Program Overview

Data Science and Analytics MSc

Overview

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 stretches the artificial intelligence (AI), machine learning (ML) and decision science themes to business intelligence, data science and business analytics.

Course Structure

MSc students take five core modules, including a project and two from the option modules.

Core Modules

• Business Analytics: This is a self-contained module in applied statistics and operational research (OR) for decision making, which lays the foundations for more advanced modules in data mining, optimisation and simulation modelling.
• Data Mining and Machine Learning: This module will provide an overview of modern techniques in machine learning and data mining that are particularly customised for data science applications.
• MSc Project: The module consolidates and extends the knowledge students acquired in the taught part course, encourages and rewards individual inventiveness and application of effort.
• Data Warehousing and Business Intelligence: 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.
• Data Visualisation and Dashboarding: This module covers the theoretical and practical aspects of data visualisation including graphical perception, dynamic dashboard visualisations, and static data ‘infographics’.

Option Modules

• Web and Social Media Analytics: This module explores the use of modelling to analyse and measure both online presence and impact using web and social media data.
• Simulation Modelling: 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.
• Data Repositories Principles and Tools: A hands-on module with highly technical content, it covers the underlying technologies and approaches used in capturing, maintaining and modelling persistent data.
• Big Data Theory and Practice: The module discusses how to manage the volume, velocity and variety of Big Data, SQL and noSQL databases, and it touches on issues related to data governance and data quality.

Professional Accreditation

This course has been accredited by BCS, the Chartered Institute for IT, for the purposes of partially meeting the further learning academic requirement for registration as a Chartered IT Professional.

Entry Requirements

• A minimum of a lower second class honours degree (2:2) 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 your first language is not English, you should have an IELTS 6.5 with at least 6.5 in writing and no element below 6.0.

Fees and Funding

• UK tuition fee: £10,700 (Price per academic year)
• International tuition fee: £17,500 (Price per academic year)

Teaching and Assessment

• Teaching methods include lectures, seminars, workshops, problem-based and blended learning, and where appropriate practical application.
• Assessments include practical and coursework, such as presentations, podcasts, blogs, essays, in-class tests, portfolios, and dissertation.

Research Groups

• Centre for Parallel Computing
• Health and Social Care Modelling Research Group

Location

• Cavendish Campus, central London

Related Courses

• Big Data Technologies MSc
• FinTech with Business Analytics MSc
• AI, Data and Communication MA