Data Engineering for Machine Learning

Batch and streaming pipelines, orchestration, data quality and feature stores — get clean, reliable data to your models.

⏱ 9 weeks🎚 Intermediate🧩 3 projects · Certificate🖥 Any device · our GPUs

Apply for this course Where this leads

Draft syllabus. Timeline and topics are indicative and being finalised with the teaching team — the shape is right, the week-by-week detail may shift.

🎯 Who it's for

Software engineers and analysts moving into data or ML-platform roles.

✅ Prerequisites

SQL
Python
Basic familiarity with one cloud provider
Git and the command line

Topics covered

Data modelling for analytics and ML
Batch pipelines and orchestration
Streaming ingestion basics

Data quality and testing
Cloud warehouses and lakes
Feature stores

Expected completion timeline

9 weeks part-time at 8–12 hours per week ≈ 2.1 months. Self-paced learners can go faster; the live cohort keeps this pace.

Wk 1–3

Pipelines and orchestration

You produce: An orchestrated batch pipeline with tests.

Wk 4–6

Streaming and data quality

You produce: A streaming ingestion job with data-quality checks.

Wk 7–9

Feature store and project

You produce: A documented feature pipeline feeding a model.

🧭 Where this fits

Core of the Data Engineer path and the first half of MLOps Engineer. See the career & salary map for the full path, the expected pay by region, and the total cost.

💳 Delivery & fees

Available in all four delivery formats. Fees vary by format and are confirmed on your advisor call; instalments available. [Add the fee and the next cohort date here.]

Add this to your plan

Book a call and we'll place this course in a full path toward the role you want.

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