Programming for Business Analytics

Module title	Programming for Business Analytics
Module code	BEF2011DA
Academic year	2024/5
Credits	15
Module staff

Duration: Term	1	2	3
Duration: Weeks	11

Number students taking module (anticipated)	20

Module description

Students will be introduced to object-oriented programming with Python as its core language. Using this tool, they will address the financial industry's technological challenges and financial data analytics. To do so, students will learn how to manage Python environments and libraries.

We will introduce Python data types and structures through the use of financial datasets. A series of statistical recapitulations will follow to determine what best fits the available data in order to perform some visualization and the best Python function to analyze it.

By the end of this module, students will have undertaken several coding exercises and challenges related to the Finance literature (e.g., CAPM, testing hypothesis, fraud detection).

Students will also be introduced to standard machine-learning algorithms in finance, a core subfield of artificial intelligence.

By this module's end, students will have a good understanding of how to use software and technologies to tackle complex problems independently.

Module aims - intentions of the module

This module aims to enhance students' data analytics and digital competencies within the Banking and Finance fields and beyond. In addition, students will receive a comprehensive introduction to algorithm design and analysis, core programming skills (all taught in Python with financial application in mind).

Products and Services (K9)

Systems and Processes (K13, 14)

Developing self and others (S29, 31)

Attention to Detail (B42)

Adaptability (K37)

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

On successfully completing the module you will be able to...

1. Products and Services (K9) Distinguish between quantitative and qualitative business analytics processes and techniques;
2. recognise the theory and rationale behind various analysis and research methods;
3. identify practical issues and limitations surrounding business analytics projects;
4. Systems and Processes (K13, K14) Interpret and apply data sources, statistics, and relevant software;
5. use coding techniques to share original content and insight through visualisation.
6. Display proficiency with coding and analytical tools, particularly the manipulation of data

ILO: Discipline-specific skills

On successfully completing the module you will be able to...

7. Developing self and others (S29, 31) identify the latest developments in technology and programming;
8. use contemporary technology to tackle complex problems.

ILO: Personal and key skills

On successfully completing the module you will be able to...

9. Attention to Detail (B42) Demonstrate key skills in coding and analytical practices, including data handling;
10. Adaptability (K37) utilise investigatory skills in order to find solutions to complex logic problems;
11. complete multistage tasks within a defined period whilst assisted by supervision

Syllabus plan

The module syllabus will include the following topics:

Introduction to Python
Handling financial data with the ‘pandas’ software library
Statistical analysis in financial discipline.
Visualisation and report presentation
Fraud detection
Web scraping for banking and finance research
Machine learning to enhance empirical finance methods

Learning activities and teaching methods (given in hours of study time)

Scheduled Learning and Teaching Activities	Guided independent study	Placement / study abroad
40	110	0

Details of learning activities and teaching methods

Category	Hours of study time	Description
Scheduled Learning and Teaching Activity	14	Masterclass/workshops
Scheduled Learning and Teaching Activity	18	Online lectures and webinars
Scheduled Learning and Teaching Activity	6	Guided Revision
Guided Independent Study	112	Reading and research, web-based activities

Formative assessment

Form of assessment	Size of the assessment (eg length / duration)	ILOs assessed	Feedback method
Online Discussion	Online discussion contributions	1-11	Online discussion feedback from peers and lecturer
Weekly questions	Questions	1-8	Correct answers on ELE
Case Studies	Practical case studies	1-11	Feedback and suggested solutions provided on ELE

Summative assessment (% of credit)

Coursework	Written exams	Practical exams
100	0	0

Details of summative assessment

Form of assessment	% of credit	Size of the assessment (eg length / duration)	ILOs assessed	Feedback method
Applied Exercise/Assignment	100	3000 words	1-11	Written feedback
	0
	0
	0
	0
	0
	0

Details of re-assessment (where required by referral or deferral)

Original form of assessment	Form of re-assessment	ILOs re-assessed	Timescale for re-assessment
Applied Exercise/Assignment (100%) 3000 words	Applied Exercise/Assignment (100%) 3000 words	1-11	Next available opportunity

Re-assessment notes

Defer – as first time

Refer – capped at 40%

Indicative learning resources - Basic reading

Python for Finance, 2^nd Edition (2018) by Yves Hilpisch
Python for Finance Cookbook: Over 50 recipes for applying modern Python libraries to financial data analysis (2020) by Eryk Lewinson

Extra Readings:

Matthes, E. (2019). Python Crash Course (2nd Edition). No Starch Press, Inc.
Python for Excel (2021) by Felix Zumstein
Python for Data Analysis, 3^rd Edition (2022) by Wes Mckinney
Essentials of Statistics for Business & Economics
Introductory Econometrics for Finance by Chris Brooks

Reading lists for the different academic areas covered in this module will be provided within the relevant study sessions

Indicative learning resources - Web based and electronic resources

Web based and electronic resources introduced but not limited to: Anaconda, Spyder, Python Crash Course, Github, Kaggle, Stackoverflow, Automatetheboringstuff, towardsdatascience and application/library specific resources

Indicative learning resources - Other resources

Reading lists for the different academic areas covered in this module will be provided within the relevant study sessions

Key words search

Data; Coding; Python; Analysis; Analytics; Statistics

Credit value	15
Module ECTS	7.5
Module pre-requisites	None
Module co-requisites	None
NQF level (module)	6
Available as distance learning?	No
Origin date	18/08/2020
Last revision date	20/11/2023