Exploratory data analysis (EDA) is the step where you get to know a dataset before drawing conclusions from it. You look at its structure, summarise it, and ask questions to find patterns, anomalies, and relationships worth investigating further.
In this module you'll follow complete exploratory analyses in Python: reading and profiling data from files, joining tables, applying filtering, sorting, and aggregation, and using Matplotlib and Seaborn charts to spot trends such as seasonality and top-performing items. You'll also see how an EDA case study fits into a full project, from loading a dataset to asking and answering questions about it.
Once you can explore data with confidence, you'll be ready for the next module, Data Visualization, where you learn to present your findings clearly.
A complete exploratory analysis of restaurant order data: reading and profiling flat files, joining multiple tables, applying filtering, sorting, and aggregation, and visualizing the results to find order seasonality and menu item performance.
Lesson 5 and the course project of a 10-hour course: loading a dataset with Pandas, operations with NumPy, visualization with Matplotlib and Seaborn, and asking and answering questions about a real-world dataset.