Python in Finance is a unique, easy-to-follow, introductory course which requires no prior programming knowledge or experience. Designed to meet the enormous rise in demand for individuals with knowledge of Python in finance, students are taught the practical coding skills now required in many roles within banking and finance.
The material contains multiple examples of practical applications in finance with a focus on quantitative risk/pricing analytics, giving you an opportunity for valuable practical experience. The course contents provide you with the must-have coding skills needed to excel in the modern finance sector. The course material has been developed in partnership with industry veteran and renowned practitioner, Dr Simon Clift.
After this course, candidates will possess the knowledge to write their own code from scratch in the Python programming language to, for example price options with the Black Scholes model, derive greeks, perform Monte Carlo simulation, generate an implied vol surface and many more.
The Python programs developed by the student during the training program, and for the project completed at the end of the cohort (in the full program only) become an important part of the student’s portfolio. The portfolio is a valuable addition to a student’s CV/resume, and is a real asset that is evidence of the student’s practical experience and knowledge. This is particularly useful during a job search to set the student apart from other applicants.
Each student in the full program will be assigned a Teaching Assistant (TA) who will assess and provide feedback on the Python project. Our TAs will be Master’s/PhD candidates sought from some of the best Finance graduate programs in the UK including:
1. Computational Finance, University College London (UCL)
2. Financial Risk Management, University College London (UCL)
3. Financial Risk Management, ICMA Centre
What you learn
You will learn how to use the following and more, to write Python code for financial applications.
- Jupyter Notebook
- Pandas (for data analysis)
- NumPy, and SciPy (for quantitative computing)
- Matplotlib (for data visualisation)
This course assumes knowledge of the topics in the Market Risk (Finance) curriculum.
For more detailed information on the course’s curriculum please click below:
Notepad++ or a similar text editor for .py files
(Get both Jupyter Notebook and Python together at: anaconda.com/download)
Note: This course is taught in a Windows environment. Students using a different environment will have to Google installation instructions for their specific environment. The course is designed for business analysts in finance. It is not intended for IT specialists, computer scientists, or professional developers in finance.