Loughborough University
Leicestershire, UK
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Loughborough University

Programme Specifications

Programme Specification

MSc Business Analytics (2020 entry)

Academic Year: 2020/21

This specification provides a concise summary of the main features of the programme and the learning outcomes that a typical student might reasonably be expected to achieve and demonstrate if full advantage is taken of the learning opportunities that are provided.

This specification applies to delivery of the programme in the Academic Year indicated above. Prospective students reviewing this information for a later year of study should be aware that these details are subject to change as outlined in our Terms and Conditions of Study.

This specification should be read in conjunction with:

  • Summary
  • Aims
  • Learning outcomes
  • Structure
  • Progression & weighting

Programme summary

Awarding body/institution Loughborough University
Teaching institution (if different)
Owning school/department School of Business and Economics
Details of accreditation by a professional/statutory body
Final award MSc/ PGDip
Programme title Business Analytics
Programme code BSPT37
Length of programme The minimum period of study for the award of MSc is twelve calendar months (i.e. completed programme taught element plus project). For the award of PGDip is nine calendar months (i.e. completed programme taught element).
UCAS code
Admissions criteria

Full Time MSc - http://www.lboro.ac.uk/BSPT37

Date at which the programme specification was published Thu, 18 Jun 2020 09:27:58 BST

1. Programme Aims

  • To equip students with a broad range of knowledge and analytics methodologies, techniques and tools to enable them to work effectively in supporting problem-solving and decision-making in a business or policy context;

  • To build students’ analytics consulting skills in a number of areas within business and government (e.g.  marketing, operations, policy);

  • To enable students to exploit the opportunities offered by the availability of ‘big data’ within and between organisations (e.g. managing big data, leading analytics initiatives);

  • To produce graduates with the ability to use rigorous quantitative and qualitative model-supported analyses to help tackle complex problem or decision situations within organisations; 

  • To enhance students’ lifelong learning skills and personal development in preparation for a professional career in analytics within business and government, or as preparation for further research in the field of analytics;

  • To provide a relevant, practical and constantly updated programme  through close links with many analytics experts or users in business and government, and with a taught content and practical experience that enable students to identify opportunities for the deployment of analytics in practice.

2. Relevant subject benchmark statements and other external reference points used to inform programme outcomes:

  • The QAA benchmark statement for Master's awards in business and management.

  • The Framework for Higher Education Qualifications.

  • University Learning and Teaching Strategy.

  • Teaching and learning policies of the School of Business and Economics.

  • The increasing take-up of analytics within business and government.

  • The research interests and specialisms of the School of Business and Economics academic staff and their professional involvement in their disciplines.

3. Programme Learning Outcomes

3.1 Knowledge and Understanding

On successful completion of this programme participants should be able to demonstrate knowledge and understanding in the following areas: 

K1        The main business or policy application areas in industry, and of current and leading edge research in these areas.

K2        A range of analytics approaches, techniques and tools for analysing big data, together with their expected benefits and limitations.

K3        Current thinking and issues relevant to big data.

K4        The role of the analytics professional and the process of deploying effective analytics projects in organisations.

3.2 Skills and other attributes

a. Subject-specific cognitive skills:

On successful completion of this programme students should be able to:


C1       Construct original analytics-driven insights that draw on appropriate evidence from a variety of sources.

C2       Critically analyse, evaluate, and synthetize the practice of analytics.

C3       Critically appraise the relative importance and relevance of different sources of data to the deployment of analytics, and recognise and address issues relevant to management of big data.

C4       Recognise opportunities to apply a wide range of analytics approaches in organisations, deployed effectively and critically in either expert or facilitative modes, to a wide range of problem and decision situations.

b. Subject-specific practical skills:

On successful completion of this programme students should be able to:

P1        Apply model-supported problem structuring and decision analysis skills to formulate complex or unstructured problem or decision situations, distinguish facts from values, define objectives, preferences, constraints and assumptions, and create and evaluate options.

P2        Make effective use of information and communication technologies, including the appropriate selection and competent application of a range of relevant computer software for deploying analytics.

P3      Conduct research effectively and efficiently into the field of analytics, using a variety of data, information and knowledge sources.

P4        Undertake, and manage effectively, an analytics-driven project in a problem or decision situation within a business or policy context, selecting and employing various methodologies, techniques and tools as appropriate, and developing helpful interactions with analytics users or experts

c. Key transferable skills:

On successful completion of the programme students should be able to:

T1        Communicate complex ideas and arguments effectively, both orally and in writing and using a range of media, to expert and lay audiences.

T2        Work effectively with others in a team environment, recognising and utilising individuals’ contributions in group processes, and displaying effective negotiation and project management skills when needed.  

T3:       Demonstrate high personal effectiveness, including critical self-awareness, self-reflection and self-management, sensitivity to diversity in people and situations, time management, and the ability to take responsibility for their own learning, and to continue learning through reflection on practice and experience.

T4:       Use information technology to scan, organise and assess relevant information for problem solving, decision making and sharing knowledge.

T5:       Analyse complex problems and develop novel solutions, and apply numerical reasoning appropriately in problem solving processes. 

Learning outcomes associated with the PGDip do not include K4, C4, P4 and T5.,








4. Programme structure

The MSc programme lasts one-year full-time, and is divided into a taught and project component. Students take 8 compulsory taught modules delivered across two semesters, which amount to 120 credits. In the summer students take a supervised analytics consulting or research project worth 60 credits. 




Modular weight



Skills for Consulting Projects




Discovery Analytics




Managerial Decision Modelling




Managing Big Data




Customer Analytics




Logistics Modelling and Operations Analytics




Policy and Strategy Analytics




Process and Programming for Analytics




Analytics Project




The PGDip is awarded after successful completion of all eight 15-credit taught modules.




5. Criteria for Progression and Degree Award

In order to be eligible for the award, candidates must satisfy the requirements of Regulation XXI. In addition, and in accordance with Regulation XXI, candidates who have a right of re-assessment in a module may choose to be re-assessed in the University's special assessment period.

6. Relative Weighting of Parts of the Programme for the Purposes of Final Degree Classification

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