Syllabus

This is a course on scientific computing using Python. We'll cover aspects of the Python language as they are relevant to the material. The following schedule should be seen as a high-level guide to what we'll do in 8 lectures, but is not set in stone.

1. Introduction to Python & NumPy
2. Dense Linear Algebra in NumPy
3. Intro to SciPy - Dense & Sparse linear algebra
4. Optimization I - Scipy.optimize
5. Pandas, Sckit learn
6. Tensorflow
7. Optimization II - ortools
8. TBD (Survey)

We'll intersperse the visualization libraries:

• Matplotlib
• Plotly