Written by Shizuo Kaji
This repository provides hands-on tutorials for topological data analysis (TDA), with notebooks that run in Google Colaboratory so no local Python setup is required.
| Notebook | Description | Colab |
|---|---|---|
| TopologicalDataAnalysisWithPython.ipynb | End-to-end TDA workflow with examples across multiple data types and machine learning tasks. | Open in Colab |
| PersistentHomology_Interactive.ipynb | Interactive examples focused on persistent homology concepts. | Open in Colab |
The tutorial notebook includes:
- Interactive intuition-building for persistent homology of Vietoris-Rips and cubical complexes
- Feature extraction using persistent homology from point clouds, graphs, images, volumes, and time-series data
- Regression and classification using topological features
- Dimension reduction while preserving topological features
- Visualisation to reveal the shape of data
This tutorial was originally prepared for the online event: TDA for Applications: Tutorial and Workshop (18-19 June 2020).
The following example is no longer maintained and is not compatible with Google Colaboratory:
- NLP Example: Vectorisation and Visualisation Analyse mathematics papers from arXiv and visualise the data using mapper. Setup instructions: NLP_example.md
- Persistent Homology - An Introduction via Interactive Examples: A quick and interactive introduction to persistent homology theory.