JOURNAL / DATA + CAREER
Ideas, guides & practical career notes
Practical notes on Data Analytics, interviews, career growth and building a smarter way of working.
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Data Validation Before Reporting: 10 Checks Analysts Should Run
Use this Data Analyst data validation checklist to catch duplicates, NULLs, bad joins, date gaps, category errors, outliers and reconciliation problems before reporting.
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Cohort Analysis for Data Analysts: A Practical Guide
Learn cohort analysis for Data Analysts, including cohort definition, retention tables, time-based behavior and common data-quality mistakes.
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KPI vs Metric: How Data Analysts Should Define the Right Measure
Understand the difference between a KPI and a metric, and learn how Data Analysts can define measures with clear formulas, owners, targets and business…
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Root Cause Analysis for Data Analysts: How to Find Why a Metric Changed
Learn a practical root cause analysis process for Data Analysts: baseline the metric, validate data quality, segment the change and test causal explanations.
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Data Analyst Career: Build Evidence Instead of Collecting More Courses
Stop collecting courses and start building evidence. This Data Analyst career system shows how to turn projects into proof of technical and business skills.
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What Hiring Managers Look for in a Data Analyst Candidate
What hiring managers look for in Data Analyst candidates: problem framing, data judgment, technical fluency, communication, ownership and responsible AI use.
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Data Analyst Interview Case Study: A Framework for Solving Business Problems
A practical Data Analyst case interview framework: clarify the decision, define the metric, structure the investigation, validate evidence and make a recommendation.
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How to Stand Out in Data Analyst Job Applications
How to stand out in Data Analyst applications by turning skills into evidence, tailoring proof to the job, and making projects interview-ready.
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Data Analyst Career Roadmap 2026: The Skills That Actually Matter
A practical 2026 Data Analyst career roadmap covering core technical skills, business thinking, AI-assisted workflows, portfolio evidence and job readiness.
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Should a Data Analyst Use AI for Every Task? A Practical Decision Framework
AI is useful enough that the temptation is to use it for everything. That is not the same as using it well. Use AI…
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