The MSc in Business Intelligence and Analytics is validated by the University of Westminster (UK) in November 2019. This course is one of most popular postgraduate courses in contemporary international higher education market and takes you to the world of modern data science that addresses the business need to advance data organisation and information gathering, go insight into information and exploit knowledge hidden in routinely collected data in order to improve decision-making. The course is more technology focused, and stretches the data-mining and decision-sciences theme to the broader agenda of business intelligence.
The course provides a balanced study, which aims at producing graduates that are capable of:
- Thinking in a systematic and methodological way about Business Intelligence & Analytics issues;
- Utilising their problem-solving skills and their knowledge of various techniques, tools and methods, to deliver Business Intelligence & Analytics solutions to a wide range of problems;
- Creating models and deploying appropriate software tools that satisfy specified requirements, and testing their use in a target domain;
- studying the context within which the design of Business Intelligence & Analytics takes place, i.e. as part of the range of strategic, managerial and operational activities involved in the gathering, processing, storage and distribution of information;
- Identifying the security and legal implications of Business Intelligence & Analytics applications, e.g. Customer Relations Management (CRM);
- Independent in-depth analysis of a chosen topic making use of information resources outside a teaching environment.
- An undergraduate degree from a recognised institution with a minimum of second lower class honors (2:2 or equivalent);
- For those applicants whose first degree was not taught and assessed in English, an IELTS score of at least 6.5 (including 6.0 in writing) or equivalent;
- To have at least 12 months of full-time paid work experience or equivalent on the date of enrollment;
- To be at least 22 years old on the date of enrollment.
The application process consists of submission of an application form, and, if the minimum criteria are met, applicants will be formally interviewed by a panel of the course team.
This is a self–contained module in applied statistics and operational research (OR) for decision making that lays the foundations for more advanced modules in data mining, optimisation and simulation modelling. It covers the essential of descriptive, predictive, and prescriptive analytics in an application driven manner and makes use of appropriate software tools such as EXCEL (including add-ins) and R to derive meaningful solutions.
An introductory module that covers theoretical & practical issues related to technologies employed in persistent storage of data. It evaluates underlying technologies & approaches used in capturing, maintaining & modelling persistent data; reviews the evolution of DBMSs their components & functionality, along with some of the predominant & emerging data models; addresses practical issues related to conceptual data modelling, practical & current trends in database design. It also discusses in detail the features and constructs of the SQL, the de-facto database language for the definition and manipulation of relational data constructs, current technological trends including the advances that Big Data Era has brought.
The module strengthens students’ skills for the research and industry needs in the area of their studies, their final project, and their professional development. It guides the students’ personal development plan towards the professional requirements of the discipline, and covers methods of critical evaluation, gathering and analysing information, and preparing and planning a project proposal. The module serves as a vehicle for the development of a well-researched and planned proposal for an MSc level project that students will implement within the course.
The project module consolidates and extends the knowledge students acquired in the taught part course, encourages and rewards individual inventiveness and application of effort. Students need to carry out and bring to fruition a comprehensive piece of individual work that involves activities (related to a single theme relevant to their studies) that include research, planning, critical evaluation and reflection. The scope of the project is not only to complete a well-defined piece of work in a professional manner within the time the module runs, but also to place the work into the context of the current state of the art of the subject area.
This module will provide an overview of modern techniques in Machine Learning and Data Mining that are particularly customised for Data Science applications. Students will be introduced to a range of toolkits, such as R and Python and they will explore the features and strengths of different machine learning and data mining methodologies using selected data sets related to specific public sector or businesses application domains.
Business Intelligence, Data Mining and Analytics are a set of methods and technologies that transform raw data into meaningful and useful information. A Data Warehouse is the architecture or structure that supports these activities. This module teaches students how to build Data Warehouses by understanding their structures and the concept of multi-dimensional modelling. The focus is on Data Warehouse design, multi-dimensional modelling, the integration of multi-source data and analysis, cloud-based data warehousing, NOSQL OLAP, aiming to support better business decision making.
The module focuses on the choice and use of appropriate simulation modelling approaches to treat real–world problems, developing solution(s) using powerful simulation software and explaining the business and industrial implications thereof. Relevant applications to problems such as stock control, reliability, project management, and service redesign will be considered in domains such as healthcare, supply-chain, and transport.
The module focuses on assisting students in recognising challenges in digitalisation and digital transformation of contemporary businesses, assessing different information needs of organisations, discovering issues related to the deployment of complex information systems and e-business models, and appreciating the increasing importance of digital technology and management of information and knowledge in global businesses.