Analyzing Categorical Data in Python

Data is rarely just numbers; much of the information we collect is descriptive, qualitative, and categorized. To draw accurate conclusions from real-world datasets, you must know how to properly structure, process, and analyze categorical variables without introducing bias. This text-based course guides you through the foundational concepts of categorical data analysis using modern Python libraries, ensuring you can handle non-numeric data with confidence. You will transition from basic data sorting to implementing robust statistical techniques and modern encoding practices for machine learning workflows. What you'll learn: - Understand the fundamental differences between nominal, ordinal, and interval data - Clean and prep messy categorical variables using modern pandas techniques and category data types - Implement proper encoding strategies including one-hot encoding, ordinal encoding, and target encoding - Apply statistical tests such as Chi-Square to analyze relationships between categories - Visualize category distributions and relationships through clear, code-driven analysis - Avoid common pitfalls like high-cardinality issues and data leakage during preprocessing This course begins with essential terminology and the mathematical foundations of categorization, before moving step-by-step through practical data manipulation, statistical testing, and modern encoding workflows. You will work with clean, text-based code examples that illustrate each concept in action. This course is designed for beginner data analysts, aspiring data scientists, and Python developers who want to build a solid foundation in qualitative data preprocessing. No advanced statistical background is required; familiarity with basic Python syntax is recommended.