Core learning areas
- Statistics, experimentation, modelling and reproducible analysis
- Databases, ingestion, transformation, orchestration and data quality
- Warehousing, scalable processing, model operations and capstone delivery
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AI & DataData Science & Data Engineering
Statistics, pipelines, databases, modelling, experimentation and production data workflows.
Ask about this programme ↗Detailed overview
What the learning journey covers.
This practical programme combines clear instruction, guided laboratories, applied assignments and demonstrable project outcomes.
Capability outcomes
- Design a dependable path from raw data to insight
- Build and document a small data pipeline
- Connect analytical work to production and governance needs
Learning model
Learn, practise, build and present.
Format, duration, fees, prerequisites and cohort dates are confirmed for each intake or corporate brief. Completion evidence depends on attendance and assessment.
- 01
Foundation
Understand the concepts, tools, safety and context.
- 02
Guided practice
Apply each capability through supervised exercises.
- 03
Project work
Combine skills in realistic tasks with feedback.
- 04
Evidence
Present a capstone or assessed practical outcome.