Integrating machine learning, data-analysis tools and Python programming in the exploration of physical phenomena.
Fundamentals of Python programming and scientific libraries such as NumPy, SciPy and Matplotlib, applied to physics research and simulations.
Techniques and tools for data cleaning, visualization, statistical analysis and big-data workflows in physics contexts using Pandas, Plotly, and more.
Supervised and unsupervised learning, neural networks, and physics-based ML methods for modeling, prediction, and discovery in physical systems.