General Prerequisites:
Course Term: Michaelmas
Course Lecture Information: 16 lectures
Course Overview:
Course Overview:

This is an intensive eight-hour in-class lecture course. It commences with a series of online classes to introduce Jupyter notebooks, enabling students to become comfortable with this environment, including instruction on conda and pip management.
Why Python? Python is rapidly becoming the standard in scientific computing, receiving much excitement about the application to mathematical finance and medicine. Its appeal continues to grow in both academic and industry sectors. Python is available on multiple platforms. It is simple to use, easy to maintain, promotes productivity and free to download, with a growing amount of add-on modules. The course assumes no previous knowledge of python. A set of detailed lecture notes, exercises and code will be provided in the form of a complete and self-contained Jupyter notebooks.


Learning Outcomes:
On completion of the course, a student should be comfortable using python to solve practical problems in several mathematical areas including mathematical finance and modelling in medicine. The focus is the data intensive application of Python.
Course Synopsis:
Data types and structures. Input, output and flow control. Functions and modules.
Special libraries to include: NumPy (numerical computing). Matplotlib, Plotly & cufflinks (graphics). SciPy (scientific algorithms). Pandas (data science). scikit-learn (machine learning).
Numerical recipes in Python – root finding; interpolation; numerical integration; linear algebra; statistics and random number generation. Monte Carlo methods for SDEs and derivative pricing.

Resources:
https://www.anaconda.com/products/individual
https://www.python.org/
https://docs.python.org/2/library/numeric.html