2024/25 Taught Postgraduate Module Catalogue

LUBS5056M Python Programming for Finance

15 creditsClass Size: 100

Module manager: Nabi Omidvar
Email: m.n.omidvar@leeds.ac.uk

Taught: Semester 2 (Jan to Jun) View Timetable

Year running 2024/25

This module is not approved as an Elective

Module summary

This module introduces Python programming with a focus on its application in the financial sector, including working with big data. Starting with the basics of Python, students will learn how to access and manipulate files, perform data manipulation and transformation on large-scale financial datasets, and get an introduction to data analysis and visualisation. The course also introduces predictive analytics in finance, demonstrating how Python can be employed to analyse big data for solving real-world financial problems.No prior knowledge of programming is required, making this module a suitable starting point for finance students looking to acquire coding skills and those interested in applying data analysis within the financial sector. It aims to equip students with the ability to develop Python programmes for various financial use-cases, from big data manipulation to basic predictive analytics, setting a strong foundation for further exploration into financial technology applications. This module serves as an entry point to data science in finance and prepares students for more advanced topics around AI, machine learning, and deep learning, with a particular emphasis on big data applications.

Objectives

This module introduces the essentials of programming and software development with a focus on finance. It aims to equip students with the skills required to develop computer applications capable of managing and analysing large-scale financial datasets, or "big data," encompassing data processing, manipulation, and visualisation. Students will gain the experience needed to handle financial datasets effectively, enabling them to generate visual representations and perform advanced big-data manipulations with efficiency. The course is structured to prepare students for further exploration into advanced analytics for more sophisticated financial modelling.

Learning outcomes
Upon completion of this module students will be able to:
1. Design, build, and test Python programmes, focusing on skills applicable to the financial sector.
2. Implement big data analysis applications using Python, tailored for financial use-cases.
3. Develop systems that connect with external data sources, facilitating big data manipulation and transformation to enhance financial data analysis capabilities.
4. Create data visualisations to communicate financial insights from large-scale datasets clearly and effectively.
5. Apply data analytics using Python for basic predictive modelling in finance.

Skills outcomes
On completion of this module, students will be able to:
- Master the essentials of Python programming, laying the foundation for its application in the financial services industry.
- Manipulate and analyse large financial datasets using Python, enabling the handling of Big Data and scaling complex financial analyses beyond the limitations of traditional tools and manual processing.
- Construct basic predictive models to generate insights and forecasts, essential for strategic planning and risk management across financial services.
- Visualise financial data effectively and interactively, facilitating clear communication of complex insights to stakeholders in the financial sector.
- Access and manage financial data from diverse sources efficiently, helping to streamline data integration and analysis for agile decision-making in the financial sector.

Syllabus

1. Python Programming Foundations
2. Data Handling and Manipulation
3. Accessing Financial Data with Python
4. Analysis and Visualisation of Financial Data
5. Applied Text Mining for Finance
6. Introduction to Predictive Analytics in Finance

Teaching methods

Delivery type	Number	Length hours	Student hours
Lecture	10	2.00	20.00
Practical	10	1.00	10.00
Independent online learning hours			5.00
Private study hours			115.00
Total Contact hours			30.00
Total hours (100hr per 10 credits)			150.00

Private study

Lecture materials: students are expected to read lecture material. 4 hours per lecture = 40 hours.
Coding practice: students are expected to practice coding on their own. 6 hours per lecture = 60 hours.
Research and embedded exercises: students are expected to spend 2 hours per lecture conducing their own research around certain topics and exercises embedded in the material. 2 hours per lecture = 20 hours.

Opportunities for Formative Feedback

The module offers ample opportunities for formative feedback. In weekly practical lab sessions, students will tackle guided exercises, allowing them to directly apply concepts learned in lectures and receive immediate feedback. Additionally, bi-weekly short multiple-choice quizzes focusing on core concepts from the lectures will reinforce learning and provide insights into students' comprehension. Furthermore, bi-weekly online guided code walkthroughs with embedded exercises will offer students a chance to deepen their understanding of coding practices.

Methods of assessment

Coursework

Assessment type	Notes	% of formal assessment
Report	Data analytics project with a 3,000-word report	100.00
Total percentage (Assessment Coursework)		100.00

The resit for this module will by 100% by 3,000 word report.

Reading list

The reading list is available from the Library website

Last updated: 16/08/2024 11:44:41

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