M01
What data is and why it matters
Data starts as recorded observations, for example numbers on a meter, text in a form, or pixels in a photo.
Course summary
Data
Use this page to revisit what each stage gives you and return to the exact weak point that needs another pass.
Stage 1 of 3
Data Foundations
Start with the language, formats, and habits that make data useful across teams.
M02
Data, information, knowledge, judgement
I want a simple model in your head that stays useful even when the tools change, and DIKW works because it forces you to separate raw observations from meaning before.
M03
Units, notation, and the difference between percent and probability
Data work goes wrong when people are casual about units.
M04
Data representation and formats
Computers store everything using bits (binary digits) because hardware can reliably tell two states apart.
M05
Standards, schemas, and interoperability
Interoperability is a boring word for a very expensive problem.
M06
Open data, data sharing, and FAIR thinking
Open data is not “everything on the internet”.
M07
Visualisation basics (so charts do not lie to you)
Visualisation is part of data literacy.
M08
Data quality and meaning
Quality means data is accurate (close to the truth), complete (not missing key pieces), and timely (fresh enough to be useful).
M09
Data lifecycle and flow
Data starts at collection, gets stored, processed, shared, and eventually archived or deleted.
M10
Data roles and responsibilities
Roles exist so someone is accountable for quality, access, and change.
M11
Foundations of data ethics and trust
Ethics matters from the first data point.
Stage 2 of 3
Data Intermediate
Move into models, pipelines, and applied analytics while keeping reliability in view.
M01
Data architectures and pipelines
Data architecture is how data is organised, moved, and protected across systems.
M02
Data governance and stewardship
Governance is agreeing how data is handled so people can work quickly without being reckless.
M03
Interoperability and standards
Interoperability means systems understand each other.
M04
Data analysis and insight generation
Analysis is asking good questions of data and checking that the answers hold up.
M05
Probability and distributions (uncertainty without the panic)
Data work is mostly uncertainty management.
M06
Inference, sampling, and experiments
Inference is the art of learning about a bigger reality from limited observations.
M07
Modelling basics (regression, classification, and evaluation)
Modelling is not magic.
M08
Data as a product (making datasets usable, not just available)
A mature organisation treats important datasets like products.
M09
Risk, ethics and strategic value
Data risk is broader than security.
Stage 3 of 3
Data Advanced
Join up data architecture, streaming, governance, and product thinking for real systems.
M01
Mathematical foundations of data systems
Maths in data systems describes patterns, uncertainty, and change.
M02
Data models and abstraction at scale
Models are simplified representations of reality.
M03
Advanced analytics and inference
Inference is about drawing conclusions while admitting uncertainty.
M04
Data platforms and distributed systems
Data systems distribute to handle scale and resilience.
M05
Governance, regulation and accountability
Regulation exists to protect people and markets.
M06
Data as a strategic and economic asset
Data creates value when it improves decisions, products, and relationships.