Data Science and Analytics MSc

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. 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.

This module will provide an overview of modern techniques in machine learning and data mining that are particularly customised for data science applications. You’ll be introduced to a range of toolkits, such as R and Python, and 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.

The module consolidates and extends the knowledge students acquired in the taught part course, encourages and rewards individual inventiveness and application of effort. You’ll be required to carry out and bring to fruition a comprehensive piece of individual work on an approved topic (relevant to your course of studies) that involves research, planning, critical evaluation, reflection activities. To provide appropriate foundational knowledge to support the project development, the module includes a series of four blended learning workshops.

This module covers the theoretical and practical aspects of data visualisation including graphical perception, dynamic dashboard visualisations, and static data ‘infographics’. Tools such as R and Tableau are used. The aim is to prepare students for becoming a data visualisation specialist.

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.

This module explores the use of modelling to analyse and measure both online presence and impact using web and social media data. You will learn how to listen to social media conversations taking place, how such data can be transformed into actionable insight for a brand or organisation. You will also study ways in which the effectiveness of modern websites is often judged and how online web metrics can be used to drive performance. The overriding aim of the module is to equip students with the necessary technical skills and industrial knowledge for a career in the area of web or social media marketing.

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.

A hands-on module with highly technical content, it covers the underlying technologies and approaches used in capturing, maintaining and modelling persistent data; it addresses practical issues related to data modelling and database design; finally, it provides practical/hands on skills by introducing the features and constructs of SQL.

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.