Data
Engineering
Master the modern data stack to design, build, and optimize scalable data pipelines, data warehouses, and big data architectures.
Technologies You'll Master
Learning Outcomes
Master the Curriculum
A carefully structured syllabus designed by industry experts — progressive mastery from ground zero to production-level readiness.
Month 1
Foundations
- Introduction to Data Engineering and Data Ecosystem
- Roles: Data Engineer vs Data Analyst vs Data Scientist
- Basics of databases and data pipelines
- Python setup (Anaconda, Jupyter)
- Weekly Test 1
Real-World Project Execution
As part of the Data Engineering Internship Program, project-based learning is a critical component designed to provide students with hands-on exposure to real-world data systems, pipelines, and scalable architectures. The objective of this framework is to ensure that students develop the ability to design, build, optimize, and deploy data workflows aligned with industry standards. The project structure follows a progressive model, categorized into five distinct sets based on complexity. The initial sets focus on foundational database operations and data handling. Intermediate sets introduce pipeline development, big data processing, and workflow orchestration. The final set consists of advanced, industry-level projects incorporating cloud platforms, streaming systems, and end-to-end deployment.
Learning Stages
- Set 1 & Set 2: Foundational (Basic Level)
- Set 3 & Set 4: Intermediate (Medium Level)
- Set 5: Advanced (Industry-Level Capstone)
Implementation Guidelines
- Follow a structured lifecycle: problem definition, data ingestion, cleaning, transformation, and storage.
- Perform pipeline development, validation, optimization, and deployment.
- Intermediate and advanced projects must incorporate Apache Spark, Apache Airflow, and Kafka.
- Advanced projects must include deployment on AWS or Microsoft Azure ensuring exposure to scalable, production-grade environments.
Expected Outcomes
- Design and implement robust data pipelines and manage structured/unstructured large-scale data workflows
- Operate effectively with distributed systems and gain practical experience in ETL and real-time processing
- Develop proficiency in modern industry tools like Kafka, Airflow, and Spark
- Enhance problem-solving capabilities to build scalable data portfolios aligned with modern roles
Project Set 1
This set focuses on basic data handling, file processing, and introductory database operations.
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