Module 1: Introduction to Data Engineering

Sprint 1

Intermediate Python & Git - Python data model, Python sequences, Git basics

Sprint 2

Introduction to Relational Databases & SQL Basics - Python mutability and object references, SQL queries

Sprint 3

Intermediate SQL - SQL joins, subqueries, sets, and strings

Module 2: Fundamentals of Data Engineering

Sprint 1

Advanced Python & Linux Shell Commands - Linux distribution and architecture, shell commands, Python interfaces, and inheritance

Sprint 2

Managing Relational Databases & Advanced SQL - database security and compliance, Python iterators and generators, SQL indices, transactions, and views

Sprint 3

Working with Data Pipelines & Apache Airflow - constructing ETL pipelines, Airflow DAGs, and workflows

Module 3: Intermediate Data Engineering

Sprint 1

Data Warehousing & dbt - enterprise data warehousing, defining data models with dbt

Sprint 2

Data Mesh & ML systems design - architecture, principles of data mesh, feature engineering, model development, and evaluation

Sprint 3

Docker & Intro to MLOps - Docker basics, container concept, and containerization principles, ML model monitoring, and continual learning

Specialization modules (optional)

(learner must choose at least one)

Module 4A

Google Cloud Platform

Module 4B

Amazon Web Services

Specialization

Data analysis and visualisation with Python