Data Science with Sandwich placement

This module aims to integrate the subject material studied in other level 4 and 5 modules and give students experience of developing a software or hardware artefact in a team. Students will learn to work in teams to coordinate and manage a project.

This module looks at different data processing and mining techniques and state-of-the-art data processing and mining tools. The topics include: introduction to data mining and knowledge discovery process, data collection and processing with various data types from variety of sources, social web mining. You will look at a variety of techniques and trends in data mining.

This module will provide you with a firm grounding in the principles of Professional Databases. The topics you will study include why databases are important, how to design and manipulate a DBMS. This will involve you looking at database design techniques; different models of data and gain a firm understanding of the challenges of working with a multi-user database user environment. The module will give you practical experience of using SQL within a modern relational database management system, such as Oracle.

Operational research (or Decision/Management mathematics) is one of the most useful branches of mathematics, applying various mathematical methods (modelling, statistical techniques and algorithms) to help make better decisions in order to improve or optimise some business objective function such as profit, cost or time. This module will introduce students to some of the most fundamental operational research techniques such as linear programming and network analysis which are used to solve many problems, including those related to logistics, manufacturing and transportation. You will learn how to apply the relevant techniques to solve well defined problems by hand, and also how to use software to help solve more complex problems. & nbsp;

This course extends the probability and statistics studied in year 1. It will introduce you to the theory and practice of Linear Statistical Modelling and Survey Design. Linear statistical modelling statistical modelling where a response variable depends on one or more explanatory variables. Examples include, how well a patient responds to treatment, given their age and disease severity, or how different strains of corn compare when grown in various conditions. Survey design is a critical topic when determining the quality of survey based research. The potential for poor survey design is enormous whether intentional or not. Examples of poor survey design can be found in many real world spheres including political polling and readership surveys.

This module builds on High Performance Computing and Database modules to provide understanding and knowledge of the essential concepts, theories and practices of Big Data. This will include Distributed System, latest advances and techniques in processing and managing big data.

Financial mathematics is a vast topic, and here you will be introduced to applying probaility, statistics and mathematics to finance related problems. Taking uncertainty into account in your calculations is one of the key aspects that we will focus on in particular studying some of the mathematical and statistical techniques used by life and general insurance companies. Among the topics that may be included are valuations of financial and insurance products, different ways of dealing with uncertainty and risk, premium setting, mortality, morbidity and hazard rate analysis. In addition there will be analysis of both financial and non financial data using a variety of time series analysis techniques such as using Box Jenkins and Holt Winters methods in forecasting. There is also an opportunity to undertake data analysis to underpin data informed decision making; the data will typically be from subjects that either directly or indirectly impact the finanical sector such as investment prices, economic forecasts and political trends. This is a very practical module and the emphasis is on the work you will do when preparing your report(s) and presentation(s). You will be collecting and analysing data, writing technical reports and presenting your findings to a wider audience, with plenty of opportunities for formative feedback from module tutors to help you maxiimise your potential.

This module will further develop your knowledge and understanding of Concurrent Systems. It will cover advanced topics drawn from the latest developments in High Performance Computing such as GPUs, FPGAs and computer clusters. Research will include investigation of the current state of these technologies and the development, implementation and performance testing of programs that use them to maximise performance.

Multivariate statistics is a branch of statistics involving the observation and analysis of more than one outcome variable at the same time. There are various forms of multivariate analysis and in this module you will be learn about several. Cybermetrics aims to measure aspects of the World Wide Web to gain information about hyperlinks between pages. It encompasses many techniques including network visualisation and content analysis. In this module you will learn about these and more.

In this module you will start by looking at the Professional issues related to working in the Computing and IT industry. You will consider professional conduct and the social, legal and ethical implications related to the profession – in-line with BCS guidelines. In the first semester you will also start to consider the subject of your final year project. Following discussions with staff members you will produce a project proposal document and start a literature review on your chosen topic. You will be assigned a project supervisor who will meet with you at regular intervals to provide you with feedback on your work as it progresses. In your second semester you will continue to work individually on the main part of your final year project with regular meetings with your supervisor. This supervisor plus another member of staff will assess your project.