Data Analyst
Feb 14, 2026
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3 min read
10 queries separating average analysts from dangerous ones
Feb 7, 2026
2 min read
Why legends are lazy design (and what to do instead)
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Why direct labeling beats tooltips every single time
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Why starting at zero isn't optional anymore
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The 60-30-10 rule that separates pros from amateurs
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data fails when responsibility is unclear
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why 2025 analysts need more than formulas
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Data Science
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Data Science Pain Points
How to Save Hours and Still Deliver Better Insights
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📱 Read This if You Want a Data Job (But Not the Typical One)
High accuracy can still mean a terrible model.
Repetitive plotting wastes your time. Here’s how to fix it.
Training is step one. Deploying is the real challenge.
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If it’s hard to read or hard to find, it’s not helping anyone.
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Start learning Big Data on YouTube—these top channels make it simple, even for beginners.