First-Principles Thinking for Data Analysts
What first-principles thinking means for a data analyst, with worked examples: questioning a metric, a benchmark and a dashboard request.
Analyst Craft & Authority
The non-technical half of the job — storytelling, portfolios, metric definitions and prioritization that make analysis land.
SQL Interview Questions Analysts Are Actually Asked
The recurring SQL interview patterns behind most data analyst interviews, how to approach each, and how interviewers actually grade the answers.
RICE Prioritization: Scoring a Roadmap Without Bias
How the RICE scoring framework works, a worked example ranking three features, and where it breaks down as a decision tool.
KPI Dashboard Best Practices: Build One People Actually Open
Most dashboards get built, launched and forgotten. Design for decisions, keep the metric count low, define every number, keep data fresh and review usage.
Data Analyst Portfolio Projects That Get Interviews
What hiring managers look for in a data analyst portfolio, how to choose three strong projects, and how to present each one around a business problem.
Excel vs SQL vs Power BI: How an Analyst Chooses
Which tool for which job? A practical way to choose between Excel, SQL and Power BI, based on data size, repeatability, audience and who needs to reuse the work.
Data Storytelling for Non-Technical Stakeholders
How to present analysis so busy, non-technical people act on it. A simple structure, slide habits, and the mistakes that make good analysis get ignored.
Metric Definition Doc: What to Write Down (With a Template)
Stop dashboard debates with a one-page metric definition. Includes a copy-paste template, a filled-in example and the owner, grain and exclusion rules that matter.
Why AI analysts fail on bad metric definitions
AI can write valid SQL in seconds, but it can't know which definition of revenue your CFO approved. Why a governed metrics layer matters, with benchmark data.