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Business Intelligence and Analytics (Full Time) - MSc


Course Description

This course addresses the need to propel information-gathering and data organisation, and exploit potential information and knowledge hidden in routinely collected data to improve decision-making. The course, which builds on the strength of two successful courses on data mining and on decision sciences, is more technology focused, and stretches the data mining and decision sciences theme to the broader agenda of business intelligence.

You will focus on developing solutions to real-world problems associated with the changing nature of IT infrastructure and increasing volumes of data, through the use of applications and case studies, while gaining a deep appreciation of the underlying models and techniques. You will also gain a greater understanding of the impact technological advances have on nature and practices adopted within the business intelligence and analytics practices, and know how to adapt to these changes.

Course content

Embedded into the course are two key themes. The first will help you to develop your skills in the use and application of various technologies, architectures, techniques, tools and methods. These include warehousing and data mining, distributed data management, and the technologies, architectures, and appropriate middleware and infrastructures supporting application layers. The second theme will enhance your knowledge of algorithms and the quantitative techniques suitable for analysing and mining data and developing decision models in a broad range of application areas. The project consolidates the taught subjects covered, while giving you the opportunity to pursue in-depth study in your chosen area.

Teaching approaches include lectures, tutorials, seminars and practical sessions. You will also learn through extensive course work, class presentations, group research work, and the use of a range of industry standard software such as R, Python, Simul8, Palisade Decision Tools, Hadoop and Oracle.

Taught modules may be assessed entirely through course work, or may include a two-hour exam at the end of the year.

Modules

The following modules are indicative of what you will study on this course.

Core modules
-BIG DATA THEORY AND PRACTICE
-BUSINESS ANALYTICS
-DATA MINING AND MACHINE LEARNING
-RESEARCH METHODS AND PROFESSIONAL PRACTICE
-BUSINESS SYSTEMS POSTGRADUATE PROJECT

Option modules
-ADVANCED BIG DATA ANALYTICS
-BUSINESS OPTIMISATION
-DATA VISUALISATION AND DASHBOARDING
-DATA WAREHOUSING AND OLAP
-DATA REPOSITORIES PRINCIPLES AND TOOLS
-SIMULATION MODELLING: RISK, PROCESSES, AND SYSTEMS
-WEB AND SOCIAL MEDIA ANALYTICS

Associated careers

Graduates can expect to find employment as consultants, decision modelling or advanced data analyst, and members of technical and analytics teams supporting management decision making in diverse organisations. Typical employers include local authorities, PLCs (such as GlaxoSmithKline, Prudential, Santander and Unilever), public sector organisations (such as the NHS and primarily care trusts), retail head offices, the BBC, the Civil Service and the host of banks, brokers and regulators that makeup the city, along with all the specialist support consultancies in IT and market research and forecasting, all of the whom us data for the full range of decision making.

Professional recognition

This course is accredited by the British Computer society for partial fulfilment of the academic requirement for Chartered IT professional.

Visit the Business Intelligence and Analytics (Full Time) - MSc page on the University of Westminster website for more details!

Entry Requirements

You will have a suitable Honours degree from a UK university (or equivalent qualification) in a scientific or engineering discipline with some exposure to the use of IT, or in an area of computer science or IT with a strong interest in quantitative analysis. If you do not have a formal qualification, but you are already in employment, you may be considered if your role involves the data mining and decision support techniques and technologies deployed in the course.

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Recipient: University of Westminster

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