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FIN42330

Academic Year 2023/2024

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 and Pandas and write functions using conditional statements and loops.

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 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

Student Effort Hours:
Student Effort Type Hours
Autonomous Student Learning

100
Lectures

24
Tutorial

6
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
Not applicable to this module.
 

Assessment Strategy
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Examination: Midterm Week 4 No Alternative linear conversion grade scale 40% Yes
30
 Yes
Group Project: A Group project to cover the topics of the module. Week 6 n/a Alternative linear conversion grade scale 40% No
50
 No
Continuous Assessment: Tutorials/Assignments on the core concepts of the module. Throughout the Trimester n/a Alternative linear conversion grade scale 40% No
20
 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).

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