Covers algorithmic thinking, Python programming, numerical methods, and essential computing concepts for scientific computing.
Covers the Metropolis-Hastings algorithm and gradient-based approaches for biasing searches towards higher likelihood values.
Covers simulation, modeling, acceleration profiles, natural frequencies, rigidity calculations, and anti-resonance solutions for multi-axes robots.
Focuses on advanced pandas functions for data manipulation, exploration, and visualization with Python, emphasizing the importance of understanding and preparing data.
