Course Structure*
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 | |
*Turing College reserves the right to update and (or) amend the course curriculum and its structure as well as release new course versions.