The course has been specifically designed to give a structured understanding of business analytics and the practical tools which can drive business advantage. You’ll have the opportunity to learn how to analyse business data and solve business problems analytically. We start by providing an overview of big data and business analytics and its context in a business environment where data is rapidly becoming one of the most valuable assets. We will introduce you to the historical context and growth of big data, as well as the characteristics defining big data. We also encourage you to consider the social, ethical and legal implications of handling, storing and using all manner of datasets. We then focus on the underpinning analytical techniques and tools used to analyse and interpret data, forecast future trends, automate and streamline decisions and optimise courses of action. This should include statistical analysis, data mining, forecasting and regression, optimisation, simulation and spreadsheet modelling. You should also gain a deeper understanding of the value and benefits of different analytical techniques for advanced decision-making and how they can be used to identify patterns, relationships, associations, factors and clusters. Finally, we consider how to apply analytical methods and techniques to specific business functions, such as an organisation’s marketing function, value chain and financial planning. For example, we will consider how to assess financial performance and economic conditions of a business, identifying relevant costs for short-term and long-term decision-making.
This course will be assessed using a variety of methods which will vary depending upon the module. Assessment methods include the Postgraduate Business Project, reports, presentations, formal examinations and practical coursework. The Coventry University Group assessment strategy ensures that our courses are fairly assessed and allows us to monitor student progression towards the achieving the intended learning outcomes. Assessments may include exams, individual assignments or group work elements.
A second class honours (minimum 2:2 or higher) in either; business, finance, economics, management science, logistics, engineering, computing or mathematics. Applicants from other degrees may be considered on a case-by-case basis provided they can show strong quantitative ability. Applications from candidates who can demonstrate considerable experience at an appropriate professional level but who do not have formal academic entry qualifications may also be admitted on an individual basis
Fees and funding
No fee information has been provided for this course