Data analysis in 4 steps.
Most people only know the last one.
1. Collect Before any analysis, you need data.
Not perfect data. Not clean data.
The quality of everything that follows depends on what you collect here.
2. Analyze This is where the real work happens.
You clean the mess.
You run the queries. You build the pivot tables.
You ask questions of the data — and let it talk back.
Most beginners want to skip straight here without doing step 1 properly.
Don’t.
3. Summarize Raw findings mean nothing to most people.
Your job is to turn complexity into clarity.
One chart that tells the story beats 10 slides full of numbers.
This is where good analysts separate themselves from great ones.
4. Decide This is the whole point.
Every dataset you clean, every dashboard you build — it exists to help someone make a better decision.
Not to look impressive.
Not to show off your SQL skills.
To drive action.
The companies paying top salaries aren’t paying for analysis.
They’re paying for the decision that follows it.
Master all 4 steps.
Not just the one that looks good on a CV.
Sending a generic resume is like throwing a bottle into the ocean — no direction, no impact.
Most resumes fail because companies use ATS to filter applications.
Wrong keywords, bad formatting, one-size-fits-all = instant rejection.
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Makes it fully ATS-friendly
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A generic one gets filtered out.
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♻️ Repost to help someone who’s been skipping steps.