This programme benefits from Surrey Business Schools excellent industry connections, equipping you with the expertise to analyse data and create the knowledge that leads to competitive advantages for business decisions.
At Surrey Business School, we take pride in the quality of our research and teaching. Our excellent standards are reflected in the findings of the last Research Assessment Exercise, our accreditations from the AACSB and the AMBA, and our close links with national and international businesses of all sizes and across all sectors.
Business analytics skills are more vital than ever in large organisations. They play a crucial role in supply chain management, operations management and finance, as businesses strive to increase their efficiency and productivity in order to build a competitive advantage.
This programme equips you with these skills, giving you the ability to interpret, conceptualise and convert Big Data into useful information that improves organisations performance. These skills are more vital than ever in large organisations and, as such, employees with an understanding of business analytics are increasingly in-demand in the job market.
The programme centres on two main areas: the ability to analyse business data, and the skill to solve business challenges analytically. Through your elective choices you can further specialise in either the economic or managerial aspects of the programme. Business analytics students often pursue careers as consultants, researchers, managers, and analysts.
This module is the science of examining raw data in order to support businesses and organisations in their decision making. This module looks at the relationships of entities in databases using the Structured Query Language to extract relevant information efficiently and uses statistical techniques to extract the essential management information. It also introduces unstructured data concepts. Special focus is given to Big Data, providing the knowledge, analysis and practical skills to gain additional business and customer insights.
This module is designed to provide a practical study of the basic principles and advanced knowledge of financial accounting systems used around the world, and addresses the major issues to be reformed.
This module focuses on the supply chain management initiatives of large-scale retail and international businesses. Successful supply chain management is critical at both at an operational level and increasingly at a strategic level. An effective logistics infrastructure is essential to meeting customer expectations while minimising service costs.
This module builds on the statistical and econometric foundations, exploring a number of techniques for subsequent applied work, specifically concerning the estimation and inference of econometric models.
This module aims to familiarise students with conceptual and appropriate basic mathematical and statistical tools in economics, introducing simple linear regression techniques. This module has 20 hours of lectures, which are scheduled to take place in Week One.
Management Science is used to solve supply chain aspects analytically. Techniques examine the Supply Chains underlying transportation network which connects suppliers via transshipment nodes to its demand locations.
This module introduces the foundations of knowledge management, epistemology and semantics as sources to identify, capture, create, and distribute organisational knowledge. The module describes these strategies, along with the new roles and responsibilities for knowledge workers in the age of Big Data.
This module looks at the diverse models and frameworks used to evaluate and implement organisational change. The module seeks to identify the means and mechanisms that promote organisational flexibility and agility.
This module provides knowledge about the critical aspects of applied marketing research theory and practice to support and improve marketing decision making. Market research proposal and its planning aspects are discussed. Techniques of applied marketing research and typical approaches are also examined.
This module builds on the Econometrics I module. Asymptotically valid methods of estimation and hypothesis testing are introduced and we look at models involving several equations. Limited dependent variable and panel data models are also examined. Matrix algebra is used extensively to explore the properties of the models.
This module is an introduction to economic forecasting. The module covers the following topics: forecasting trends, ARMA models of the cycle, modelling seasonality, forecasting with macroeconomic models, assessing forecasts and smoothing methods.
This module examines the Big Data phenomenon. The module underscores the relationship between operations management on a day-to-day basis and its subsequent usage in modelling and analytics-driven managerial decision making. This module also provides hands-on experience with an enterprise software system (SAP).
The MSc Programme will require you to undertake an applied MSc thesis*. The module is designed to allow you to undertake the development of a modelling-based decision tool. Students will be required to:
This is a great opportunity to do add real value to a business, company or industry.
*In the exceptional case that the student cannot do an applied thesis, a conventional MSc thesis may be approved by the programme director.
You will get hands-on experience using a wide range of tools in the course. An indicative list of the software tools include: