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EXPLORE A CAREER FIELD

Data and AI

Turn information into insights and smarter decisions.

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CodingmoderateTechnical depthhighPeople interactionmoderateCreative workmoderateBusiness focushigh

What is this field really about?

Data teams organise information, ask useful questions and communicate what the evidence shows. Some build predictive models; many focus on spreadsheets, reports and data quality.

Why it exists

Help people make better decisions using trustworthy evidence.

A typical day might include

  • Clean and check datasets
  • Explore patterns and build reports
  • Explain uncertainty and findings

One field. Different roles.

A field is a broad area of work. These roles are specific occupations within it; their responsibilities and coding needs vary.

Data analyst

Uses data to answer questions and communicate useful findings.

  • Clean and explore datasets
  • Build reports and explain limitations

Analytics engineer

Turns raw data into reliable, reusable datasets for analysis.

  • Transform and test data models
  • Document shared metric definitions

Machine learning engineer

Builds and operates software that uses trained predictive models.

  • Evaluate model behaviour
  • Monitor model and data changes

What makes it rewarding

  • Connect analysis to real decisions
  • Start experimenting with small public datasets

The realistic challenges

  • Messy data takes time to prepare
  • AI-focused roles often need deeper mathematics

Possible ways in

  • Start with spreadsheets and a small analysis project
  • Consider a statistics, analytics or computing course
TRY IT BEFORE YOU COMMIT

Explore your weekly habits

Analyse a small fictional dataset of time spent on daily activities.

  1. Create a 20-row spreadsheet of fictional activities
  2. Check missing values and calculate totals
  3. Make one chart and explain its limits

You’ll finish with: A chart with a clear finding and a note about uncertainty.

FROM CURIOUS TO CONFIDENT

Your beginner roadmap

01

Understand

  • Data teams organise information, ask useful questions and communicate what the evidence shows. Some build predictive models; many focus on spreadsheets, reports and data quality.
  • Learn: Spreadsheets and SQL, Statistics basics, Critical thinking.
  • Read an introductory guide from the resource list below.
02

Practise

  • Explore your weekly habits
  • Create a 20-row spreadsheet of fictional activities
  • Check missing values and calculate totals
  • Make one chart and explain its limits
03

Demonstrate

  • Document your project and how you approached it.
  • Share a portfolio page or GitHub repository where appropriate.
  • Explain one decision, one limitation and one lesson.
04

Prepare

  • Explore entry titles such as Data analyst.
  • Compare three job descriptions and note recurring skills.
  • Practise explaining spreadsheets and sql to a beginner.
  • Find a professional community and read its beginner discussions.

Start learning: Microsoft Learn: Data training ↗

Seven days to explore

A small activity each day. Take longer whenever you need.

  1. Day 1Explore what data & ai teams do and note one question.
  2. Day 2Learn the basics of spreadsheets and sql.
  3. Day 3Plan your project: explore your weekly habits.
  4. Day 4Create a 20-row spreadsheet of fictional activities
  5. Day 5Check missing values and calculate totals
  6. Day 6Make one chart and explain its limits
  7. Day 7Document what you learned and decide whether to explore further.

Keep exploring

Content reviewed: 2026-09-14. Learning routes are possibilities, not employment guarantees.