# Mathematical Python

*Mathematical Python* is an introduction to mathematical computing including:

- Jupyter notebooks, markdown and $\LaTeX$
- Basic Python programming: datatypes, logic, loops and functions
- Scientific computing with NumPy, SciPy and Matplotlib
- Applications in calculus, linear algebra and differential equations

## Notebooks

*Mathematical Python* is a collection of Jupyter notebooks and are available at:

If you have a UBC CWL:

## Prerequisites

We assume the reader has completed undergraduate courses in:

- Differential calculus: derivatives of elementary functions, Taylor series and optimization
- Integral calculus: Riemann sums, sequences and series
- Linear algebra: vector and matrix operations, systems of equations, eigenvalues and eigenvectors
- Differential equations: Euler's method for first order equations, linear systems of ODEs

## Author

Patrick Walls is an instructor in the Department of Mathematics at the University of British Columbia and teaches mathematical computing, differential equations and vector calculus for mechanical engineering.

## Feedback

Comments and suggestions are always welcome! Please contact Patrick Walls, make a pull request to the GitHub repo or share your thoughts in the Google form.

## Acknowledgements

*Thank you ...*

- Pacific Institute for the Mathematical Science (PIMS) for creating Syzygy and hosting Jupyter notebooks for thousands of students and researchers across Canada.
- Jupyter, Python and SciPy developers for creating transformative open source tools.
- MkDocs developers and Martin Donath for creating a Material Design theme for MkDocs.

## License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

## Last Modified

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Wed 4 Dec 2019 17:20:04 PST
```