Data & Analytics · Data Analytics / Business Intelligence
Data Analyst
Turn raw data into answers people actually act on — queries, dashboards, and plain-English findings.
What you actually do
You take messy raw data and turn it into answers: write SQL queries to pull it, clean it until it's trustworthy, build dashboards in Power BI or Tableau, and explain what the numbers mean in plain language to people who will make decisions with them. It's one of the most accessible, least code-heavy on-ramps into modern IT — SQL plus strong spreadsheet skills is the realistic baseline, and junior roles rarely gate on a degree. The craft is half technical, half communication: the query is worthless if nobody understands the answer.
- •Write a SQL query to pull last quarter's numbers, then spend twice as long cleaning duplicates and mismatched categories
- •Update the weekly KPI dashboard and chase down why one metric moved before someone asks
- •Turn a vague request ('why are sales down?') into a specific, answerable question
- •Walk a manager through a chart in plain English — what it says, what it doesn't, and what's still uncertain
- •Document where a dataset came from and what you did to it, so the analysis can be trusted and repeated
What the work feels like
This fits you if…
- ✓You like finding the story hiding in a pile of numbers
- ✓You're patient with tedious cleanup because you know the answer depends on it
- ✓You enjoy explaining things simply as much as figuring them out
Less ideal if…
- −You want deep programming work — this is queries and tools, not software engineering
- −Vague, shifting requests frustrate you more than they intrigue you
- −You'd rather not present or defend your findings to non-technical people
Skills to build
Your roadmap
An ordered path from beginner to employable. Certifications are optional checkpoints, not the point — skills and a portfolio are.
- 1Master the baseline
Get genuinely good at Excel (pivot tables, lookups) and learn SQL — free via SQLBolt, Mode's SQL tutorial, or Khan Academy. Practice on real public datasets, not toy ones.
- 2Learn a BI tool and basic statistics
Pick Power BI (bigger corporate footprint) or Tableau and rebuild your spreadsheet analyses as dashboards. Learn enough statistics to know when a difference actually means something. The Google Data Analytics Certificate can structure this whole stage.
Study guide: Google Data Analytics Certificate → - 3Build a public portfolio
Publish two or three end-to-end projects — messy source data, documented cleaning, a dashboard, and a short plain-English writeup. This is what gets junior interviews; add PL-300 if you're targeting Microsoft/Power BI shops.
Study guide: Microsoft PL-300 (Power BI Data Analyst) → - 4Land the first role and pick a direction
Apply broadly — reporting, operations, and BI analyst titles are all this job. Once in, add Python (pandas) to grow toward data engineering, or go deeper on the business side toward analytics or BA leadership.
Certifications for this path
A facet of the path, not the path itself — skills and a portfolio come first.
A guided, project-based introduction that doubles as portfolio material — the gentlest structured start.
View study guide →The recognized, exam-backed credential for BI/analyst roles in Microsoft shops.
View study guide →The ~2-month sequel to Data Analytics — pipelines and stakeholder dashboards, the skills behind the better-paid BI-analyst tier.
View study guide →Try it this weekend
Do the actual job in miniature: take a real public dataset, clean it, find one genuine insight, and state it in one plain sentence.
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