A broad introduction to scientific computing using Python. Introduction to Python and its packages Numpy, SciPy, and Matplotlib. 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 transform. Introduction to data-driven and machine learning methods, including deep learning using Keras and Tensorflow. Introduction to quantum computing using Qiskit and IBM-Q. Students develop numerical calculations in the homework and in midterm and final projects.