Data Science Foundations: Working with Data in Azure ML

Raw data is rarely ready for machine learning. To build accurate models, you must first master the art of data preparation, cleaning, and structuring within your cloud environment. This text-based course guides you through the essential concepts of data flow and data preparation using Azure Machine Learning (Azure ML). You will transition from handling messy, unstructured datasets to curating clean, model-ready data pipelines. In this course, you will learn to: Understand core data types, distinguishing between continuous and categorical variables; Clean datasets by identifying and removing repeated values, duplicates, and extreme outliers; Apply scaling and normalization techniques to prepare numeric features; Configure data ingestion and manage secure data flows within the Azure ML ecosystem; Practice structuring data assets and implementing modern data quality checks. Starting with foundational definitions of data quality and data types, the course progresses through step-by-step written explanations of data cleaning, transformation, and integrating these processed flows into cloud pipelines. This course is designed for aspiring data scientists, analysts, and beginners looking to understand cloud-based data engineering without any prior machine learning experience. Start mastering your data workflows today.