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.
Intro to plotting for analysis
A chart for analysis is a decision tool, not decoration. Learn a small matplotlib stack that turns pandas groupbys into honest bars and lines you can explain at…
How to tell a data story that earns the trust of senior leaders
Seniors trust analysts who lead with context, numbers, uncertainty, and a clear ask. Part 3 of Analyst career path gives a story spine and email skeleton you can…
How to use Python and SQL together for data analysis
Use SQL for large filters and governed aggregates, then polish results in pandas. Learn a read_sql hybrid pattern, when each tool wins, and how to review AI-generated code…
How to export pandas results and hand them off so others can use them
Finish the job with clean CSV exports, optional Parquet or SQL loads, and short metadata notes for every file. Learn what each destination needs so BI tools and…
How to turn one-off pandas data cleaning into a repeatable pipeline
Turn ad-hoc notebook cells into a light load-clean-validate-summarize pipeline you can trust. Small functions, row-count checks, and cold-start reruns make your Python analytics reproducible for teammates.




