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Programming for Business Analytics

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.

Full module specification

Module title:Programming for Business Analytics
Module code:BEF2011DA
Module level:2
Academic year:2023/4
Module lecturers:
Module credit:15
ECTS value:






Duration of module: Duration (weeks) - term 1:


Module aims

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)


ILO: Module-specific skills

  • 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

  • 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

  • 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

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

Scheduled Learning and Teaching ActivitiesGuided independent studyPlacement / study abroad

Details of learning activities and teaching methods

CategoryHours of study timeDescription
Scheduled Learning and Teaching Activity14 Masterclass/workshops
Scheduled Learning and Teaching Activity18Online lectures and webinars
Scheduled Learning and Teaching Activity6Guided Revision
Guided Independent Study112Reading and research, web-based activities

Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
Online DiscussionOnline discussion contributions1-11Online discussion feedback from peers and lecturer
Weekly questionsQuestions1-8Correct answers on ELE
Case Studies Practical case studies1-11Feedback and suggested solutions provided on ELE

Summative assessment (% of credit)

CourseworkWritten examsPractical exams

Details of summative assessment

Form of assessment% of creditSize of the assessment (eg length / duration)ILOs assessedFeedback method
Exam501-hour written exam1-8Written feedback
Applied Exercise/ Assignment502,000 words1-11Written feedback

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

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
Exam (50%)1-hour written exam (50%)1-8Next available opportunity
Applied Exercise/ Assignment (50%)2,000 words (50%)1-11Next available opportunity

Re-assessment notes

Defer – as first time

Refer – capped at 40%

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

Indicative learning resources - Basic reading

Indicative learning resources - Basic reading

  • Python for Finance, 2nd 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, 3rd 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

Module has an active ELE page?


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

Origin date


Last revision date