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The short answer
A data analyst collects, cleans and interprets data to help an organisation make decisions. No single degree is required. People come from statistics, mathematics, computer science, commerce, economics and other fields, and what employers look at is whether you can do the work: working with spreadsheets, writing database queries, using a reporting or visualisation tool, understanding basic statistics and explaining findings clearly. A degree helps, but a small portfolio of real projects often counts for more at entry level. Plan the route around building those skills, and be careful with courses that promise a guaranteed job.
Admission rules change; confirm with the official notice.
The basics
Typical entry
Degree plus practical skills
Subjects vary widely.
Core tools
Spreadsheets, SQL, a reporting tool
Programming in Python or R is common.
Core knowledge
Basic statistics and clear communication
Interpreting data matters as much as handling it.
Proof of skill
Projects and a portfolio
Internships help.
Related roles
Business analyst, reporting analyst, data scientist
Each has different requirements.
The skills to build, in order
Start with the basics and add one layer at a time.
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SpreadsheetsStart here
Cleaning data, formulas, summaries and charts. Many entry-level roles use these every day.
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Databases and SQLHigh value
Retrieving and combining data from tables with queries. This is one of the most requested skills.
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StatisticsFoundation
Averages, variation, sampling, correlation and basic testing, so you know what a result does and does not show.
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Visualisation and reportingCommunicate
Building clear dashboards and reports in a business intelligence tool.
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ProgrammingNext layer
Python or R for larger datasets and automation. Useful for growth, even if not needed on day one.
A practical plan
- Choose a degree or current course that gives you some quantitative base, such as maths, statistics, computer applications or commerce. If you already have a degree in another field, you can still start with skills.
- Learn the basics through free and low-cost material from reputed sources, and practise on real, public datasets.
- Build three to five small projects that answer a clear question and explain your method and findings in plain words.
- Share the work in a simple portfolio or code repository, and write a short summary of each project.
- Apply for internships, junior analyst and reporting roles, and be ready to discuss your projects in interviews.
- Keep learning one new skill at a time, and revisit your plan every few months.
Where data analysis fits among your options
Data analysis overlaps with technology, commerce and science. Careers for students interested in data describes the wider field, and careers for students interested in AI covers the neighbouring area of machine learning.
If you are changing fields, moving into tech from a non-tech background and upskilling after graduation cover how to learn alongside work or study. For a degree route, see BCA course explained.
Frequently asked questions
Official sources to check
Use these for free learning material and current job listings. We do not endorse any private course.
