MSc Business Analytics

Course Details

You will study four compulsory modules in term 1 to give you an overview of key business areas (plus the compulsory Project Skills module), and take four optional modules of your choice on this one-year course. Please note that availability and delivery modes may vary. All students will undertake a dissertation.

Assessment

Assessment is usually a mix of exams, group projects and coursework. Your project and dissertation will either be as a consultant to an organisation or as applied research.

Programming skills
We support the learning of multiple programming languages with access to a variety of interactive online materials, covering SAS programming (provided by the SAS Institute), R and Python (provided by Data Camp). Several modules feature application of such programming languages in their syllabus.

Dissertation
Your final dissertation could be a consultancy project or an individual research project. Both will require you to submit a 10,000 word dissertation at the end. You will often complete a client report in addition to your dissertation if you are working on a consultancy project. 

Previous dissertation projects

  • UniCredit Bulbank: Effect of Development of Personalized Offers to a Sub-Segment of Clients of UniCredit Bulbank
  • Sainsbury's: An Application of Multinomial Logit Modelling in E-fulfilment Demand Management
  • Barclays: Optimising branch staffing levels 
  • Dunlop oil & marine: improvement of short & long term sales forecasting methodology for a mainly project-based business 
  • Whittington Health: Scheduling patient appointments in an IAPT Service within the NHS 
     

Research project
A research project allows you to explore a topic critical to effective decision-making in organisations. Supervised by one of our academics, demonstrate you can undertake systematic and rigorous applied research over 12,000 words.

Previous research projects

  • Tackling Resource Contentions in Project Management
  • Exploiting AirBnB data to enhance hotel revenue management
  • Demand modelling for bike sharing
  • Spending trend and family structure
  • Travelling salesman problem: Investigating special structures in optimisation.

Compulsory Modules

Data Management

The main aims of the module are to familiarise you with spreadsheets, relational databases, SQL and tools designed to work with big data. By the end of the module, you will be able to demonstrate:

  • an ability to work with spreadsheets
  • an ability to extract, manipulate, join and store data in databases
  • an ability to read and write SQL queries
  • basic conceptual understanding and ability to work with big data technologies.
Project Skills

This module conveys the skills necessary to conduct the dissertation project. This will encompass soft consultancy skills to define and conduct a project for a client, problem structuring techniques, project management, academic writing and legally compliant data handling.

Analytics in Practice

With this module you will become familiar with the cross-industry standard process for data mining. The aim of the module is to teach you how to structure and conduct an analytical project including visualisation and communicating the project's results to the end-user.

Business Statistics

Gain a foundation in the analysis and presentation of quantitative data. Examine the basic elements of probability and statistics, essential to management science and operational research, and undertake computer-based analysis using a statistics package.

Optimisation Models

The module aims to develop your interest in, and knowledge and understanding of, various optimisation models to support decision-making in organisations. You will learn about the theoretical underpinnings of these models as well as how they are used in applications. You will gain practical experience in modelling and problem-solving. Topics covered include:

  • Optimisation modelling: mathematical programming, including extensive studies of linear programming and dynamic programming
  • integer programming, introduction to non-linear optimisation, introduction to algorithms and heuristic.

The techniques mentioned are illustrated on a range of real-life applications. IT tools (Excel Solver, Python libraries, etc.) are used to demonstrate the usage of the theory in practice.

 

Example Optional Modules

Advanced Analytics: Models and Application

This module introduces advanced analytics using different optimisation models and demonstrates them with applications ranging from healthcare, sports, social networks, to asset management and fraud detection.

 

Forecasting

This module provides an introduction to current quantitative forecasting methods, and its overall aim is to develop practical competence in their use. The module concentrates on models for short term forecasting, as these illustrate all the basic principles of analysing, comparing and extrapolating different models.

Strategy Analytics

Develop skills to successfully help companies to develop and rehearse strategies using modelling and analytical techniques that support a strategic planning process.

 

Advanced Data Analysis

Explore a range of sophisticated statistical methods to convert information into knowledge. Gain practical experience in the use of specialised software to analyse large sets of data information, and be able to report on the analysis in a practical way for improved management decision-making.

Pricing & Revenue Management

Pricing and revenue optimization – or revenue management as it is also called – focuses on how a firm should set and update pricing and product availability decisions across its various selling channels in order to maximise its profitability. In this module you will learn to identify and exploit opportunities for revenue optimisation in different business contexts.

Discrete Event Simulation

Students will learn the theoretical underpinnings of discrete-event simulation and gain practical experience in problem solving using commercial simulation software.

Supply Chain Analytics

Supply Chain Analytics aims to introduce various (analytical, mathematical and statistical) techniques to analyse supply chain performance and to identify inefficiencies and risk factors in operational, managerial and financial aspects of a supply chain.

Text Analytics

Developed in conjunction with the SAS Institute, topics covered include:

  • Introduction to Reporting using Tables & Graphics in SAS
  • Introduction to SAS Enterprise Miner
  • Predictive Modeling Techniques using SAS
  • Model Assessment
  • Model Implementation
  • Introduction to SAS Text Miner
  • Overview of Text Analytics
  • Applications of Text Mining

Illustrative bibliography:

  • Introduction to Data Mining Using SAS Enterprise Miner, Patricia Cerrito, SAS Institute, 2006.
  • Data Preparation for Analytics Using SAS, Gerhard Svolba, SAS Institute, 2006.

See indicative compulsory and optional modules for this course More Less

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