and data science?
BI, analytics, data science, data engineering, and AI share tools but not goals. Plain definitions, how work hands off, and how to frame projects without buzzword soup.
