Data

Learn how data becomes trustworthy enough to support decisions, with clear treatment of source context, structure, quality, privacy, and reuse.

This course is for people who need to move from spreadsheet folklore to reliable data judgement. By the end, you should be able to explain where data breaks down, what governance actually requires, and how to make a pipeline defensible.

12 hours4 stages, 26 modulesBeginners welcomeFree, no account
Start with Module 1
Analytics dashboard - photo by Carlos Muza on Unsplash

What you will learn

  • Explain why data matters to outcomes and how different roles collaborate around data
  • Recognise common data formats and describe how they shape modelling and storage choices
  • Sketch pipeline steps from ingestion through integration to analytics
  • Plan for data quality, testing and trust signals in real data flows
  • Compare warehouse and lakehouse approaches and describe how streaming influences design
  • Relate data governance and stewardship models to scaling teams and regulatory duties

Who this course is for

  • People who want a grounded view of data without heavy theory.
  • Teams linking data work to AI, cybersecurity, or digital delivery.
  • Lecturers and leaders who want concise, reusable explanations and practice activities.

Prerequisites: None. Course starts from everyday examples and builds up.

Course curriculum

Read the modules in order on the first pass. Use the practice and stage tests when you want a stricter check on what stuck.

1

Stage 1. Data Foundations

11 modules · 4 hours

1What data is15 min2DIKW hierarchy15 min3Units, notation, and binary basics30 min4Data representation and formats30 min5Standards and interoperability30 min6Open data and FAIR as a guiding lens15 min7Visualisation basics15 min8Data quality and meaning30 min9Data lifecycle and flow15 min10Roles and responsibilities15 min11Ethics and trust30 minPracticePractice testUntimed
2

Stage 2. Applied Data

9 modules · 3 hours

12Architectures and pipelines30 min13Governance and stewardship30 min14Interoperability and standards15 min15Analysis and insight15 min16Probability and distributions30 min17Inference and experiments15 min18Data modelling basics15 min19Data as a product15 min20Risk, ethics, and strategic value15 min
3

Stage 3. Data Practice and Strategy

6 modules · 4 hours

21Mathematical foundations for data30 min22Models, abstraction, and databases30 min23Advanced analytics and inference30 min24Platforms and distributed systems30 min25Governance, regulation, and compliance30 min26Data as a strategic asset30 min
4

Data summary and games

1 hour

A recap and playful space to connect data practice with real scenarios and next steps.

Standards and references

This course is aligned to the following standards, frameworks, and certification objectives.

  1. 1DAMA DMBOK 2 (Data Management Body of Knowledge, 2nd Edition)
  2. 2ISO/IEC 11179 metadata registries
  3. 3ISO/IEC 27701:2025 privacy information management
  4. 4ICO data protection principles and UK GDPR guidance
  5. 5ISO 8000 Data quality
  6. 6FAIR principles (used as a guiding lens for sharing and reuse, not a formal standard)
  7. 7GOV.UK Data Quality Issues Framework
  8. 8ISO/IEC 9075 SQL standards
  9. 9Data Mesh principles (used as an architectural lens, not a standard)