References
Computing Resources
This page contains links to helpful resources for learning Python and Numpy+Scipy+Matplotlib, including links to documentation, books, and tutorials. The material below was not created by course staff and are not required for the completion of the course.
Why Python
Python Help
- Dive into Python 3
- Learn Python the hard way
- Python tutorial
- Facts and myths about Python names and values
- CSE workshop training material
Numpy/Scipy Help
- Numpy/Scipy documentation
- Numpy Quickstart Tutorial
- Numpy Routines
- Numpy for Matlab Users
- Python Data Science Handbook
- Scipy Lectures
- Introduction to Python for Science
- An introduction to Numpy and SciPy
- 100 Numpy exercises
- The Numpy MedKit by Stéfan van der Walt
- From Python to Numpy (An open-access book on numpy vectorization techniques, Nicolas P. Rougier, 2017)
- More in this reddit thread
Matplotlib help
Other Resources
See below some useful links that we have collected over the years. These are not material created by course staff, and are not required for the completion of the course.
Textbook
There is no required textbook for the course. However, the textbook Numerical Mathematics and Computing 6th edition by Cheney and Kinkaid can be helpful if you need some extra reading. The 5th and 7th editions are also fine references.
Linear Algebra
- Immersive Linear Algebra
- Essence of Linear Algebra (YouTube, by 3Blue1Brown)
- Linear Algebra (YouTube, by MathTheBeautiful)
Statistics (Goes Beyond Class Material)
- Statistics for Hackers by Jake VanderPlas
Floating Point
Errors
Vectors and Matrices
- What is a vector space?
- What is a vector space?
- “Practical Data Science: Matrices, vectors, and linear algebra”
- Vector Spaces
- Gaussian Blur
Optimization (Goes Beyond Class Material)
- An Interactive Tutorial on Numerical Optimization by Ben Frederickson