JOURNAL / DATA + CAREER
Ideas, guides & practical career notes
Practical notes on Data Analytics, interviews, career growth and building a smarter way of working.
-
How to Reconcile Two Reports When the Numbers Don’t Match
When two reports show different totals, use this practical data reconciliation workflow to check definitions, filters, duplicates, joins, and exceptions—with SQL examples.
-
Why I Wrote the AI-Assisted Analyst Books
Why the AI-Assisted book series focuses on analytical judgment, practical workflows and interview readiness instead of endless AI prompt lists.
-
Which AI Book Should Data Analysts Read First?
Choosing between AI-Assisted Analyst and AI-Assisted Interview Prep? Use this guide based on whether your priority is better analyst workflows or interview readiness.
-
The AI-Assisted Analyst Series: Two Books for the Modern Data Analyst
A practical overview of the AI-Assisted book series for data analysts: one book for AI-assisted analytical work and one for AI-assisted interview practice.
-
A 7-Day AI-Assisted Data Analyst Interview Practice Plan
A focused 7-day Data Analyst interview practice plan using AI for SQL, case studies, behavioral questions, communication and targeted feedback.
-
Behavioral Data Analyst Interviews: Use AI Without Sounding Scripted
Use AI to improve Data Analyst behavioral interview answers without sounding scripted. Start with real evidence, then practice skeptical follow-ups.
-
The Data Analyst Case Study Interview: A Better Way to Practice with AI
Practice Data Analyst case-study interviews with AI by focusing on problem framing, metric choice, assumptions, evidence, follow-ups and recommendations.
-
How to Use AI for Data Analyst SQL Interview Practice
Practice Data Analyst SQL interviews with AI without copying answers. Use realistic schemas, hints, progressively harder constraints and interviewer-style review.
-
AI-Assisted Interview Prep: How to Practice Data Analyst Questions with AI
Use AI as a realistic interviewer for Data Analyst preparation: practice problem framing, metrics, analysis, follow-ups and communication without memorizing scripts.
-
A Practical AI Quality-Control Checklist for Data Analysts
A practical 10-point AI quality-control checklist for data analysts covering problem framing, grain, joins, totals, edge cases, assumptions and recommendations.
Looking for structured interview preparation? Explore the free resources →