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Business Analytics

Module description

This module will explore the role of information and analytics in supporting the development of strategies, and the practical techniques managers can use to design effective information flows.  You will be introduced to key concepts in data science for business, including fundamentals of data management, modelling, analysis and visualisation. You will consider how ‘design thinking’ can be used to effectively develop and deploy analytics systems within businesses, including the roles of descriptive, diagnostic, predictive and prescriptive systems. There are no pre-requisites but useful complementary modules to have taken in the first year include GEO1419 Introduction to Data Science.

Full module specification

Module title:Business Analytics
Module code:BEP2140
Module level:2
Academic year:2023/4
Module lecturers:
  • Dr Eunice Oppon - Convenor
Module credit:15
ECTS value:






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


Module aims

Businesses are constantly faced with making decisions and managers are expected to make the right decisions to optimise their operations. The aim of this module is to equip students with the ability to use variety of quantitative tools in analysing data to aid decision making in business. Students will explore the use of the different quantitative tools for analysing data in realistic business situations including finance, marketing, operations and other areas of business. On completion of the module, students will develop competencies in the use of analytic software particularly Excel for data analysis and data visualisations to support effective decision making.

ILO: Module-specific skills

  • 1. Define business analytics (BA), related concepts and the importance of BA in a business environment
  • 2. Conduct data analysis and construct data visualisations using basic and advanced excel functions to aid management decision making
  • 3. Plan and execute quantitative analysis using descriptive, predictive and prescriptive analytical tools
  • 4. Interpret the results of quantitative analysis and investigate potential issues

ILO: Discipline-specific skills

  • 5. Critically evaluate the various factors which impact on effective management of analytics data when using descriptive, predictive and prescriptive analytical tools in a variety of business contexts
  • 6. Effectively communicate findings to a variety of audiences

ILO: Personal and key skills

  • 7. Example computer literacy and analytical skills
  • 8. Display an understanding for decision making analysis in a business context

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 Activity11Lectures (11 x 1 hour)
Scheduled Learning and Teaching Activity11Tutorials (11 x 1 hour)
Guided Independent Study128Reading, research and assessment preparation

Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
In class quizzes and multiple choice exercises During each class1-8Verbal 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
Written exam401 hour open book exam1,5,8Written
Practical exam602 hours lab based, open book practical exam1-8Written

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

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
Written exam (1 hour open book exam) (40%)Written exam (1 hour open book exam) (40%)1,5,8August re-assessment period
Practical exam (2hr lab based, open-book practical exam) (60%)Practical exam (2hr lab based, open-book practical exam) (60%)1-8August re-assessment period

Syllabus plan

Topics discussed on the module include (not exclusively):


  • Foundations of Business Analytics. Students will be introduced to basic concepts in business analytics including the use of basic data management and analytic software tools such as Excel.
  • Descriptive Analytics. One important aim of business analytics, is to present meaningful and easy to understand information from the large data analysed. To achieve this, students will be introduced to data visualisation, descriptive statistics, probability and decision making under uncertainty.
  • Predictive Analytics. Topics to be covered include times series and regression analysis, forecasting, use of spreadsheet to build and analyse data models.
  • Prescriptive Analytics. Linear optimisation and its application.
  • Making Decisions. Central idea of business analytics is to assist managers in decision making. Students will be introduced to the underlying philosophies, tools and techniques of decision analysis.

Indicative learning resources - Basic reading

The following books are a useful resource for this course:


  • Evans, J.R., 2017. Business analytics, Global edition England: Pearson.
  • Albright, S.C. and Winston, W.L., 2014. Business analytics: Data analysis & decision making. Cengage Learning.

Module has an active ELE page?


Indicative learning resources - Other resources

A more comprehensive bibliography will be available to students taking this course.

Origin date


Last revision date