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How to become a Data Analyst

Data Analyst

Career progression and salary expectations for a Data Analyst

1

Junior Data Analyst

| Up to £25,000
2

Data Analyst

| Up to £35,000
3

Analytics Manager

| Up to £65,000

The role of a Data Analyst

Want to work with numbers? Big fan of ‘big data’? Actually know what ‘big data’ is? You should be a Data Analyst…

Whether it’s sales figures, audience demographics and market research, or keeping track of their overheads, every business, regardless of size, records some kind of data. It’s a Data Analysts' job to interpret this data, find trends, and present this back for this business to act upon.

Typical duties for a Data Analyst could include:

  • Gathering and extracting numerical data
  • Interpreting the numbers
  • Finding trends, patterns and algorithms within the data
  • Analysing market research
  • Presenting these findings back to the business to help inform future decisions
To be successful as a Data Analyst, a high level of statistical literacy and a natural flair for mathematics are key. You will be expected to work at a fast pace, and be able to keep calm under pressure, especially when working to tight deadlines.

Other key skills include:

  • Highly developed organisational skills
  • Pattern recognition
  • Critical and analytical thinking
  • Ability to extract and interpret relevant data
  • Excellent attention to detail
"If you love being surrounded by numbers like me, becoming a Data Analyst is the perfect career move for you. If you struggle using a calculator, you may not be quite as happy. Poring over difficult data can be long and laborious at times, but when you identify a trend or see a pattern in the numbers for the first time, it makes all the frustration worthwhile. And even better, I know my work directly impacts what the company decides to do moving forward. How many people can say that?"

Get qualified as a Data Analyst

To become a Data Analyst, you'll usually need a degree in a relevant field such as mathematics, statistics, computer science, economics, or data science. Proficiency in data analysis tools and programming languages is also crucial for success. Commonly used tools include Microsoft Excel, SQL for database querying, and programming languages like Python or R are useful for data manipulation and analysis. Employers may also look for experience with data visualisation tools such as Tableau or Power BI.

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