Business Analytics and Research Skills

Module description

Making accurate causal claims is fundamental to successful business decisions. This module will teach you how to design, source and analyse both quantitative and qualitative business data to make fully informed decisions about the strategic management of an organisation or business. We will use interactive lecture and case study materials to build real world scenarios that will help you develop your abilities to use business analytics techniques to their maximum effect. In this module we will cover topics such as data collection, survey, experimental design and analytical skills to address the challenges facing business.

Internationalisation:  the module will draw on recent scholarship in the areas of data and analytics published by researchers internationally (the UK, Europe, the United States) and case studies based on a variety of national contexts.

Employability: the module will offer an opportunity to acquire knowledge and develop analytical skills for a broad range of areas, e.g. strategy, planning and marketing.

Full module specification

Module title:Business Analytics and Research Skills
Module code:BEMM389
Module level:M
Academic year:2020/1
Module lecturers:
  • Professor Joshua Ignatius - Convenor
Module credit:15
ECTS value:


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


Module aims

The module aims to enhance your ability to model a business problem, as well as to collect, structure and analyse data for generating managerial implications.

Specifically we will consider:

  • How to collect structured and unstructured data.
  • How to conduct survey and launch an experimental design protocol.
  • How to model business problems and derive solutions through data analytics approaches.

ILO: Module-specific skills

  • 1. critically evaluate current approaches used for collection, management, communication and analysis of structured and unstructured data, and how these results can support informed decision-making;
  • 2. apply counterfactual reasoning to the analysis of a specific business challenge;
  • 3. demonstrate familiarity with analytical tools available for the analysis of structured and unstructured data and use these to find, derive and evaluate information;
  • 4. apply survey or experimental techniques to address challenges facing an organisation.

ILO: Discipline-specific skills

  • 5. design surveys or experimental approaches for addressing business challenges;
  • 6. demonstrate the use of appropriate analytical techniques for identified business problems.

ILO: Personal and key skills

  • 7. critically reflect upon challenges within the analytics field;
  • 8. demonstrate effective independent study and research skills.

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 Activity3Online module introduction and basic underpinning theoretical content
Scheduled Learning and Teaching Activity11Whole cohort lecture
Scheduled Learning and Teaching Activity10Small group workshop
Guided Independent Study40Preparatory reading prior to workshops and lectures
Guided Independent Study40Practice use of software and concepts from additional exercises and examples
Guided Independent Study46Assignment preparation

Formative assessment

Form of assessmentSize of the assessment (eg length / duration)ILOs assessedFeedback method
Review of individual performance on group exercisesDuring workshops / tutorials1-8Verbal
Outline plan for assessed reportOne page1-8Electronic/Verbal

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 (take-home) coursework exercise304 hours1-6, 8Electronic, written comments
Individual Report702000 words1-8Electronic, written comments

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

Original form of assessmentForm of re-assessmentILOs re-assessedTimescale for re-assessment
Practical (take-home) coursework exercisePractical (take-home) coursework exercise (4 hours)1-6, 8August resit period
Individual ReportIndividual Report (2000 words)1-8August resit period

Syllabus plan

  • Introduction to decision making models and data analytics
  • Correlation and Causality
  • Data Collection Processes
  • Conducting Surveys and Experiments
  • Introduction to analytical tools, including R / Python

Indicative learning resources - Basic reading

  • Ledolter, J. (2013). Data mining and business analytics with R. Hoboken, NJ: Wiley.
  • Sekaran, U., & Bougie, R. (2016). Research methods for business: A skill building approach. John Wiley & Sons.

Module has an active ELE page?


Indicative learning resources - Web based and electronic resources

  • Kleinberg, S. (Ed.). (2019). Time and Causality Across the Sciences. Cambridge University Press. (Available as E-book through Encore)

Origin date


Last revision date