Data Preparation with Data Wrangler in Fabric

Before you can train any machine learning model, you must ensure your data is clean, consistent, and correctly formatted. Raw data is rarely ready for analysis, and manual preprocessing can be time-consuming and error-prone. This text-based course guides you through the foundations of data preparation using Data Wrangler in Fabric. You will transition from handling messy, unstructured datasets to generating clean, model-ready data directly within notebook environments, accelerating your data science workflow. What you'll learn: Understand foundational data preprocessing concepts and common data quality issues; Navigate the Data Wrangler interface within Fabric notebooks to profile and explore data; Apply transformations to handle missing values, duplicates, and inconsistent formatting; Perform feature engineering steps like one-hot encoding, scaling, and data normalization; Generate clean Python code automatically to integrate preprocessing steps into reproducible pipelines; Prepare high-quality datasets optimized for training machine learning models. The course begins with essential terminology and data cleaning principles before moving into hands-on written walkthroughs of Data Wrangler's transformation tools. You will read through practical examples and code snippets that show you how to seamlessly export your cleaning steps into production-ready Python code. This course is designed for beginner data analysts, aspiring data scientists, and developers who want to streamline their data preparation workflows. No prior experience with Fabric is required, though a basic familiarity with Python and data concepts is helpful. Start reading today to simplify your data cleaning process and build better machine learning models.