Data Analyst

Overview

Data analysts are business detectives who use data to help companies make better decisions and solve problems.

Description

Collect data from different sources.
Clean up messy data by removing unnecessary information.
Look for patterns and trends in data.
Record analysis results in detailed reports.
Set up and manage databases and data systems.

Entry requirements

A Bachelor's Degree in Computer Science or related fields is preferred.
If you want a head start, download public datasets from data.gov.sg and practise finding insights. It’s a great way to build your skills early.

Salary

$4,200 – 7,900

/mo

Salary ranges are estimated based on public information found on Singaporean job portals, including MyCareersFuture, MySkillsFuture. Updated as of 2026.

Trivia

Data analysts are needed in almost every industry. You can work anywhere, in areas like market research, finance, healthcare and more.

What to expect

You'll constantly adapt to changing trends and new technologies, so expect lots of learning on the job.

Soft
skills

Communication

Able to explain data findings clearly through reports or presentations.

Critical Thinking & Problem-Solving

Ability to critically assess data, identify patterns, and question assumptions to find solutions.

Adaptability & Creativity

Flexibility with new tools and innovative ways to analyse and present data.

Analytics Software

Skilled in data analysis software tools including Excel, SAS, SPSS, R and Python libraries.

Programming Languages

Familiarity with and ability to use Python, R, or SQL for managing data, conducting statistical analysis, and automating processes.

Data Visualisation

Ability to turn data into easy-to-understand visual presentations using tools like Tableau or Power BI.

Hard
skills

"I transform data into a clean visible dashboard, allowing clients to make decisions fast and easily."

Joey Tay, Data and Analytics Consultant

NCS Group

Frequently asked questions

Yes. You'll need to be comfortable working with numbers and spotting trends in data.

Knowing popular programming languages like Python and SQL is useful.

They may remove the missing data or predict it, depending on the situation. They also check how it affects the results and record what they did.

They use various tools, especially Microsoft Excel and Google Sheets.