How to Become a Data Analyst: Skills, Salary, and Career Path

Data AnalystUpdated February 2026

Data analysts are in high demand, and the role is one of the most accessible entry points into tech. If you are comfortable with spreadsheets and enjoy finding patterns in information, you might already have the foundation for a data analytics career.

Key Facts

SQL alone can land you a junior data analyst role
No degree required - certifications like Google Data Analytics are recognized
3-6 months of focused learning to become job-ready
Remote data analyst positions are common

What Data Analysts Actually Do

Clean and organize datasets
Create visualizations and dashboards
Write reports that inform business decisions
Query databases using SQL
Collaborate with stakeholders to define metrics

Essential Skills

Must-Have:

SQL (learn in 4-8 weeks)
Excel/Google Sheets (advanced level)
Data visualization (Tableau or Power BI)
Basic statistics

Nice-to-Have:

Python or R
A/B testing knowledge
Domain expertise in any industry

Who Makes a Good Data Analyst?

Accountants and finance professionals
Marketing specialists used to metrics
Operations managers who track KPIs
Anyone who loves spreadsheets

Salary Expectations

Entry level: $55K-70K
Mid-level: $70K-95K
Senior: $95K-130K

Career Advice

Build a portfolio of data projects using real datasets from Kaggle or government open data. Analyze something you are genuinely curious about - that enthusiasm shows in interviews. Use CareerCheck to see how your current skills map to data analyst job requirements.

Frequently Asked Questions

Do I need to know programming?

Not necessarily. Many data analysts primarily use SQL and visualization tools. Python is a bonus but not required for entry-level roles.

Is data analyst a good first tech job?

Yes. It is less technical than data science or engineering, has strong demand, and provides a foundation to specialize later.

What is the difference between data analyst and data scientist?

Data analysts focus on describing what happened using existing data. Data scientists build predictive models and often need more advanced programming and statistics.