Data Science Workloads with Azure Databricks

Modern data science requires the ability to process massive datasets that exceed the limits of a single machine. Transitioning your analytics to the cloud allows you to harness distributed computing power to solve complex problems at scale. This course provides a clear path to understanding how Azure Databricks facilitates collaborative data science and high-performance machine learning. You will learn to navigate the workspace, manage clusters, and execute data experiments through written explanations and practical code examples. What you'll learn: - Understand foundational Spark architecture and distributed computing concepts - Configure and manage Databricks clusters optimized for data science tasks - Process and transform large-scale datasets using Spark DataFrames - Build and evaluate machine learning models using scalable libraries - Track experiments and manage model lifecycles with MLflow - Apply Delta Lake principles for reliable and performant data storage The course begins with essential terminology and environment setup before progressing into data manipulation, exploratory analysis, and model deployment. This text-based program is designed for beginners looking to enter the field of cloud data science, and no prior experience with Databricks is required. Start building scalable data solutions through structured reading and written exercises.