Data course stage

Stage 2. Applied Data

Move into models, pipelines, and applied analytics while keeping reliability in view.

3 hours across 9 modules

Stage 2 Applied as a four-cluster supply chain map

Nine Applied modules group into four practical clusters that turn governed source data into a contracted product.

Stage 2 Applied as a four-cluster supply chain map Four cluster cards left to right: Pipelines (architecture + medallion), Governance (stewardship + catalogue), Interoperability (semantics + APIs, emphasised), Products + analytics (contracts + claims). Verb arrows step through the supply chain. A red-accent callout names the contract as the stage's binding output. STAGE 2 APPLIED · FOUR-CLUSTER SUPPLY CHAIN MAP L1DatabricksPipelinesArchitecture, medallion, failure modesL2DMBOK 2 §3GovernanceStewardship, catalogueL3ISO 11179InteroperabilitySemantics, APIsL4ODCS v3.1Products + analyticsContracts, claims, evidence governed bylinked bywrapped by The data contract is the stage's binding output Stage 2 finishes when a learner can defend every clause of a published data contract under producer-consumer review. ransfordsnotes.com

Stage 2 Applied builds the supply chain that turns governed source data into a contracted product: pipelines, governance, interoperability, analysis. Each cluster has its own canonical anchor; the stage finishes when a learner can defend each clause of a data contract.

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. A1

    Architectures and pipelines

    0.5h
    • Explain ETL vs ELT and when each is appropriate
    • Describe data lineage and why it matters for trust
    Open module

  2. A2

    Governance and stewardship

    0.5h
    • Describe DAMA DMBOK knowledge areas
    • Explain data catalogues and master data management
    Open module

  3. A3

    Interoperability and standards

    0.25h
    • Explain the role of APIs in data interoperability
    • Describe common data exchange standards
    Open module

  4. A4

    Analysis and insight

    0.25h
    • Distinguish descriptive, diagnostic, predictive, and prescriptive analytics
    • Explain feature engineering in machine learning contexts
    Open module

  5. A5

    Probability and distributions

    0.5h
    • Explain normal distributions and what they tell you about data
    • Describe correlation vs causation with examples
    Open module

  6. A6

    Inference and experiments

    0.25h
    • Design a valid A/B test with proper controls
    • Explain p-values and their limitations
    Open module

  7. A7

    Data modelling basics

    0.25h
    • Create conceptual and logical data models
    • Apply normalisation rules to reduce redundancy
    Open module

  8. A8

    Data as a product

    0.25h
    • Explain Dehghani's data mesh principles
    • Describe data contracts and their purpose
    Open module

  9. A9

    Risk, ethics, and strategic value

    0.25h
    • Explain the right to erasure under GDPR
    • Connect data ethics to business risk and reputation
    Open module