IBM: Professional Certificate in Data Engineering Course Syllabus

Overview: This IBM Professional Certificate in Data Engineering on edX is designed for learners seeking to build advanced, industry-relevant skills in data engineering. The program spans approximately 15–20 hours of content, divided into six structured modules that progress from foundational concepts to real-world application. Learners will engage with hands-on projects, quizzes, and peer-reviewed assignments, culminating in a capstone project. The curriculum emphasizes practical tools and methodologies used in modern data engineering, including SQL, Python, ETL, and big data technologies. With guidance from IBM experts and real-world case studies, this course prepares individuals for entry or advancement in high-demand data roles.

Module 1: Introduction & Foundations

Estimated time: 4 hours

• Case study analysis with real-world examples
• Introduction to data engineering principles and workflows
• Hands-on exercises applying foundational techniques
• Guided project work with instructor feedback
• Assessment through quiz and peer-reviewed assignment

Module 2: Core Concepts & Theory

Estimated time: 3 hours

• Discussion of best practices and industry standards
• Review of essential data engineering tools and frameworks
• Foundational theoretical knowledge in data modeling and systems
• Guided project work with instructor feedback

Module 3: Practical Application & Techniques

Estimated time: 3 hours

• Hands-on exercises in ETL and data pipeline development
• Application of SQL and Python for data engineering tasks
• Review of tools and frameworks used in practice
• Assessment via quiz and peer-reviewed assignment

Module 4: Advanced Topics & Methods

Estimated time: 4 hours

• Introduction to big data technologies (e.g., Hadoop, Spark)
• Interactive lab: Building scalable data solutions
• Review of cloud-based data platforms and architectures
• Exploration of emerging trends in data engineering

Module 5: Case Studies & Real-World Projects

Estimated time: 2 hours

• Analysis of real-world data engineering challenges
• Discussion of best practices in production environments
• Guided project work with instructor feedback
• Assessment through quiz and peer-reviewed assignment

Module 6: Capstone Project

Estimated time: 2 hours

• Design and implementation of an end-to-end data pipeline
• Application of SQL, Python, and ETL techniques
• Submission of a professional portfolio piece

Prerequisites

• Familiarity with basic programming concepts (Python preferred)
• Understanding of fundamental data concepts and databases
• Access to a computer with internet for labs and assignments

What You'll Be Able to Do After

• Evaluate best practices and emerging trends in data engineering
• Develop and manage ETL pipelines using SQL and Python
• Apply theoretical knowledge to real-world data scenarios
• Build a professional portfolio showcasing data engineering projects
• Analyze and solve complex data problems using structured methodologies