💻 Software & Environment Setup
Data Science & Analytics
The computational toolchain and environment used throughout AI/ML for Scientific Discovery Using PyTorch and JAX.
Scientific discovery pytorch courseScientific discovery pytorch online trainingBest scientific discovery pytorch certificationScientific discovery pytorch for researchersScientific discovery pytorch hands-on workshopLearn scientific discovery pytorch
# 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 AI/ML for Scientific Discovery Using PyTorch and JAX
e-Certificate and e-Marksheet issued on successful completion.
