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Programming for Business Analytics

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 R, 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.

Full module specification

Module title:Programming for Business Analytics
Module code:BEM1025
Module level:1
Academic year:2023/4
Module lecturers:
  • Dr Mohsen Mosleh - Convenor
Module credit:15
ECTS value:






Duration of module: Duration (weeks) - term 2:


Module aims

This module aims to give a comprehensive introduction to the programming skills that underpin Business Analytics and Data Science.  We will focus primarily on Python, but you will also learn how to use the R software environment. 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 and R
  • Import and process data using Python
  • Understand the principles of object-oriented programming for Python

ILO: Module-specific skills

  • 1. demonstrate knowledge and understanding of fundamental, and domain-specific, analytics methods and tools;
  • 2. 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

  • 3. critically analyse the use of data within a business context, identifying strengths and limitations.

ILO: Personal and key skills

  • 4. demonstrate technological and digital literacy: Our graduates are able to use technologies to source, process and communicate information.

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
Scheduled Learning and Teaching Activity24Structured 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 quizzes and multiple choice exercisesDuring each class1- 4Oral – 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
Practical exam 1402hr lab based, open-book practical exam1-4Written
Practical exam 2602hr lab based, open-book practical exam1-2Written

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

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
Practical exam 1 (40%)Practical exam 1 (2 hr, lab based, 40%) 1-4 August/September Reassessment Period
Practical exam 2 (60%)Practical exam 2 (2 hr, lab-based, 60%)1-2August/September Reassessment Period

Syllabus plan

The following content will be covered during the course.

  • Getting started with Python and R
  • Introduction to solving problems using programs
  • Functions
  • Control Structures
  • Sequences and iteration
  • Data types and structures for Python and R
  • Data manipulation using Python
  • Developing more complex programmes using Python
  • Introduction to Python libraries

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, Allen B, Downey, O’Reilly, second edition

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, Haley Wickham and Garrett Grolemund, O’Reilly, 2016

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.

Module has an active ELE page?


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