💻 Software & Environment Setup
Data Science & Analytics
The computational toolchain and environment used throughout Topological Data Analysis (TDA): Persistent Homology for High-Dimensional Datasets.
Topological data analysis persistent courseTopological data analysis persistent online trainingBest topological data analysis persistent certificationTopological data analysis persistent for researchersTopological data analysis persistent hands-on workshopLearn topological data analysis persistent
# Recommended conda environmentconda create -n dstc_env python=3.11 -yconda activate dstc_envpip install numpy pandas scikit-learn matplotlib seabornPython 3.11 + Jupyter
Interactive analysis notebooks
Interactive analysis notebooks
pandas / NumPy
Data wrangling & numerics
Data wrangling & numerics
scikit-learn
Modelling & evaluation
Modelling & evaluation
Plotly / Seaborn
Statistical visualisation
Statistical visualisation
Supported OS: Linux (Ubuntu 22.04+), macOS, or Windows via WSL2.
Hardware: 16 GB RAM minimum; a CUDA GPU helps for deep-learning modules.
Earn government-registered certification in Topological Data Analysis (TDA): Persistent Homology for High-Dimensional Datasets
e-Certificate and e-Marksheet issued on successful completion.
