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University of Exeter Business School

Business Analytics

Module titleBusiness Analytics
Module codeBEP2140
Academic year2023/4
Module staff

Dr Eunice Oppon (Convenor)

Duration: Term123
Duration: Weeks




Number students taking module (anticipated)


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.

Module aims - intentions of the module

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.

Intended Learning Outcomes (ILOs)

ILO: Module-specific skills

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

  • 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

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

  • 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

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

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

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

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 1,5,8Written
Practical exam602 hours lab based 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 Written exam (1 hour exam) 1,5,8August re-assessment period
Practical exam Practical exam (2 hours lab based practical exam)1-8August re-assessment period

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

Indicative learning resources - Web based and electronic resources


  • ELE – College to provide hyperlink to appropriate pages

Indicative learning resources - Other resources

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

Key words search

Business Analytics, Data Management, Decision Making

Credit value15
Module ECTS


Module pre-requisites


Module co-requisites


NQF level (module)


Available as distance learning?


Origin date


Last revision date