Applicants should normally hold an Undergraduate degree (or equivalent) from a recognised higher education institution with a minimum of a second lower class honours (2:2 or equivalent). Applicants without a formal HE qualification or the formal qualification is not at the equivalent academic standard, may be considered if the following conditions apply.

• They are or have been in employment where their employed role is in the area of the course and involves a high level of analysis and critical thinking. If so such candidates will be required to provide evidence of such employment, its nature and level. This evidence will be considered at Interview and the decision of the panel (see below) will be final.

Applicants must have:

• Had their first or second degree(or equivalent) taught and assessed in English; or

• An IELSTS score of 6.5 overall with a minimum 6.0 in writing component, or another English Language test recognized by the University of Westminster
  

Applicants must have at least 12 months of full-time paid work experience or equivalent at the start of the academic year. For the purposes of this regulation the academic year runs from 1st October to 30th September.

Applicants will have to be 22 years of age at the start of the academic year. For the purposes of this regulation the academic year runs from 1st October to 30th September.

Applicants who have met the minimum entry requirements will be formally interviewed. The purpose of the interview is to determine the suitability of the candidate to undertake and complete the course. The panel will also consider any equivalent or non-standard admission qualifications. In so doing the Panel may require additional evidence from the candidate to support the candidates request for consideration for equivalency, such as the nature and level of the work experience.

The module discusses how to manage the volume, velocity and variety of Big Data, SQL and non-SQL databases, and it touches on issues related to data governance and data quality. The module will also introduce students to data science tools and technologies such as Python, Matlab, Apache Spark, JavaScript Amazon AWS Machine Learning and MS Azure Machine Learning as well as to private and sensitive data management approaches and open data framework.

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.