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MATHEMATICS & DATA / CORE

DAT120Data science: SQL, EDA & statistical reasoning

32 h productive allocation12 h 48 m learning + 19 h 12 m training4 units · 1 assessed practical brief

Detailed course plan and training brief. A complete taught course pack is in preparation. The linked foundation module, where shown, is a smaller available resource.

BEFORE YOU BEGIN

A clear starting point.

Prerequisites: AI101 · MTH110

Core studio / CPU-friendly local or notebook environment

WHAT YOU SHOULD BE ABLE TO DEMONSTRATE

  1. Explain the assumptions and failure modes in data structures and querying.
  2. Complete the practical: audit a synthetic operational dataset, answer five questions with SQL and build an EDA notebook that links each conclusion to evidence.
  3. Defend the resulting data dictionary with reproducible evidence.

THE SUBJECTS / FOUR CONNECTED UNITS

What you will study.

UNIT 018 h allocated

Data structures and querying

  • NumPy arrays
  • pandas tables
  • SQL joins
  • aggregations and nulls
UNIT 028 h allocated

Exploratory investigation

  • Missingness
  • distributions
  • visual reasoning
  • units and provenance
UNIT 038 h allocated

Statistical thinking

  • Sampling
  • uncertainty
  • confidence intervals
  • hypothesis-test limits
UNIT 048 h allocated

A defensible data contract

  • Schema and validation
  • grouped and temporal splits
  • leakage audit
  • data dictionary

THE TRAINING STUDIO

Make the subject
visible in your work.

Audit a synthetic operational dataset, answer five questions with SQL and build an EDA notebook that links each conclusion to evidence.

Evidence to produce

  • Data dictionary
  • SQL query file
  • EDA notebook
  • validation report

Training brief for supervised delivery. Code, datasets, answer keys and environment acceptance must be completed in the full course pack before delivery is confirmed.

ASSESS THE REASONING

A question you
should be able to answer.

What changes when rows are repeated measurements of the same machine rather than independent samples?

Review dimensions

Technical correctness · reproducibility · interpretation of results · failure analysis · individual explanation. Course-level rubric and faculty moderation form part of the final adopted teaching pack.

External reference access, hardware, cloud accounts and paid services are separate. No third-party certification is included by implication.

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