Exploratory Data Analysis and Visualization with Python

Raw data is rarely ready for analysis, and understanding its underlying patterns is the first step in any successful data project. This text-based course guides you through the foundational techniques of exploratory data analysis (EDA) and data visualization using Python. You will transition from looking at messy datasets to confidently summarizing, cleaning, and plotting data to find key trends and anomalies. What you'll learn: 1. Understand core statistical concepts and data distributions through detailed written explanations. 2. Clean and prepare raw datasets by identifying missing values and outliers with pandas. 3. Write Python code to generate clear data visualizations using matplotlib and seaborn. 4. Analyze relationships between variables using correlation and cross-tabulation techniques. 5. Apply modern data quality assessment workflows to validate dataset integrity. 6. Practice interpreting data patterns and forming hypotheses through written exercises. The course begins with essential terminology and statistical foundations before moving into step-by-step code demonstrations and practical written exercises. Designed for absolute beginners, this course requires no prior programming or advanced mathematics experience. Start reading today to unlock the stories hidden within your data.