Exploratory Data Analysis with Python

Discover patterns, spot anomalies, and understand your data. Before building models or running advanced tests, every data project begins with exploration. Exploratory Data Analysis (EDA) is the process of examining datasets to understand their structure, spot errors, and reveal insights. In this course, you’ll learn how to use Python to perform EDA effectively and systematically. We start by introducing the goals of EDA: understanding distributions, identifying missing or unusual values, and finding relationships between variables. Using Pandas, you’ll generate descriptive statistics and summary tables to quickly grasp the shape of your data. Then, with Matplotlib and Seaborn, you’ll create visualizations that highlight trends and uncover hidden patterns—histograms, box plots, correlation heatmaps, scatter plots, and more. Next, we cover data cleaning during EDA, such as detecting outliers, handling missing data, and checking assumptions before deeper analysis. You’ll also learn how to document your exploration process, turning it into a reusable and shareable workflow. By the end of this course, you will be able to: Apply Pandas for descriptive statistics and quick insights. Use visualization to explore distributions and relationships. Detect anomalies and prepare data for further analysis. Build an EDA workflow you can reuse for different datasets. Develop intuition for asking the right analytical questions. Hands-on practice is central to this course. You’ll work with real-world datasets, encounter messy and imperfect data, and learn how to interpret what the numbers and charts are telling you. Exploratory Data Analysis with Python is the bridge between raw data and meaningful conclusions. By mastering EDA, you’ll be ready to design stronger analyses, avoid common mistakes, and set the foundation for advanced techniques like hypothesis testing and machine learning.