Data Cleaning and Analysis with the University Towns Dataset

Raw data is rarely ready for analysis, often arriving in unstructured formats that require careful preparation. This text-based course guides you through the practical process of transforming a messy, real-world dataset of university towns into a clean, structured format ready for downstream data pipeline integration. You will learn to think like a data engineer by identifying common data quality issues and systematically resolving them. By completing this course, you will gain the confidence to handle unstructured text files, extract geographic hierarchies, and prepare datasets for robust analysis. What you'll learn: - Understand foundational data cleaning concepts and common pipeline bottlenecks - Parse unstructured text files to extract structured state and town relationships - Apply modern Python type hints and data structures to ensure code quality - Clean and normalize geographic text data using pandas and regular expressions - Validate processed data to ensure consistency and prevent pipeline failures This course begins with essential data engineering concepts and raw data inspection before moving step-by-step through parsing, regex normalization, and final validation. It is designed for beginners and aspiring data engineers with a basic grasp of Python syntax; no advanced data science experience is required. Start reading today to build your data preparation skills.