Applied Data Science and Cyber Security

Core Modules

Year 1

• Computing for Data Analysis

  The module will focus on programming problems and assignments designed to teach students algorithmic thinking and problem solving and covering programming concepts such as conditional statements, loops, and arrays. The students will use standard libraries to manipulate data and apply machine learning algorithms for regression, classification, and clustering and learn to interpret the results.

• Research Methods

  This module precedes the dissertation and is aimed at allowing you to develop a plan for your research, resulting in a detailed proposal. As such, work done on the module contributes towards the first three chapters of your Dissertation. In this module you will acquire an understanding of the research context. You will learn how to develop feasible research objectives and an appropriate conceptual/analytical framework for your research. You will learn how to identify and critically review appropriate literature, and how to make informed decisions about which research philosophies, strategies and methods are suitable for your research. The subjects of triangulation, reliability, validity and research ethics will be explored, with the aim that you learn how to select a combination of methods that form a critically robust research design, such that you can apply this in your dissertation module.

• Applications of Data Science

  The aim of this module is to practice the use of Data Science by working through a series of case studies. The case studies will be based on real- life problems and will start with a description of the setting of the problem and the intended outcomes. Analyses will start with raw data that will have to be sense-checked and manipulated into a form that is suitable for the intended analyses. Deciding on the exact form of the analyses in each case will be a central focus of this module and an important aim of this module will be developing the skills to make decisions in this regard, drawing on information from the setting, the exact nature of the problem being assessed and knowledge of the techniques and methods that are available. In each case study, the results of the chosen form of analyses will be interpreted, with particular attention given to the best way of communicating the results to a variety of technical and non-technical audiences. Activities will include problem formulation, knowledge discovery, the application of statistical and machine learning techniques, report writing and presentation. Assessment will be based on practical examples using real-world data examples.

• Cyber Security Foundations

  This module introduces the broad range of concepts, challenges and technologies that underpin the provision of cyber security. Students will gain an understanding of what cyber security is, why it is important, and of the principal techniques and technologies that are used to achieve cyber security. Gaining an understanding of certain key elements of cyber security is necessary to be able to properly appreciate individual aspects of the subject in greater detail. This module is intended to give students this broad understanding so that they can set the ideas and skills developed in other modules into a broader context.

• Cyber Security for Data Science

  Students completing this module will gain a deep appreciation of the many links between data science and cyber security and privacy, and in particular both how data science can support cyber security, and how cyber security and privacy requirements can influence the practice of data science. This will equip graduates with the skills necessary for a professional career working in applications of data science and/or the provision of security for large and complex data sets. There are particular synergies between the two topics, as explored in this module. Key topics to be explored include: Privacy Enhancing Technologies (PETS), intrusion detection systems, data analysis for security, ethical issues relating to data, privacy and security, and privacy impact assessments.

• Ethics in Advanced Computing and Artificial Intelligence

  This course is designed to enhance your awareness of the many ethical implications of working with advanced technology. The course recognises that the ethical issues in computing and AI come to the forefront through developments in technology, bringing new responsibility for novel ethical, social, and legal implications of technology almost on a daily basis.

• Individual Project in Applied Data Science and Cyber Security

  The individual project is by far the most important single piece of work in the MSc programme. It provides the opportunity for students to demonstrate independence and originality, to plan and organise a large project over a long period, and to put into practice some of the cyber security techniques they have been taught throughout the course. The content of each project is individual. The student selects preferred project topics from the provided list or proposes his or her own project topic. Each student is allocated a supervisor who is the main point of contact for the duration of project work. The project leads to the production of the dissertation.

• Academic Integrity

  This module will describe the key principles of academic integrity, focusing on university assignments. Plagiarism, collusion and commissioning will be described as activities that undermine academic integrity, and the possible consequences of engaging in such activities will be described. Activities, with feedback, will provide you with opportunities to reflect and develop your understanding of academic integrity principles.

   

All modules are core