Semester 1 · Core
Data Engineering & Databases for ML
https://epochschool.com/subjects/data-engineering-databases
Where training data comes from and how to keep it trustworthy: the relational model, relational algebra and SQL, integrity constraints and normal forms, storage and indexing, data warehousing, data transformation for ML, and reproducible pipelines with data versioning.
- Prerequisites
- Recommended: Helpful background. You can start without it if you are willing to fill the gaps.
What you’ll be able to do
- LO1Model a domain with entity–relationship diagrams and translate it into a relational schema with keys and constraints.
- LO2Express queries in relational algebra, tuple calculus and SQL, including joins, aggregation and window functions.
- LO3Use functional dependencies and normal forms to design schemas without redundancy or anomalies.
- LO4Explain file organisation and indexing, and reason about the cost of queries.
- LO5Design warehouse schemas (star, snowflake) with concept hierarchies and measures for analysis.
- LO6Build reproducible, validated data pipelines and transformations that feed ML models.
Chapters
- 01Modelling data with entities and relationships
- 02Relational algebra and tuple calculus
- 03SQL for analysis
- 04Integrity constraints and normal forms
- 05Storage, file organisation and indexing
- 06Data warehousing and OLAP
- 07Transforming data for ML
- 08Reproducible pipelines and data versioning
Graded labs
- L1
From ER diagram to a working schema
After chapter 1 · about 5 hours · College course registration · Authored for this course, CC0 · Runs on a CPU
- L2
Answering questions with SQL
After chapter 3 · about 5 hours · Synthetic online shop · Authored for this course, CC0 · Runs on a CPU
- L3
Normal forms from scratch
After chapter 4 · about 5 hours · Authored relation schemas and functional dependencies · Authored for this course, CC0 · Runs on a CPU
- L4
Indexes and query plans
After chapter 5 · about 5 hours · Synthetic UPI transactions · Authored for this course, CC0 · Runs on a CPU
- L5
A mini warehouse and ML pipeline
After chapter 8 · about 5 hours · Synthetic UPI transactions and merchant master · Authored for this course, CC0 · Runs on a CPU
Before you start
Recommended
Helpful background. You can start without it if you are willing to fill the gaps.
Where it leads
Responsible AI, Ethics & Governance
Capstone Project
How it’s taught
You read short sections written for the page. Each ends with a check, and in-browser exercises let you practise. Graded labs run on your own cloud lab machine, CPU-friendly.
No videos. No live classes. No AI tutor.