Big Data Analytics - MSc

As technological advances accelerate development and revolutionise the shape of our future, businesses and individuals compete ever so vigorously to maximise their efficiency. The competition involves cutting costs and making data-informed decisions. Also, the nature of data itself is evolving with the arrival of new technologies such as the Internet of Things (IoT). In fact, at its extreme, even the entire working or living environment can be treated as data: natural evolution is a well-established scientific theory that supports such a notion. 

Information from data is required by many organisations and this module focuses on the application of statistical techniques to data sets primary using statistical and data analytics software. Hence a mixed learning and teaching approach is proposed that consists of both computer lab work with applying theory in practice through the use of specialist software and interactive taught sessions in a seminar room where you can work together putting theory into organisational context. 

Within this module, you will gain knowledge and experience of advanced concepts of database systems and implementation techniques of database management systems. The module begins with reviewing the fundamental concepts necessary for designing, using, and implementing relational database systems. This includes conceptual design, relational modelling, Structured Query language (SQL) programming and database implementation techniques. The module then explores some of the advanced concepts of databases relevant to the role of database administrator. This includes query planning and optimization; transaction processing and concurrency control; and big data technologies. The objective is to enhance the comprehension and the advanced use of database system in order to optimize its performance. In addition, the module contains a substantial practical element utilising advanced SQL, NoSQL, enabling students to gain transferable skills in designing and developing relatively complex ‘real life’ database applications.

This module focuses on aspects of managing big data systems with respect to the five V’s (Volume, Velocity, Variety, Veracity, and Valence); i.e. systems that provide operational capabilities for realtime, interactive workloads where data is primarily captured and stored to support any analytical capabilities.

Big Data has driven the creation of new technology architectures with the likes of NoSQL, MPP databases and Hadoop that enable new types of products and services. Operational systems, such as the NoSQL databases, focus on servicing highly concurrent requests while exhibiting low latency for responses operating on highly selective access criteria. 

Data mining is the non-trivial process of finding patterns and building models from data stored in data repositories such as databases and data warehouses. At the heart of Big Data Analytics and business intelligence, data mining algorithms provide readily available solutions to many Big Data problems. Data mining is an established field that provides both predictive and descriptive analytics solutions. Such solutions are often generic and can be applied to a wide range of applications from business to scientific and governmental applications.

In this module, you will be taught the internal mechanisms of developing descriptive and predictive data mining methods. Also you will be taught how to use modern data mining tools to build and numerically validate models and patterns extracted from data. You also will be able to critically evaluate current trends in data mining.

The exponential growth of social media has transformed the social, political, and technological landscapes. An increasing amount of data is generated from today’s social sites such as Twitter, Facebook, and YouTube. People use social media to publish rich content, annotate it with descriptive metadata, communicate and respond to each other. Data analytics is a powerful tool to identify trends and patterns in social media and explore how social media have been used in times of disasters, crisis or during important events such as political campaigns. This course is multidiscipline that combines social network analysis (SNA), natural language processing, and data analytics for mining social data. We aim in this course to understand an end-to-end process of social media analytics starting form data collection to extracting insights and deriving conclusions.

The purpose of the module is to enable you to undertake a sustained, in-depth and research-informed Level 7 project exploring an area that is of personal interest to you. In agreement with your supervisor, you will decide upon your topic which will take the form of a practical outcome (artefact) with accompanying contextual material. The main consideration when choosing your topic is that it must be aligned to the programme you are studying and informed by the research strategy of your school, and you should consider the relevance of this topic to your future academic or professional development.