Programming is an engineering tool that plays a vital role to drive most of the modern technologies surrounding us, including the technological devices for communication, transportation and entertainment. In other words it can be said that our modern lifestyles are heavily dependent on programming.

Moreover, businesses increasingly rely on computers and the software run on them. Programming skills and a broader and deeper understanding of programming are therefore becoming increasingly important to the jobs market.

Data applicable to organisational bodies for decisions making process are increasing rapidly. Automation of tracking, the increase of new data types (e.g., social media, audio, and video), improved storage of electronic records, re-use of administrative records, and the outburst of modelling data have all amplified the availability of data. However, making full use of these data requires not only right storage and but also advanced analytical capabilities.

Data Science is a multidisciplinary field that deals with technologies, processes, and systems to extract knowledge and insight from data and supports reasoning and decision making under various sources of uncertainty. The goal of Introduction to Data Science is to teach students how to answer questions with data.

The module will enable students to conduct computational investigations needed for vital decisions and prediction of new trends and at the same time enhance students’ employability skills.

The vast majority of web applications are connected to databases for the purpose of storing and retrieving information. On this module you will be introduced to relevant concepts and technologies, applying them to the development of such applications. You will gain knowledge and skills concerning data modelling (entity-relationship models) and relational database implementation and administration (using SQL).

You will also acquire new programming skills (e.g., writing server-side scripts in PHP) in order to produce applications that provide useful functionality. You will also gain practical experience in the use of web frameworks to develop web applications to enhance your employability skills.

Computer Scientists need to address the realities of the application of their field within an environment where cyber security threats present unique challenges to application and system developers, in relation to the requirement for secure design and operation. This module provides a foundation for security consideration as required in the design of software expected to perform within a networked and data sharing environment. This module has been designed to provide the necessary theoretical framework, foundations and practical support for effectively pursuing security solutions with reference to the requirement for secure application development.

The aim of this module is to identify, apply and design data visualisations. The module provides you with the fundamental principles and practice-based activities needed to design data visualisations for different contexts and different types of data. More advanced visualisation concepts and tools for analysing multi-dimensional data and large data sets will also be examined and appraised.

You will learn how to employ visualisation as a tool that can help users understand large and/or complex data sets. Finally, upon completing the module, you should be able to decide on the best visualisation for the data and research question at hand, and then implement in the best way possible.

This module is about software engineering with a focus on software design. It covers three stages of the software engineering life-cycle, requirements, design and implementation, but with a focal point on design and an emphasis on the design theme.

The module provides the necessary skills to:

1. construct models of requirements and designs,
2. synthesise implementations from design models,
3. apply software design patterns.

The standard UML is used as the modelling language, the vehicle through which important design concepts are explored to convey a software engineering ethos based on getting it right by adequately studying the problem and mindfully constructing designs of software solutions.

This module will introduce NoSQL databases and distributed data storage frameworks through practice based activities. Through guided hands-on tutorials, you will become familiar with techniques using non-structured and semi-structured data examples. Also, you will gain knowledge on managing and processing data in a distributed infrastructure. This course will improve your development skills and provide experience with many different data systems.

Deep  learning (also  called  deep  neural  network) is  a  sub-field  of  Artificial  Intelligence(AI),  aiming  at extracting knowledge automatically from large data sets in a hierarchical way.

Deep learning, which is considered as the state-of-the-art in data science and AI, is inspired by the structure of human brain. Now well established and accepted in industry, deep learning provides both predictive and descriptive analytics solutions that can be applied to a wide range of applications from business to scientific and governmental  applications.

Artificial Intelligence (AI) is a core component of computer science, aiming at developing intelligent agents that mimic human’s cognitive capability in learning, reasoning, and problem solving.

As a branch of AI, machine learning (ML) allows to create software that adapts and learns (from examples), rather than being explicitly programmed for a particular outcome. Both AI and ML rely on managing, processing and analysis of large datasets, something that Data Science (DS) is concerned with. Many services provided by technology giants such as Google, Microsoft, IBM, Facebook, Amazon, etc. are powered by DS, ML and AI. The recent advances in these subjects have already led to significant industrial applications such as self-driving cars and Industry 4.0.

The purpose of the module is to enable you to undertake a sustained, in-depth and research-informed 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 you should consider the relevance of this topic to your future academic or professional development.

At this level, you will be expected to work independently but you will receive additional one-to-one support from your supervisor, who will be familiar with your chosen topic area. As you progress on the module, extra support will be available and this may take the form of group seminars, workshops and online materials that will help to develop your project.