- Lecturer: Riaz Ahmad
Course Term: Michaelmas
Course Lecture Information: 8 hours of lectures in week -1 and week 0.
Course Overview:
Course Overview:
This is an intensive eight-hour in-class lecture course. It introduces Jupyter notebooks, enabling students to become comfortable with this environment, including instruction on conda and pip management. Students are expected to have viewed the pre-course online video lectures.
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. A small part of each class will be devoted to working the through the coding problems.
This is an intensive eight-hour in-class lecture course. It introduces Jupyter notebooks, enabling students to become comfortable with this environment, including instruction on conda and pip management. Students are expected to have viewed the pre-course online video lectures.
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. A small part of each class will be devoted to working the through the coding problems.
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:
Functions and modules. Operating system and file management.
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
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