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FIN42330

Academic Year 2024/2025

Python for Fin. Data Science (FIN42330)

Subject:
Finance
College:
Business
School:
Business
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr Emmanuel Eyiah-Donkor
Trimester:
Autumn
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module aims to introduce students to Python, an open-source programming language that has been used in a wide range of fields. Students will work with Python libraries such as NumPy, Pandas, SciPy, statsmodels, among others and use conditional logic and loop control flow statements and write custom functions.

An extensive amount of text data is available publicly and statistical analysis of qualitative data has been receiving a significant amount of attention in the recent years. Hence, it is important that students understand issues surrounding text data and adopt possible methods to analyse qualitative data. In addition to transforming, cleaning and analysing numerical data, students will learn to extract, read, process, write and store text files.

Student will work with advance processing methods such as lexicon-based methods and similarity measures using several Python packages and combine these results with the Ordinary Least Squares estimation technique. The module will also show students ways to create and store outputs. Towards the end of the module, students will develop a Python application and apply it to real-world datasets.

About this Module

Learning Outcomes:

1. Understand the fundamental principles of programming via the Python platform
2. Crawl data from public websites
3. Manage and process both structured and unstructured data
4. Learn text similarity measurements and lexicon-based approaches in sentiment analysis
5. Develop an application to analyse both structured and unstructured data

Indicative Module Content:

Part 1: Basics of Python Programming
(a) Python Basics, IPython, and JupyterLab
(b) Data Types and Structures
(c) NumPy for Numerical Computing
(d) Data Analyses with Pandas
(e) Object-oriented Programming
(f) Data File Formats

Part 2: Financial Application
(a) Statistics
(b) Econometric forecasting
(c) Portfolio optimization and trading strategies

Student Effort Hours:
Student Effort Type Hours
Lectures

24
Tutorial

6
Autonomous Student Learning

100
Total

130

Approaches to Teaching and Learning:
The main delivery of the Lectures are on a computer-aid Lecture/Tutorial where students are going to have direct interaction with the concepts of the module.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Incompatibles:
COMP41680 - Data Science in Python


 

Assessment Strategy
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Assignment(Including Essay): Four assignments (continuous assessment) on the core concepts of the module. Week 1, Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10, Week 11, Week 12 Alternative linear conversion grade scale 40% No
20
 No
Exam (In-person): Midterm test. It will assess the level of individual preparation attained so far in the module. Week 9 Alternative linear conversion grade scale 40% Yes
30
 Yes
Group Work Assignment: The group project assignment involves an empirical investigation covering the topics of the module. Week 7, Week 8, Week 9, Week 10, Week 11, Week 12 Alternative linear conversion grade scale 40% No
50
 No

Carry forward of passed components
Yes
 

Remediation Type Remediation Timing
In-Module Resit Prior to relevant Programme Exam Board
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Online automated feedback

How will my Feedback be Delivered?

Feedback on assessment will be provided on the basis of a rubric. Students will receive feedback 2-3 weeks after the assessment (approximately).

Recommended textbooks
1. Yves Hilpisch (2018). Python for Finance: Mastering Data-driven Finance, 2nd Edition (henceforth, YH). O’Reilly Media Inc.
2. Wes McKinney (2022). Python for Data Analysis: Data Wrangling with pandas, NumPy, & Jupyter, 2nd Edition (henceforth, WM). O’Reilly Media Inc. The “Open Access” html version of the book is available at https://wesmckinney.com/book.

Other Readings
1. VanderPlas, Jake (2016). Python Data Science Handbook: Essential Tools for Working with Data. O’Reilly Media Inc.
2. The official Python Tutorial.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
Autumn Lecture Offering 1 Week(s) - 7 Fri 11:00 - 12:50
Autumn Lecture Offering 1 Week(s) - 9, 10, 11, 12, 13 Mon 11:00 - 12:50
Autumn Tutorial Offering 1 Week(s) - 8, 9, 10, 11, 12 Thurs 10:00 - 10:50
Autumn Lecture Offering 1 Week(s) - 8, 9, 10, 11, 12, 13 Tues 11:30 - 13:20
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