Scientific Computing and Data Science

Graduate course, RUC, Department of Science and Environment, 2023

Flipped-classroom format course, co-taught with Prof. Thomas Schrøder focused on giving an overview of major topics in scientific computing and data science. In-class activities followed Jupyter notebooks hosted here. Topics in the data science portion of the course follow the book Data-Driven Science and Engineering by Steve Brunton and J. Nathan Kutz, and make use of recorded lectures from Steve Brunton’s YouTube channel

Topics include:

  • Scientific Computing
    • Programming in Python; numpy, scipy, data visualization
    • Numerical integration and differentiation
    • Sovling IVPs
    • Solving PDEs
    • Molecular dynamics
    • Optimization
  • Data Science
    • Regression and Model selection
    • Clustering and Classification
    • Neural networks
    • Data-driven dynamical systems (SINDy)
    • Singular value decomposition