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