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University of Exeter Business School

Programming for Business Analytics

Module titleProgramming for Business Analytics
Module codeBEMM458
Academic year2023/4
Module staff

Dr Aishwaryaprajna Aishwaryaprajna (Convenor)

Mr Ross Hollyman (Convenor)

Duration: Term123
Duration: Weeks

11 - Sept start

11 - Jan start

Number students taking module (anticipated)


Module description

In this module you will learn fundamental programming skills that enable you to search and sort data. You will be introduced to programming in Python and will learn how to develop and run programmes in Jupyter Notebooks. You will learn key programming principles and will practice applying them to real business problems. These skills will form the basis of your ability to address business problems using data.

Module aims - intentions of the module

This module aims to give a comprehensive introduction to the programming skills that underpin Business Analytics and Data Science. You will learn to: 

  • Understand the role that programming plays in a Business Analytics context
  • Be confident writing, testing and debugging procedural and functional programmes in Python
  • Import and process data using Python
  • Understand the principles of object-oriented programming for Python

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

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

  • 1. P1: Demonstrate knowledge and understanding of fundamental, and domain-specific, analytics methods and tools
  • 2. P5: Create, manage, interrogate, interpret and visualise data from a wide range of different sources, types and including structured and unstructured forms

ILO: Discipline-specific skills

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

  • 3. P6: Critically analyse the use of data within a business context, identifying strengths and limitations
  • 4. P7: Critically analyse and interpret relevant academic, technical and industry literature

ILO: Personal and key skills

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

  • 5. P14: Technological and digital literacy: Our graduates are able to use technologies to source, process and communicate information

Syllabus plan

The following content will be covered during the course: 

  • Introduction to solving problems using software programming
  • Introduction to Python and Pandas library
  • Functions
  • Control Structures
  • Sequences and iteration
  • Data types and structures for Python
  • Data manipulation using Python and Pandas
  • Developing more complex programmes using Python

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 activity12Scheduled lectures
Scheduled learning and teaching activity24Scheduled labs and practical workshops
Guided independent study24Structured sessions and practical exercises via online resources, for example, Datacamp
Guided independent study60Guided reading and practice of technical skills
Guided independent study30Completion of coursework assessments

Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
In class quizzesDuring each class1-5Oral - in class

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
In class test 302 hour lab based, practical exam1-5Written
Final assignment70Written assignment to be delivered two weeks after the end of the module1-5Written

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

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
In class test In class test (30%)1-5Referral/deferral period
Final assignmentFinal assignment (70%)1-5Referral/deferral period

Re-assessment notes

Re-assessment will be in nature to the original assessment, but the topic, data, and materials must be new.

Deferral – if you miss an assessment for certificated reasons judged acceptable by the Mitigation Committee, you will normally be either deferred in the assessment or an extension may be granted. The mark given for a reassessment taken as a result of deferral will not be capped and will be treated as it would be if it were your first attempt at the assessment.

Referral – if you have failed the module overall (i.e. a final overall module mark of less than 50%) you will be required to re-take some or all parts of the assessment, as decided by the Module Convenor. The final mark given for a module where re-assessment was taken as a result of referral will be capped at 50%.

Indicative learning resources - Basic reading

The following book is a useful resource for this course. It is freely available online, and also available in printed format in the university library:

Think Python:

• Downey, A. (2012). Think Python: How to think like a computer scientist. Needham, Mass.: Green Tea Press

You may also find the following book useful for learning more about R. It is freely available online, and also available in printed format in the university library:

R for Data Science:

• Wickham, H., & Grolemund, G. (2016). R for data science: import, tidy, transform, visualize, and model data. Sebastopol, Calif.: O'Reilly

• There are further useful resources on the Python and R websites

• Further information and resources for the Jupyter Notebook interactive development environment are available on the Jupyter website

You will find information about how to install Python, R, and Jupyter Notebook on the module ELE pages. It also contains further information about other IDE’s, code editors and other useful tools for programming.

Key words search

Python, R, Programming, Analytic

Credit value15
Module ECTS


Module pre-requisites

This module is closed to MSc Business Analytics and MSc FinTech students only

Module co-requisites


NQF level (module)


Available as distance learning?


Origin date


Last revision date