This programme is enhanced by Surrey Business School’s excellent industry connections, equipping you with the expertise to analyse data and produce the in-depth information that leads to long-term competitive advantages for businesses in a range of sectors.
At Surrey Business School, we offer a diverse range of business and management programmes that are fuelled by a common approach: encouraging you to be entrepreneurial and innovative, providing opportunities for you to engage with real-world business problems throughout your programme.
Big Data drives big decisions and, as such, 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.
Our MSc in Business Analytics equips you with these skills, giving you the ability to interpret, conceptualise and convert Big Data into useful information – thus allowing you to analyse, model, optimise and improve organisational performance.
Because of this, graduates 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 of solving business challenges analytically. Through your optional choices, you can further specialise in either the economic or managerial aspects of the programme.
As part of the programme, you will also benefit from hands-on experience of a wide range of software tools such as simulators and mathematical tools (see below for more details).
The programme involves five compulsory modules, three elective modules, one compulsory supporting module and a dissertation.
- Data Analytics
- Supply Chain and Logistics Management
- Supply Chain Analytics
Compulsory supporting module:
- Quantitative Methods Induction Course
- Business Process Management
- Informatics for Decision Making
- Introduction to Marketing Analytics
- Investment Analysis
- Econometrics II
- Foundations of Finance
- Managing Decisions Implementation
- Principles of Accounting
Example module content
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.
Principles of Accounting:
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.
Supply Chain and Logistics Management:
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.
Quantitative Methods Induction Course:
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.
Supply Chain Analytics:
Management Science is used to solve supply chain aspects analytically. Techniques examine the Supply Chain’s underlying transportation network which connects suppliers via transshipment nodes to its demand locations.
Informatics for Decision Making:
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.
Managing Decision Implementation:
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 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.
The module studies the various stages of the investment analysis and management process from the award of an investment sponsor’s mandate through investment manager selection to the portfolio and performance outcome of that selection. Consideration is made at each stage of who makes the decisions and what those decisions are based upon. This includes asset allocation and comprises equity investment, active versus passive investment, drivers of value, security selection, investment style and investment performance. The assignment involves a real asset allocation problem. This module is particularly useful for students considering a career in finance; investment management, investment banking, investment consultancy or asset management but is also useful for those involved in other areas of the financial sector such as insurance and pensions; the main users of investment management services.
Business Process Management:
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).
Foundations of Finance:
The Foundations of Finance module provides the theoretical underpinnings of all of our MSc Finance and Accounting programmes. It introduces the pivotal concepts which form the basis of theoretical finance under three broad headings; Portfolio Theory and Practice, Equilibrium in Capital Markets and Introductory Analysis of Asset Classes. Core concepts include the relationship between risk and return, the Capital Asset Pricing Model (CAPM) and the Efficient Market Hypothesis (EMH) but the module also extends this analysis into new theoretical areas such as Behavioural Finance.
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:
- Identify and evaluate relevant measures and variables, as a source of decision-making insight.
- Combine identified data or variables in a model that can be used as a tool for manager’s decision-making.
- Use available data with a developed model to identify viable solutions and propose options and scenarios for organisational change and innovation that improves organisational performance.
- Analyse a business relevant issue and develop recommendations and logical conclusions.
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.
Business analytics students often pursue careers as consultants, researchers, managers, and analysts.
You will get hands-on experience using a wide range of tools in the course. An indicative list of the software tools is as follows:
- Excel (using the Solver and Data Analysis Add-Ins) and Tableau for decision making and visual analytics
- COGNOS and SQL Server for Business Intelligence for analytical processing
- Apache Hadoop (Map Reduce) with Amazon’s Elastic Cloud or IBM’s Smart Cloud for distributed Big Data analytics
- SAP for Enterprise Resource Planning
- R, SPSS and EViews for coding, statistics and forecasting
- ILOG’s Optimisation Studio (Cplex) for optimisations
- Matlab for algorithms and programming and Simulink (SimEvents) for simulations
- Arena (or Simul8) for Discrete Event Simulations
WHO WE WORK WITH
This programme is run in cooperation with IBM.
Surrey Business School is accredited by the Association to Advance Collegiate Schools of Business (AACSB) (http://www.aacsb.edu/
) and by the Association of MBAs (AMBA) (http://www.mbaworld.com/
Our Admissions Policy (http://www.surrey.ac.uk/apply/policies/postgraduate-admissions-policy
) provides the basis for admissions practice across the University and gives a framework for how we encourage, consider applications and admit students.
Further information for applicants - http://www.surrey.ac.uk/apply/postgraduate
Applicants should normally hold a Bachelors degree (UK 2.1 or above) or equivalent qualification from a recognised British or overseas university in a related subject with significant exposure of mathematical subjects: (economics, finance, mathematics, computer science or engineering subjects)If an applicant’s Bachelors degree is not in a subject related to the MSc, some relevant work experience would be an advantage. Higher level professional qualifications may also be accepted. Each applicant is assessed on their own merit.