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Business Analytics in Practice

Module description

This module will introduce the key concepts and ideas about how business analytics is used in practice. It will highlight the importance of data to organisations and how it can be used to improve firm performance in relation to competitors, customers, employees and processes. The module will introduce you to how data and analytic methods can be used to support an organisation’s overall strategy. The module will also consider ethical, regulatory and security issues associated with data and its use within a business environment

Full module specification

Module title:Business Analytics in Practice
Module code:BEM2039
Module level:2
Academic year:2023/4
Module lecturers:
  • Livio Fenga - Convenor
Module credit:15
ECTS value:






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


Module aims

Business analytics, data science, digital economy, big data – with buzzwords coming and going it can be a complex and confusing picture, and difficult to know where to start.  This module aims to introduce key topics that you’ll need to explore in more depth as you proceed on your ‘business analytics journey’. 

ILO: Module-specific skills

  • 1. identify and apply appropriate analytics methods and tools to a range of business situations;
  • 2. demonstrate appreciation of contemporary issues in management and use of data - including ethics, governance, and change management.

ILO: Discipline-specific skills

  • 3. communicate effectively through oral presentations and written reports, presenting methodologies and findings in a way that is appropriate to the intended audience.

ILO: Personal and key skills

  • 4. demonstrate a collaborative mind-set: Our graduates are enterprising and motivated individuals who are able to actively collaborate and effectively communicate within a range of diverse settings;
  • 5. demonstrate a global outlook: Our graduates are engaged and prepared for the demands of global business and society;
  • 6. demonstrate an ethical ethos: Our graduates understand the social, financial and environmental factors that can impact on corporate sustainability and are able to make decisions openly and responsibly;
  • 7. epitomise a critical thinker: Our graduates have a commercial awareness that enables them to critically analyse, conceptualise and evaluate the challenges facing business.

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 Activity36Scheduled lectures and workshops
Guided Independent Learning60Background and preparatory reading
Guided Independent Learning54Preparation for assessments

Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
In-class quizzes and exercisesIn-class1-5Verbal
Group presentationIn-class1,2,5,6Verbal

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
Group presentation (video)4015 minutes1,2,3,4,5,6Written
Individual coursework602000 words1,2,3,7Written

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

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
Group presentationIndividual presentation 15 mins (40%)1,2,3,4,5,6August/September Reassessment Period
Individual courseworkIndividual coursework (2000 words) (60%)1,2,3,7August/September Reassessment Period

Syllabus plan

The following content will be covered during the course

  • ‘The fourth industrial revolution’, analytics and data science in business
  • Data ethics, governance and professional responsibility
  • Analytics and data science processes and frameworks
  • Introduction to key data science & modelling concepts
  • Introduction to tools commonly used in analytics and data science

Indicative learning resources - Basic reading

Guided reading will be provided via module ELE pages in advance of scheduled lectures and workshops. The following resources, available online via the University Library, are indicative of the type and level of information you should expect to review. 

Marr, B. (2015) Big data in practice: how 45 successful companies used big data analytics to deliver extraordinary results. Wiley

Ford, M. (2018) Architects of intelligence: the truth about AI from the people building it. Packt Publishing

Marr, B. (2017) Data strategy: how to profit from a world of big data. Kogan Page.

Provost, F & Fawcett, T. (2013) Data science for business. O’Reilly.

You should also expect to review materials from academic journals such as Management Science (INFORMS) and Journal of Business Analytics (Taylor and Francis), and publications representative of current analytics practice within industry and government.

Module has an active ELE page?


Origin date


Last revision date