This module builds your visualization toolkit and introduces exploratory data analysis as a systematic practice. You will start with quick exploratory charts in Pandas, then expand to three specialized libraries — Seaborn for statistical visualization, Matplotlib for publication-quality figures, and Bokeh for interactive web-ready charts. The module closes with a full EDA case study that weaves together data wrangling, aggregation, and visualization into a coherent analytical workflow.
Learning Objectives
By the end of this module, you will:
- Create quick exploratory charts directly from a DataFrame using Pandas
.plot()
- Choose the right visualization library for a given analytical goal
- Build statistical visualizations with Seaborn using minimal code
- Create polished, fully-customized figures with Matplotlib’s Figure/Axes API
- Produce interactive, web-ready charts with Bokeh including hover tooltips and zoom
- Apply a systematic EDA framework — question → structure → distributions → segmentation → story — to a new dataset
Module Resources
Chapters & Notebooks
| 13 |
Visualization with Pandas |
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| 14 |
Advanced Data Visualization (Seaborn, Matplotlib, Bokeh) |
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| 15 |
Exploratory Data Analysis |
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Lab
| 🧪 Lab 5 |
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