Finance with Python

Module overview

Finance with Python is a critical module for you to learn computer programming and perform financial data analysis. This module covers the use of Python in finance, including financial data analysis and statistical modelling, which includes an introduction to Python programming language, libraries, and tools commonly used in finance, such as NumPy, Pandas, Matplotlib, Jupyter Notebook and Google Colab. Lectures are followed by in-depth practical examples using tools that show real-world implications.

Linked modules

Pre-Req: MANG1028

Aims and Objectives

Learning Outcomes

Knowledge and Understanding

Having successfully completed this module, you will be able to demonstrate knowledge and understanding of:

Apply Python programming language
Solve programmatically quantitative financial problems.
Perform data analysis and visualisation in Python

Subject Specific Intellectual and Research Skills

Having successfully completed this module you will be able to:

Select appropriate solutions and platforms for programming in Python.
Build Python scripts for basic statistical and financial applications.
Program, read and understand Python scripts.

Transferable and Generic Skills

Having successfully completed this module you will be able to:

Transfer Python programming knowledge to other programming languages
Collect and analyse quantitative data.
Demonstrate Python programming skills for financial and non-financial problems.

Learning and Teaching

Teaching and learning methods

-Weekly lectures introduce concepts and demonstrate how to use Python to solve statistical/finance-related problems. -Weekly computer-based classes will supplement the lectures, which will support student learning by providing opportunities for students to attempt and gain feedback on numerical and problem-solving exercises. Students will also have the opportunity for both directed and non-directed independent reading.

Study time
Type	Hours
Revision	50
Lecture	24
Preparation for scheduled sessions	17
Seminar	10
Follow-up work	34
Wider reading or practice	15
Total study time	150

Resources & Reading list

General Resources

Reading List. The complete updated Reading List link can be found on the module's Blackboard site.

Blackboard. All resources for this module will be detailed on the module's Blackboard site.

Textbooks

Yves J. Hilpisch (2020). Python for finance: mastering data-driven finance.

Yves J. Hilpisch (2022). Financial theory with Python : a gentle introduction.

Assessment

Formative

This is how we’ll give you feedback as you are learning. It is not a formal test or exam.

In-class activities

Assessment Type: Formative
Feedback:
Final Assessment:
Group Work: No

Summative

This is how we’ll formally assess what you have learned in this module.

Breakdown
Method	Percentage contribution
Individual Coursework	100%

Referral

This is how we’ll assess you if you don’t meet the criteria to pass this module.

Breakdown
Method	Percentage contribution
Individual Coursework	100%

Repeat

An internal repeat is where you take all of your modules again, including any you passed. An external repeat is where you only re-take the modules you failed.

Breakdown
Method	Percentage contribution
Individual Coursework	100%