DeepLearning.AI Data Engineering Professional Certificate Course Syllabus

This professional certificate program provides a comprehensive introduction to data engineering with a cloud-first approach, designed for beginners. Over approximately 16-20 weeks, learners will progress through hands-on modules covering data ingestion, transformation, orchestration, and infrastructure automation using AWS, Apache Airflow, dbt, and Terraform. The curriculum emphasizes real-world applications, culminating in a capstone project where learners build and deploy a production-level data pipeline. Consistent practice is encouraged to master in-demand skills.

Module 1: Introduction to Data Engineering

Estimated time: 15 hours

• Understand the data engineering lifecycle and core responsibilities
• Explore different data storage types and processing models
• Learn about cloud data architectures and infrastructure
• Discover tools and technologies used in modern data engineering

Module 2: Data Ingestion and Storage

Estimated time: 25 hours

• Collect and store data efficiently and securely
• Work with file formats such as JSON, CSV, and Parquet
• Ingest data from APIs, logs, and databases
• Use AWS services including S3, RDS, and DynamoDB for scalable storage

Module 3: Data Transformation with Airflow and dbt

Estimated time: 35 hours

• Build data pipelines using Apache Airflow
• Automate data cleaning and transformation workflows
• Integrate dbt for modeling and transforming data in warehouses
• Apply modular and test-driven approaches to pipeline development

Module 4: Data Orchestration and Infrastructure as Code

Estimated time: 35 hours

• Write Infrastructure as Code (IaC) using Terraform
• Provision and manage cloud data platforms on AWS
• Monitor and orchestrate workflows in production environments
• Implement DataOps principles and deployment strategies

Module 5: Capstone Project

Estimated time: 30 hours

• Design and build a production-level data pipeline
• Incorporate ingestion, transformation, and orchestration tools
• Deploy infrastructure using Terraform and AWS services

Module 6: Final Project

Estimated time: 20 hours

• Implement monitoring and error-handling in data workflows
• Document architecture and deployment process
• Present a complete technical solution using cloud-based tools

Prerequisites

• No prior experience required
• Basic understanding of programming concepts recommended
• Familiarity with cloud computing concepts helpful but not required

What You'll Be Able to Do After

• Design and manage scalable data systems on AWS
• Build and automate data pipelines using Airflow and dbt
• Provision cloud infrastructure using Terraform
• Solve business problems with robust data workflows
• Deploy production-ready data pipelines with monitoring and error handling