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FND-102

Python, Data and Engineering Foundations for AI

The programming, data and engineering habits every AI project depends on

  • EQF 5

    level

  • 2 ECTS

    50 hours

  • 4 weeks

    2 × 90 min live a week

  • 8 lessons

    live online

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About this course

A fast, practical grounding in the tools and habits that separate an AI notebook from an AI project: modern Python environments and packaging, typed and tested code, NumPy, pandas and Polars, SQL with point-in-time correctness, validated data pipelines and reproducible exploratory analysis. Learners finish with a reviewed repository they can build on in every later course.

For: student, data professional, software engineer.

Learning outcomes

On successful completion you will be able to:

  1. 1Describe the Python scientific stack and the engineering workflow used in AI projects, including environments, packaging, version control, notebooks versus modules and testing.
  2. 2Write idiomatic, typed and tested Python that loads, cleans, transforms and analyses data with NumPy, pandas or Polars, and SQL.
  3. 3Build a reproducible data pipeline from raw files or an API to an analysis-ready dataset, with validation checks at each stage.
  4. 4Explore a dataset visually and statistically to identify quality issues, leakage risks and features worth modelling.
  5. 5Work in a shared repository using Git, code review and documentation conventions, taking responsibility for the reproducibility of one's own work.

Modules and lessons

  1. Week 1Modern Python for AI work
    • Environments, packaging and project layout
    • Idiomatic, typed and tested Pythonlab
  2. Week 2Working with data
    • NumPy and vectorised computing
    • DataFrames with pandas and Polarslab
  3. Week 3Data at the source
    • SQL for machine learning
    • Pipelines, APIs and data validationlab
  4. Week 4From data to insight
    • Exploratory data analysis and visualisation
    • Reproducible AI projects

Assessment

  • Quiz (20%)

    Four weekly quizzes drawn from the lesson quiz banks.

  • Lab (40%)

    Three labs with automated tests - a refactored module, a cleaned and joined dataset, and a validated ingestion pipeline.

  • Project (30%)

    End-to-end data pipeline and exploratory analysis report in a Git repository, reviewed by a mentor through a pull request.

  • Peer review (10%)

    Structured code review of two peers' pull requests using the course review checklist.

Pass mark 60%. Grades: Distinction, Merit, Pass.