Financial Data Science with Python

• Credit value: 15 credits at Level 7
• Convenor: Ilaria Peri
• Tutors: Ilaria Peri, Simon Hubbert
• Prerequisite: Quantitative Techniques
• Assessment: a project in Python (30%) and two-hour examination (70%)

On this module you will learn how to program in Python, in particular the main computational techniques in financial data analysis, financial mathematics and machine learning. The module will be based on practical lab sessions.

Indicative module syllabus

• Programming in Python: basic operators, data type and structures, control flow, functions, modules, classes
• Data analysis with pandas and plot visualisation with matplotlib
• Randomness and simulations in Python
• Optimisation and integration in Python
• Introduction to machine learning techniques in Python

By the end of this module, you will be able to:

• understand and program in Python
• analyse and visualise data using the pandas library and the matplotlib package
• generate random number and simulate data
• apply optimisation and integration techniques
• understand and apply some machine learning techniques.