Data Wrangling with Python: Clean and Transform Datasets

Most of a data professional's time is spent cleaning and preparing data rather than performing the final analysis. This course provides you with the essential skills to navigate this critical stage of the data lifecycle, ensuring your insights are based on accurate and well-structured information. You will transform from a beginner into a confident data handler capable of automating the cleaning process and preparing high-quality datasets for visualization or machine learning. What you'll learn: - Understand foundational data structures and the core principles of the wrangling lifecycle. - Practice cleaning techniques for handling missing values, duplicates, and inconsistent formatting. - Master reshaping and pivoting datasets to meet specific analytical requirements. - Apply modern Python features like type hints to write more readable and maintainable data scripts. - Learn to merge and join data from multiple sources into a unified, clean structure. - Understand how to validate data quality and handle outliers effectively. The course begins with essential terminology and basic concepts before moving into detailed written explanations of data manipulation workflows. You will progress through structured text-based lessons that focus on practical application through code snippets and logic-based exercises. This course is designed for beginners who have a basic grasp of Python and want to build a solid foundation in data preparation. No prior experience with data science or advanced statistics is required. Start building your data manipulation skills today.