Before you build any model, you need to understand your data. Charts show you the shape of a dataset, the outliers hiding in it, and the relationships between its columns in a way that summary tables rarely can. This module covers the two skills that make that possible: data visualization and exploratory data analysis (EDA).
You'll start with Matplotlib and Seaborn, learning which chart fits which question: line plots, scatter plots, histograms, bar charts, and heatmaps. Then you'll put those charts to work in EDA: examining individual features, comparing how features relate to each other, spotting outliers, and forming questions worth answering with a model.
Once you know what your data looks like, you're ready to start learning from it in Machine Learning Fundamentals, the next module in this course.
The basics of Matplotlib, Python's core plotting library, applied to a real-world dataset so you learn to build and customize charts on actual data.
A long course whose visualization lecture covers Matplotlib and Seaborn charts (line, scatter, histogram, bar, heatmap), followed by a full EDA case study from data preparation to conclusions.
A beginner-friendly EDA walkthrough: reading and preparing a dataset, examining individual features and how they relate to each other, and forming a question to ask of the data.
A project-style course that applies Pandas, Matplotlib, and Seaborn to astronomy data, including outlier detection and line plots, and also visualizes image data.