Data course stage

Stage 1. Data Foundations

Start with the language, formats, and habits that make data useful across teams.

4 hours across 11 modules

Stage 1 Foundations as a five-cluster literacy stack

Eleven Foundations modules group into five clusters that build literacy from meaning at the base up to ethics and trust.

Stage 1 Foundations as a five-cluster literacy stack Five-cluster stack from L1 Meaning at the base up to L5 Ethics and trust at the apex. Each cluster card carries the level, the cluster name, the anchor standard right-aligned, and white module badges along the bottom listing the modules inside (eg Quality, Lifecycle for the L4 Quality and lifecycle cluster, emphasised in red soft). A red-accent callout names Quality and lifecycle as the bridge to Stage 2 Applied. STAGE 1 FOUNDATIONS · ELEVEN MODULES IN FIVE LITERACY CLUSTERS L5Ethics and trustICO + UK GDPRRolesEthics and trust L4Quality and lifecycleUK GDQFQualityLifecycle L3Standards and interoperabilityISO 11179 + W3CVisualisationStandardsOpen + FAIR L2Units and formatsISO 80000-13Units + binaryFormats L1MeaningDMBOK 2 §2What data isDIKW Quality and lifecycle is the bridge to Stage 2 The Applied stage assumes literacy is in place and starts where quality rules and lifecycle controls leave off. ransfordsnotes.com

Stage 1 builds literacy from raw observation up to ethics and trust. Eleven modules group into five clusters: meaning, units and formats, standards and interoperability, quality and lifecycle, and roles plus ethics. DAMA-DMBOK 2 §2 sets the same shape.

Module path

Work through the modules in order. The stage visual above gives the map; each module opens with the detailed visuals that carry the concept.

  1. D1

    What data is

    0.25h
    • Define data, structured and unstructured types, and metadata
    • Give examples of data in everyday life and professional contexts
    Open module

  2. D2

    DIKW hierarchy

    0.25h
    • Explain the Data-Information-Knowledge-Wisdom hierarchy
    • Classify real-world examples into each level
    Open module

  3. D3

    Units, notation, and binary basics

    0.5h
    • Convert between bits, bytes, kilobytes, and kibibytes
    • Explain why binary representation matters for data storage
    Open module

  4. D4

    Data representation and formats

    0.5h
    • Compare CSV, JSON, XML, and Parquet formats
    • Explain character encoding and why UTF-8 matters
    Open module

  5. D5

    Standards and interoperability

    0.5h
    • Explain why data standards reduce integration costs
    • Give examples of important standards like ISO 8601 and ISO 3166
    Open module

  6. D6

    Open data and FAIR as a guiding lens

    0.25h
    • Explain FAIR as a guiding lens for sharing and reuse, alongside open data
    • Explain the difference between open, FAIR, and restricted data
    Open module

  7. D7

    Visualisation basics

    0.25h
    • Choose appropriate chart types for different data
    • Spot misleading visualisations and explain why they mislead
    Open module

  8. D8

    Data quality and meaning

    0.5h
    • List the six core dimensions of data quality
    • Assess a dataset's quality using practical checks
    Open module

  9. D9

    Data lifecycle and flow

    0.25h
    • Describe the stages of a data lifecycle
    • Explain data retention and its regulatory drivers
    Open module

  10. D10

    Roles and responsibilities

    0.25h
    • Distinguish data owner, steward, engineer, and analyst roles
    • Explain GDPR controller and processor responsibilities
    Open module

  11. D11

    Ethics and trust

    0.5h
    • Explain informed consent and its practical challenges
    • Describe algorithmic bias and how it arises from data
    Open module