Data course
Data as a practice
- FoundationsLanguage, formats, and habits that make data useful.
- IntermediateModels, pipelines, and analytics for reliability.
- AdvancedArchitecture, streaming, and governance at scale.
- SummaryRecap, scenarios, and playful practice.
CPD timing
Time estimate (transparent)
I publish time estimates because CPD needs to be defensible. The goal is honesty, not marketing.
Guided learning
30h
Core levels, structured learning
Practice and consolidation
3h
Summary, drills, revisits
Notional range
16 to 40 hours
Quick: core concepts + one exercise per module. Standard: exercises + reflections for CPD evidence. Deep: extra drills and portfolio artefacts.
How I estimate time
I use a notional learning hours approach and I keep the assumptions visible. Where modules are content heavy, I add practice so the hours are earned, not claimed.
- Reading: 225 words per minute, multiplied by 1.3 for note taking and checking understanding.
- Labs and practice: about 15 minutes per guided activity, including at least one retry.
- Reflection for CPD: about 8 minutes per module for a short defensible note and evidence link.
- Assessments: about 1.4 minutes per question for reading, thinking, and review.
If you study faster or slower, your hours will differ. What matters is that the method is consistent and the activities are real.
Assessment and practice assessment
Data assessment blueprint (planned)
The data assessment system will be built as a mix of reasoning, design critique, and evidence artefacts. Until then, checkpoints and labs remain the practice loop.
Design rules
- Assessment must include at least one artefact output, for example a small schema with constraints and a data quality plan.
Standards and certifications
The map we anchor to
I map each course to reputable standards so your learning is defensible at work. I also show common certifications and how their language differs.
Important: This content aligns with these standards and certifications for learning purposes. This is guidance, not endorsement. We are not affiliated with certification providers unless explicitly stated.
Primary anchor standards
- DAMA-DMBOK (data management framework)DAMA
A widely recognised umbrella for data management domains and governance roles.
Official reference - UK GDPR and ICO guidance (where privacy matters)UK ICO
Data work often touches personal data. The course must teach safe, lawful thinking, not just pipelines.
Official reference
Certification routes
This course is not endorsed by certification bodies. It is built to prepare you honestly, including where exams simplify reality.
- practitionerVendor data certifications (Azure, AWS, Google)Major cloud vendors
A practical extension for people building data platforms and analytics systems.
Organisations and resources
These are the kinds of organisations professionals reference. If you learn how to use them properly, you become harder to mislead.
- DAMA
What it is: A global community and framework for data management.
Why it matters: It gives shared vocabulary for governance roles and domains.
- ICO
What it is: The UK Information Commissioner’s Office.
Why it matters: It sets expectations for privacy compliance and good practice in the UK.
Terminology translation
Data governance and quality basics
Data problems are rarely technology problems first. They are ownership, meaning, and evidence problems.
Data owner versus steward versus custodian
Plain English
Owner is accountable. Steward makes it usable and defined. Custodian operates the platform and controls access.
How standards use it
DAMA-DMBOK
Formalises governance roles and responsibilities so accountability is explicit.
Common mistake
Making data owner a job title with no authority and no time.
My take
If ownership has no power, it is theatre.
Quick check
Who should approve a definition change for a critical metric and why?
Metadata, lineage, provenance
Plain English
Metadata describes data. Lineage shows where it came from and where it went. Provenance is the evidence trail of how it was produced.
How standards use it
Data governance practice
A consistent distinction that helps teams debug and trust outputs.
Common mistake
Treating a catalogue as governance itself.
My take
A catalogue is a map. It is not the law.
Quick check
What question does lineage answer that metadata does not?
Anonymisation versus pseudonymisation
Plain English
Anonymised means you cannot get back to a person. Pseudonymised means you can, if you hold the key.
How standards use it
UK GDPR and ICO guidance
These are treated very differently in risk and obligations. Many teams misuse the terms.
Common mistake
Saying we anonymised it when you only hashed an identifier.
My take
Hashing is not magic. It is maths. Attackers can do maths too.
Quick check
You keep a lookup table that can re-identify someone. What is that called and what control protects it?
📊Core path
Data Foundations
Start with the language, formats, and habits that make data useful across teams.
Applied Data
Models, pipelines, and analytics that keep data reliable and ready for use.
Advanced Data Systems
Architecture, streaming, governance, and data products at scale.
Summary and games
Recap, scenarios, and playful practice for the data course.
Mapping
How this course stays defensible
This links the same four things CPD reviewers care about: what you learn, how you practise, how you are assessed, and what evidence you can show.
- DAMA-DMBOK (data management framework) (DAMA)
- UK GDPR and ICO guidance (where privacy matters) (UK ICO)
Vocabulary, formats, and basic quality reasoning.
Schemas, pipelines, and trust signals.
Architecture, governance, and trade-offs at scale.
Coverage matrix
Module-level coverage
This matrix makes the course defensible: each module is tied to an outcome focus, the anchor standards, and the evidence you can produce.
| Level | Module | Outcome focus | Domains | Alignment | Assessment | Evidence |
|---|---|---|---|---|---|---|
| Foundations | What Is Data data-foundations-what-is-data Anchors: DAMA-DMBOK (data management framework), UK GDPR and ICO guidance (where privacy matters) | Define what data is in context and what makes it useful for decisions. | contracts | Other: Definitions and shared meaning | Practice assessment | Template + rubric |
| Foundations | Representation And Formats data-foundations-representation-and-formats Anchors: DAMA-DMBOK (data management framework), UK GDPR and ICO guidance (where privacy matters) | Choose formats and representations that reduce ambiguity and errors. | contracts | Other: Interoperability | Practice assessment | Template + rubric |
| Foundations | Quality And Meaning data-foundations-quality-and-meaning Anchors: DAMA-DMBOK (data management framework), UK GDPR and ICO guidance (where privacy matters) | Identify quality risks and define what 'good' means with thresholds and actions. | quality | Other: Quality and trust | Practice assessment | Template + rubric |
| Foundations | Lifecycle And Flow data-foundations-lifecycle-and-flow Anchors: DAMA-DMBOK (data management framework), UK GDPR and ICO guidance (where privacy matters) | Explain data lifecycle and flow to prevent silent breakage and confusion. | monitoring | Other: Lineage and lifecycle | Practice assessment | Template + rubric |
| Foundations | Roles And Responsibilities data-foundations-roles-and-responsibilities Anchors: DAMA-DMBOK (data management framework), UK GDPR and ICO guidance (where privacy matters) | Assign ownership and responsibilities to keep data reliable and accountable. | governance | Other: Ownership and stewardship | Practice assessment | Template + rubric |
| Foundations | Ethics And Trust data-foundations-ethics-and-trust Anchors: DAMA-DMBOK (data management framework), UK GDPR and ICO guidance (where privacy matters) | Handle privacy, bias, and trust risks with clear classification and minimisation. | privacy | Other: Ethics and privacy | Practice assessment | Template + rubric |
| Intermediate | Architectures And Pipelines data-intermediate-architectures-and-pipelines Anchors: DAMA-DMBOK (data management framework), UK GDPR and ICO guidance (where privacy matters) | Design pipelines and change control that reduce blast radius and brittleness. | evolution, monitoring | Other: Pipelines and evolution | Practice assessment | Template + rubric |
| Intermediate | Governance And Stewardship data-intermediate-governance-and-stewardship Anchors: DAMA-DMBOK (data management framework), UK GDPR and ICO guidance (where privacy matters) | Build governance that enables autonomy within guardrails, with evidence. | governance | Other: Governance and stewardship | Practice assessment | Template + rubric |
| Intermediate | Interoperability And Standards data-intermediate-interoperability-and-standards Anchors: DAMA-DMBOK (data management framework), UK GDPR and ICO guidance (where privacy matters) | Use standards and contracts to reduce integration friction and misalignment. | contracts | Other: Interoperability and standards | Practice assessment | Template + rubric |
| Intermediate | Analysis And Insight data-intermediate-analysis-and-insight Anchors: DAMA-DMBOK (data management framework), UK GDPR and ICO guidance (where privacy matters) | Avoid misleading analysis by validating definitions, aggregations, and assumptions. | metrics | Other: Decision quality | Practice assessment | Template + rubric |
| Intermediate | Risk Ethics Strategic Value data-intermediate-risk-ethics-strategic-value Anchors: DAMA-DMBOK (data management framework), UK GDPR and ICO guidance (where privacy matters) | Make trade-offs explicit: privacy, risk, and strategic value without theatre. | privacy, governance | Other: Risk and ethics | Practice assessment | Template + rubric |
| Advanced | Math Foundations data-advanced-math-foundations Anchors: DAMA-DMBOK (data management framework), UK GDPR and ICO guidance (where privacy matters) | Use core maths ideas to avoid false certainty and misinterpretation. | modelling | Other: Statistical reasoning | Practice assessment | Template + rubric |
| Advanced | Models And Abstraction data-advanced-models-and-abstraction Anchors: DAMA-DMBOK (data management framework), UK GDPR and ICO guidance (where privacy matters) | Choose abstraction levels (models, schemas) that match the decision and constraints. | modelling | Other: Modelling and abstraction | Practice assessment | Template + rubric |
| Advanced | Analytics And Inference data-advanced-analytics-and-inference Anchors: DAMA-DMBOK (data management framework), UK GDPR and ICO guidance (where privacy matters) | Interpret inference and causality limits; avoid overclaiming from correlations. | metrics | Other: Inference discipline | Practice assessment | Template + rubric |
| Advanced | Platforms And Distributed data-advanced-platforms-and-distributed Anchors: DAMA-DMBOK (data management framework), UK GDPR and ICO guidance (where privacy matters) | Design data platforms with reliability, cost, and operational constraints at scale. | tradeoffs, monitoring | Other: Platforms and scale | Practice assessment | Template + rubric |
| Advanced | Governance Regulation data-advanced-governance-regulation Anchors: DAMA-DMBOK (data management framework), UK GDPR and ICO guidance (where privacy matters) | Meet regulatory needs with minimisation, classification, and auditable lineage. | privacy, lineage | Other: Regulation and evidence | Practice assessment | Template + rubric |
| Advanced | Strategic Asset data-advanced-strategic-asset Anchors: DAMA-DMBOK (data management framework), UK GDPR and ICO guidance (where privacy matters) | Treat data as an asset with decision rights, investment logic, and evidence of value. | governance | Other: Strategic value | Practice assessment | Template + rubric |
| Summary | Recap data-summary-recap Anchors: DAMA-DMBOK (data management framework), UK GDPR and ICO guidance (where privacy matters) | Recap the core ideas: definitions, quality, ownership, and why trust matters. | contracts, quality | Other: Consolidation and recall | Formative checkpoints | Template + rubric |
| Summary | Scenarios data-summary-scenarios Anchors: DAMA-DMBOK (data management framework), UK GDPR and ICO guidance (where privacy matters) | Practise scenario judgement on quality failures, governance trade-offs, and reliability. | quality, governance, monitoring | Other: Scenario judgement | Formative checkpoints | Template + rubric |
| Summary | Games And Labs data-summary-games-and-labs Anchors: DAMA-DMBOK (data management framework), UK GDPR and ICO guidance (where privacy matters) | Reinforce patterns using small labs and drills that create evidence. | evidence | Other: Practice and evidence | Formative checkpoints | Template + rubric |
| Summary | Connections data-summary-connections Anchors: DAMA-DMBOK (data management framework), UK GDPR and ICO guidance (where privacy matters) | Connect data decisions to adjacent areas (security, architecture, AI) with correct trade-offs. | tradeoffs | Other: Cross-domain reasoning | Formative checkpoints | Template + rubric |
| Summary | Next Steps data-summary-next-steps Anchors: DAMA-DMBOK (data management framework), UK GDPR and ICO guidance (where privacy matters) | Set a next-steps plan that keeps improvements practical and auditable. | governance | Other: Next steps and operating model | Formative checkpoints | Template + rubric |
🛠️Further practice
Hands-on labs and tools to make data concepts concrete.
📚CPD
Log minutes as you study and practise. Your records stay in this browser. Use the export view when you need a clean summary for your CPD system.