This section focuses on data engineering practices, methodologies, and tools for building robust data pipelines and analytics systems. Learn about modern data processing approaches and MLOps integration.
What You'll Learn
Data Analysis Methodologies: Implementing CRISP-DM for systematic data exploration
MLOps Integration: Bridging data engineering with machine learning operations
Data Pipeline Design: Building scalable and maintainable data processing systems
Analytics Platforms: Working with modern data platforms like Databricks
Topics Covered
This section covers practical data engineering approaches including exploratory data analysis, MLOps workflows, and platform-specific implementations for data processing and analytics.
