Intro
Data engineers are enablers of analytics and AI, connecting data engineering work to the needs of machine learning, software systems, and business decision-making.
This course teaches the core principles and practical skills of data engineering: data collection, modeling, integration, transformation, and architecture design.
Topics include:
- Data architecture fundamentals and design principles
- Data modeling techniques (relational, dimensional)
- Data ingestion from various sources (APIs, SQL/NoSQL databases, files)
- Data wrangling, quality assessment, and cleaning
- Designing scalable and maintainable data pipelines
- Evaluating data storage and processing technologies (OLAP, Data Lakes)
- Applying data governance, privacy, and security practices.
Objectives
The main objective of this course is to develop both practical skills and deep conceptual understanding of data engineering. Students will learn to collect, clean, transform, and organize data, while designing and building data infrastructures for modern analytics systems. Additionally, students will critically analyze the role of data engineering within broader technical ecosystems, including software engineering and machine learning.