Master of Science in Business Analytics

What modules will I study?

• Semester 1

  Title: Business Analytics & Modelling

  Credits: 5

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  This module will develop learners understanding of how analytics can impact decision-making and provide learners with analytical skills to understand, predict and optimise business operation and performance. Learners are introduced to key business analytics concepts, such as data exploration, mining, forecasting and optimisation. An important feature of the course is the use of MS Excel, add-ins Analytic Solver Platform and XLMiner through illustrative examples.

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  Title: Business Strategy

  Credits: 5

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  This module introduces students to strategic management and strategic issues from a business analyst point of view. It introduces a range of contemporary issues associated with the formulation and implementation of corporate and business strategies with an emphasis on identifying and implementing strategic change within the organisation, building dynamic capabilities and developing coherent strategies. Issues might include strategies in the face of uncertainty, global strategies and knowledge-based strategies.

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  Title: Relational Databases

  Credits: 5

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  This module provides the student with the skills in areas such as entity modelling, normalisation and database design. In addition, significant time will be devoted to utilising the SQL language to operationalise the output of the design process, manage data and manage security in a modern relational database management system.

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  Title: Statistics And Tools For Business Analytics

  Credits: 10

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  This module will introduce students to the role of Excel macros and VBA programming to derive insight and automate processes involving large datasets. Practical work will give the student experience in applying statistical techniques to data and in programming VBA to instruct Excel to perform tasks.

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• Semester 2

  Title: Applied Business Analytics

  Credits: 5

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  This module teaches learners the importance of data manipulation and pre-processing, exploration, analysis, and modelling.  Learners will analyse customer data sets to assess what data preparation is required to clean the data. Explore several appropriate techniques to improve data quality. Examine the different methods for dealing with missing data. Perform a range of advanced data processing techniques such as data validation in preparation for data analysis. The module also contains a modelling component where learners:

  • build linear and non-linear models.
  • assess model performance and variable selection
  • learn how to write clear reports that convey the results of the analysis.

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  Title: Data Interpretation and Business Analytics

  Credits: 5

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  Students will learn how to use data to drive their marketing activity and measure the effectiveness of their website, including how to set appropriate KPIs for digital channels to measure effectiveness. Students will have a clear understanding of how to measure and monitor their online audience. Learners will explore and evaluate how businesses may make use (or better use) of big (and not so big) data to improve business decision-making and overall performance, including how data may be better used to understand customers, enhance customer loyalty, and gain new customers through improved profiling and segmentation.

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  Title: Data Visualisation

  Credits: 10

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  This module develops student skills in data visualisation by introducing various data visualisation techniques. Students will learn how to explain the insights obtained from large data sets using data visualization techniques. One of the essential skills in data analytics is the ability to tell a story, visualizing data and findings. Students will learn how to use various techniques to present data visually, obtain a better understanding of the data, and make more effective decisions.

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  Title: Research Methods

  Credits: 5

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  This module aims to introduce students to the key concepts involved in research and to develop their understanding of the uses and relevance of the major methodologies employed. The material covered in this module will form the basis for the research dissertation element of the MSc in Data Analytics programme, with one of the key outcomes of this module being a valid and robust research proposal for research in the area of Data Analytics.

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  Title: The Science of Decision Making

  Credits: 5

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  This module introduces the student to the science of decision making and is designed to help students become more effective decision makers. It aims to help students to understand and improve the judgement and decision making of individuals, groups, and organisations.

  Decision making is a central aspect of any business activity. The ability to understand how decisions are made, and to predict, guide and improve those decisions is a key part of every change maker’s toolbox.  In this era of deep uncertainty, rapid change and big data, making the right strategic decisions is more vital than ever.

  The module explores theories and methods drawn from psychology, economics, philosophy, statistics, and management science.

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• Semester 3

  Title: Applied Research Project

  Credits: 30

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  The project builds on the Research Methods module, where the research proposal will have been developed and submitted. The dissertation will consist of 20,000 words, excluding appendices. As the capstone component of the programme, this element will help integrate the curriculum content, and working in conjunction with an approved industry partner, deliver a significant body of work that will contribute to the body of knowledge in the field of data analytics. The dissertation will draw on analytical and evaluative competences based upon knowledge and skills developed during the programme. The exercise also provides an opportunity for students to develop their interests in a particular area of Data Analytics, while working with the industry partner and to demonstrate an ability to undertake individual research in an ethical and methodologically sound manner.

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