Data Science, Technology and Innovation


Further information about programme courses, including links to the full course outlines.

Courses are listed in alphabetical order below for the 2024/25 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 2024/2025 DPTs for each programme will be made available at the bottom of this page. 

Applied research design for health and social care (HEIN11090

This course aims to provide students with an understanding of the principles of the research process, to equip students with the core skills needed to develop a dissertation-level research project, and to develop academic scientific writing skills essential to the development of a successful data science researcher.

Data Ethics in Health and Social Care (HEIN11059

This course will introduce students to the various ethical issues that arise regarding the use of service user data from health, social, and care service systems, emphasising data ethics and ownership. 

Data security and protection in health and social care (HEIN11047

The course aims to highlight the critical aspects of secure processing and use of data and how they relate to health, social and care services. The course will introduce students to the legislation regarding data security and provide essential training on collecting, processing and storing sensitive service user data, and good data security practice within health, social and care service organisations. 

Data, Sport and Society (STIS11002

The course is about being able to develop sport analytics as well as critically assessing its wider social and epistemological implications.

Earth Observation for Sustainable Development Goals (PGGE08001)

This course is an introduction to Earth observation for beginners and non-technical people (contains no mathematics or substantive quantitative analysis). The primary aim is to provide students with a well-grounded understanding of the important insights provided by satellite observations with an emphasis on how this data can be used to monitor or evaluate progress on sustainable development goals (SDGs).

EPCC Project Proposal (EPCD11014

This course comprises all aspects of the planning for the individual research project and is undertaken by the student in conjunction with the project supervisor(s). The student and supervisor(s) will agree the topic of the project, after which the student will conduct initial reading to refine the scope of the project and to inform the development of a detailed plan for its implementation. 

Foundations of research design for health and social care (HEIN11089

This course aims to provide students with an understanding of the principles of research design, to equip students with the core skills to approach research according to best research practice and to develop academic scientific writing skills essential to the development of a successful data science researcher.

Health Data Science (HEIN11060

This course aims to equip healthcare professionals with the key foundations and data skills that are needed for data-driven innovation. It provides an introduction to key concepts, principles and methods of data science in health, enabling students to explore the potential for data to transform healthcare. Students will learn how to use current data science tools to process healthcare data for effective analysis and reporting, and gain practical experience in working with data. They will also gain critical understandings of ethical and legal implications of working with healthcare data.

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.

Innovation-driven Entrepreneurship - Data Science, Technology and Innovation (CMSE11515)

The course explores entrepreneurship and innovation as a subject of study and as a practice. This course teaches some of the generic understanding of entrepreneurship and innovation, as well as specialised topics related to Data-driven innovation and entrepreneurship. It raises the student's awareness of the legal, business, managerial, ethical, creative, analytical and interpersonal skills relevant to setting up and running a new venture, and more broadly, encourages students to be an innovative thinker in a variety of organisational contexts. The primary focus is on the development of an opportunity evaluation for a venture exploiting a novel idea and/or technology both for commercial and social purposes.

Introduction to Bioinformatics (PGBI11137)

The course provides an introduction to bioinformatics - the computational analysis of DNA and protein sequences and associated data. It is targeted to students who wish to gain understanding and skills in bioinformatics, whether out of general interest or to use in their projects, employment or future research. 

Introduction to Python Programming with Data Science (PGBI11123)

The course will consist of introductory programming learning material presented in the Python language. Exercises to demonstrate the main principles of computer programming through hands-on activities related to data science.

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. 

Message-Passing Programming (EPCD11002)

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 progresses.

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 (EPCD11005)

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 (EPCD11001)

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 (EPCD11018)

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.

Social Shaping of Digital Research (PGSP11389)

Between data collection technologies and professional users of digital research data lies a massive assembly of computational and analytical resources that together constitute information infrastructure and promise to revolutionize analytical practice. This course provides an understanding of the possible outcomes of the adoption of digital research in business & policy-making, based on evidence gathered from other disciplines that have been early adopters of eScience as well as other fields whose practices have been modified by engagement with information infrastructure. In particular, the course will provide a theoretical framework for understanding the functioning of the 'human infrastructure' (e.g. technicians and scientific users) that is required to sustain digital research tools and methods. By analyzing the building of information infrastructure as a process that involves the alignment and realignment of people, processes, and tools, the course will provide an understanding of information infrastructure as it appears from the perspective of those who are creating and using it.

Software Development (EPCD11017)

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.

Threaded Programming (EPCD11003)

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.

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.

Understanding Planetary Health & Data: An Introduction to the Concepts and Themes (VESC11243

This course introduces students to the key concepts and some of the themes of Planetary Health; e.g. systems thinking, planetary boundaries, pandemics, public health, climate change, food and nutrition security, conflict, sustainable futures. 

Data Types and Structures in Python and R  (HEIN11050

This course provides an introduction to data types and structures in health, social and care service settings. This course is designed to equip students with the skills required to handle, analyse and create tools to manage different data types and structures. Concepts are illustrated with examples from health, social and care services. 

Dissertation Projects: 

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.)

  • Deanery of Clinical Sciences : Imaging: Practical Work and Assessment (NEME11033)
  • Deanery of Molecular, Genetic and Population Health Sciences Dissertation Project (HEIN11053
  • EPCC Dissertation Project (EPCD11019)

  • School of Informatics Dissertation Project (INFD11003)

  • School of Social and Political Science (PGSP11499)

2024/2025 Academic Year Degree Programme Tables:

PG Certificate Data Science, Technology and Innovation (ICL) (PgCert) - 2 Years (PTPGCDSTIN2U)

PG Diploma Data Science, Technology and Innovation (ICL) (PgDip) - 4 Years (PTPGDDSTIN2U)

MSc (part time) Data Science, Technology and Innovation (ICL) (MSc) - 6 Years (PTMSCDSTIN4U)

MSc (full time) Data Science, Technology and Innovation (MSc) (PTMSCDSTHN1F)