A broad introduction to scientific computing using the Python language. Introduction to Python and its packages Matplotlib, Numpy and SciPy. Numerical precision and sources of error. Root-finding and optimization. Numerical differentiation and integration. Introduction to numerical methods for linear systems and eigenvalue problems. Numerical methods for ordinary differential equations. Finite-difference methods for partial differential equations. Discrete Fourier transforms. Introduction to data-driven and machine learning methods. Singular value decomposition. Deep learning with PyTorch and Keras. Students will develop numerical calculations in the homework and in a final project. Not offered 2023-24.