Further information about programme courses, including links to the full course outlines.
Courses are listed in alphabetical order below for the 2020/2021 Academic Year. Click on the course codes below for full course outlines in the University’s Degree Programme Table.
NOTE: Courses are reviewed yearly and may not run in each Academic Year. The University's Degree Programme Table (DPT) is updated each Academic Year for the programme. Links for the 2020/2021 DPTs for each programme are available at the bottom of this page.
Engaging with Digital Research (PGSP11401)
The course will enable students to understand new emerging models of professional practice in business and policy making developing and deploying digital research methods and results. This will include collecting, curating, exchanging and analyzing of digitally-derived data, the use of research from digital environments, and the way this is leveraged turning this data into tools for active use and behaviour change. This course will also address the development of transferable insights in managing cross-institutional and citizen collaboration in digital data collection and analysis.
Informatics Project Proposal (INFD11014)
The aim of this distance course is to develop generic research and/or practical skills that can be deployed in academic or commercial environments. Students will demonstrate their ability to develop interesting concepts and/or hypotheses into proposals appropriate for a larger research- or implementation-based project and demonstrate their ability to identify legal, social, ethical and professional issues.
Introduction to Practical Programming with Objects (INFD11001)
This module is intended for students who have some previous programming experience, but would like to develop their ability to write complete, practical applications. Students with no programming experience should be able to complete the course, although this will almost certainly be challenging and will require additional time. The course uses an object-oriented approach, based around the Java language, but no previous experience of specific languages or technologies is assumed.
Introduction to Vision and Robotics (INFD11006)
This course applies AI techniques to the problems of making devices capable of interacting with the physical world. This includes moving around in the world (mobile robotics), moving things in the world (manipulation robotics), acquiring information by direct sensing of the world (e.g. machine vision) and, importantly, closing the loop by using sensing to control movement. Applying AI in this context poses certain problems, and sets certain limitations, which have important effects on the general software and hardware architectures. This course introduces the basic concepts and methods in these areas, and serves as an introduction to the more advanced robotics and vision modules.
Introductory Applied Machine Learning (INFD11005)
This course is about the principled application of machine learning techniques to extracting information from data. The main area that will be discussed is supervised learning, which is concerned with learning to predict an output, given inputs. A second area of study is unsupervised learning, where we wish to discover the structure in a set of patterns, i.e. there is no output "teacher signal". The primary aim is to provide the student with a set of practical tools that can be applied to solve real - world problems in machine learning, coupled with an appropriate, principled approach to formulating a solution.
Leading Technology and Innovation in Organisations (CMSE11495)
This online credit-bearing course is aimed at aspiring leaders and managers, who have technical backgrounds and roles in organisations. The course helps them lead and manage organisational changes by providing knowledge of the key concepts, frameworks, tools and techniques to implement changes and innovation in the work place.
Managing Digital Influence (PGSP11391)
One of the most impactful effects of easier access to a larger proportion of data on an increasing number of phenomena is the use of rankings to assess all aspects of the performance of products and organizations based on customer feedback. The course also offers a tutorial on Using Gephi as a tool for Measuring online Influence.
Medical Informatics (MCLM11037)
This course provides an introduction to data science in medicine, and more particularly to representing and interpreting data from areas across biomedicine and healthcare. It covers relational databases for medicine and healthcare, medical ontologies, statistical analysis of biomedical data, as well as some advanced topics in medical informatics, such as healthcare workflows and precision medicine.
Message Passing Programming (INFD11011)
In the message-passing model the tasks are separate processes that communicate by explicitly sending each other messages. All parallel operations are performed via calls to some message-passing interface that is entirely responsible for interfacing with the physical communication network. This course uses the de facto standard for message passing, the Message Passing Interface (MPI), which is a library callable from C, C++ or Fortran. Parallel programs written using MPI can run on almost any system from a multicore laptop up to the world's largest supercomputers.
Natural Computing (INFD11007)
This module teaches you about bio-inspired algorithms for optimisation and search problems. The algorithms are based on simulated evolution (including Genetic algorithms and Genetic programming), particle swarm optimisation, ant colony optimisation as well as systems made of membranes or biochemical reactions among molecules.
Neuroimaging: Common Image Processing Techniques 1 (NEME11006)
This core course aims to introduce the student to the major forms of image analysis commonly used in neuroimaging research. This ranges from the 'bread and butter' techniques like qualitative and quantitative assessment of whole brain and subregional brain volumes, simple and complex ways of measuring lesion size, region of interest measurements to assess tissue parameters as might be derived from diffusion-weighted or perfusion images, and simple tractography techniques.
Neuroimaging: Common Image Processing Techniques 2 (NEME11037)
This core course covers generally applicable image processing techniques in Neuroimaging, including DWI basic quantification, Tractography, Retinal image analysis, Registration techniques, Voxel based analysis, and Image segmentation.
Neuroimaging: Image Analysis (NEME11035)
This course is for students with a specific interest in Image Analysis, including those from a more computing background. Students will be able to focus in great detail on computing basics, sampling and quantisation as well as visual effects and their influence on perception; mathematical transformations and modelling as well as validation of techniques will also be taught so that on exit, students will independently be able to assess datasets from imaging experiments for quality, for best analysis approach including selecting the most appropriate analysis tools and algorithms, for sensibility and logic of output, and for usefulness and appropriateness to the original research goals.
Practical Image Analysis 1 (NEME11054)
This practical, medical image analysis & processing course introduces MATLAB, an industry-standard operational platform for computational image analysis. Students will also work with related software, which interacts with MATLAB, & become familiar with 2D & 3D image operations, various medical image file formats, image enhancement, image alignment, & registration. Armed with this knowledge, students will tackle different medical image processing & analysis tasks, organised by topic & increasing difficulty as the course p0rogresses.
Practical Image Analysis 2 (NEME11055)
This practical, medical image analysis & processing course explores advanced MATLAB use & application. The course will help students assimilate & consolidate prior knowledge relating to image processing & analysis. Students will become familiar with 3D & 4D image operations, sophisticated image alignment & registration techniques, threshold-based image segmentation & classification, feature descriptors, machine learning applied to image segmentation & classification, 4D medical image analysis & processing, plus basic analyses of time series of volumetric image data.
Practical Introduction to Data Science (INFD11010)
This online course will provide a practical introduction to data science. It will have two broad themes, namely Data Management and Data Analytics. Data Science is an emerging field, which is becoming very important both in research, business and industry. The amount of data that is being generated and stored is greater than it has ever been, and this brings both challenges in terms of how you work with the data and - importantly - rewards in terms of new insight gained from analysing the data.
Practical Introduction to High Performance Computing (INFD11009)
The course will cover all the fundamental concepts that underpin modern HPC. The course is practical in the sense that you will explore these topics by running parallel programs on real HPC systems such as the UK national supercomputer ARCHER.
Probability and Statistics (MATH11204)
In this course you will learn the fundamentals of probability and statistics - the building blocks for all of data science. Covers both theory and practical aspects using R.
Programming Skills (INFD11016)
This course is designed to help you to produce higher quality code; code that is readable, maintainable, usable, correct and efficient in less time and with less effort. These programming skills are applicable to programs in any language and the course is illustrated with examples from C, Python, Fortran, and Java.
Software Development (INFD11017)
|Software development is more than just writing programs and this course provides an introduction to the complete range of software development activities, from gathering requirements through to evaluation of a finished product. The course describes how software development projects are created and managed to achieve the delivery of high-quality, efficient, robust, portable, usable software products. You will be introduced to areas of increasing prominence in both academia and industry including the importance of software sustainability and the rise of agile methods and extreme programming. The course should support in the understanding of the value of practical software development skills to the HPC, computational science and engineering.|
Technologies of Civic Participation (PGSP11390)
The focus of this course is on understanding the current and potential uses of new TCPs by citizens and policy-makers in responding to mundane, everyday threats to social resilience (e.g. street crime, problems with community service delivery, environment, health, etc.), and appreciating how these activities (e.g. monitoring, informing, reporting) are linked to everyday life in the community.
Threaded Programming (INFD11012)
This course is a practical introduction to parallel programming using the threading model, which is commonly used on shared memory and multicore hardware. The majority of the course is focused on teaching the use of the industry standard OpenMP API.
The Use and Evolution of Digital Data Analysis and Collection Tools (PGSP11388)
In this course we will address the opportunities and challenges of a range of traditional and emerging digital research approaches and techniques focusing on the relevance of their applications from a user perspective. It will cover ethical, practical, legal, methodological and economic issues in theory and practice.
Understanding Data Visualisation (PGSP11484)
In this course, we examine the visual aspects of data analytics and the emerging professional practices of turning numbers into pictures or, more specifically, into screen realities. Hosting contributions from key experts in the field, the course will provide students will skills to critically interpret the most popular data visualization techniques used by major information provider firms.
Students must also complete a project (worth 60 credits) in the form of a written dissertation. This activity will allow students to draw from the combined experience of previous activities and interactions, and apply what they have learnt to a practical research question. The final submission will be expected to be at a level appropriate for an independent researcher and be a good indication of a student’s potential to go on to be a productive researcher in a relevant sub-discipline of Data Science. (Available for students taking MSc programmes only.)