Data course
Data as a practice
Follow the core path in order. Foundations, applied, practice and strategy, then a short summary with games and practice.
- FoundationsLanguage, formats, and habits that make data useful.
- AppliedModels, pipelines, and analytics for reliability.
- Practice & StrategyArchitecture, streaming, and governance at scale.
- SummaryRecap, scenarios, and playful practice.
What you will learn
Overview
Data work looks simple until quality, trust, and accountability fail. This route moves you from definitions to reliable practice.
Your progress
0%0 of 31 sections complete
Time estimate
Data quality improves with patience and clear definitions, not speed.
Data to decision loop
A practical sequence for trustworthy data work
Reuse this loop in projects so insight stays tied to evidence.
Rendering diagram...
📊Core path
Data Foundations
Start with the language, formats, and habits that make data useful across teams.
Applied Data
Move into models, pipelines, and applied analytics while keeping reliability in view.
Data Practice and Strategy
Join up data architecture, streaming, governance, and product thinking for real systems.
Summary and games
Recap, scenarios, and playful practice for the data course.
Getting started
How to use this course
How to use this data course
Keep each session focused on one real decision, then test it.
- 1
Complete the core path in order
Start at foundations so methods and terminology build correctly.
- 2
Read, then test with one tool
Run a practical check immediately after each concept.
- 3
Write units and definitions before changing data
This prevents avoidable errors during analysis and reporting.
- 4
Use practice assessments before timed attempts
Rehearse judgement first so final attempts reflect your true capability.
Hands-on
Quick practice
Quick check
Checkpoint
2 questions
For auditors and CPD
Reference and standards
These panels are for CPD defensibility, standards alignment, and audit evidence. Most learners can skip these entirely and return when they need formal documentation.
Show reference panels7 sections · timing, artefacts, assessment, terminology, standards, mapping, coverage
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.
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.
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), ISO/IEC 11179 metadata registry, 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 | Dikw data-foundations-dikw Anchors: DAMA-DMBOK (data management framework), ISO/IEC 11179 metadata registry, UK GDPR and ICO guidance (where privacy matters) | Data reasoning, definitions, and reliability | - | - | Practice assessment | Template + rubric |
| Foundations | Units And Notation data-foundations-units-and-notation Anchors: DAMA-DMBOK (data management framework), ISO/IEC 11179 metadata registry, UK GDPR and ICO guidance (where privacy matters) | Data reasoning, definitions, and reliability | - | - | Practice assessment | Template + rubric |
| Foundations | Representation And Formats data-foundations-representation-and-formats Anchors: DAMA-DMBOK (data management framework), ISO/IEC 11179 metadata registry, 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 | Standards And Interoperability data-foundations-standards-and-interoperability Anchors: DAMA-DMBOK (data management framework), ISO/IEC 11179 metadata registry, UK GDPR and ICO guidance (where privacy matters) | Data reasoning, definitions, and reliability | - | - | Practice assessment | Template + rubric |
| Foundations | Open Data And Fair data-foundations-open-data-and-fair Anchors: DAMA-DMBOK (data management framework), ISO/IEC 11179 metadata registry, UK GDPR and ICO guidance (where privacy matters) | Data reasoning, definitions, and reliability | - | - | Practice assessment | Template + rubric |
| Foundations | Visualisation Basics data-foundations-visualisation-basics Anchors: DAMA-DMBOK (data management framework), ISO/IEC 11179 metadata registry, UK GDPR and ICO guidance (where privacy matters) | Data reasoning, definitions, and reliability | - | - | Practice assessment | Template + rubric |
| Foundations | Quality And Meaning data-foundations-quality-and-meaning Anchors: DAMA-DMBOK (data management framework), ISO/IEC 11179 metadata registry, 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), ISO/IEC 11179 metadata registry, 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), ISO/IEC 11179 metadata registry, 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), ISO/IEC 11179 metadata registry, 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 |
| Summary | Recap data-summary-recap Anchors: DAMA-DMBOK (data management framework), ISO/IEC 11179 metadata registry, 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), ISO/IEC 11179 metadata registry, 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), ISO/IEC 11179 metadata registry, 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), ISO/IEC 11179 metadata registry, 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), ISO/IEC 11179 metadata registry, 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 |
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