Are you looking to follow a career in data, or are you passionate about progressing in your chosen area of expertise? With data playing an increasingly significant role, and impacting all areas of our lives, expertise in data science is becoming ever more important.
The Master of Data Science is for students holding a degree that is not highly quantitative in nature. It provides the opportunity to study a programme rich in the substance of data science.
This course with a hard-core of data science will provide you with a Masters-level education focused on data science. Whether you have a background in social sciences, arts and humanities or any other discipline without data science at its core, this programme provides a range of options for you.
All around us, massive amounts of increasingly complex data are being generated and collected, for instance, from mobile devices, cameras, cars, houses, offices, cities, and satellites. Businesses, researchers, government, communities, and families can use that data to make informed and rational decisions that lead to better outcomes. It is impossible for anyone individual or group of individuals to keep on top of all the relevant data: there is simply far too much. Data science enables us to analyse large amounts of data effectively and efficiently and as a result, has become one of the fastest growing career areas.
The advent of new techniques and increases in computing power means that data science is now much more accessible to all. Our programme will support you to learn how to access, clean, analyse, and visualize complex data. This learning, along with your expertise in your degree subject or discipline, and experience will enable you to grasp the full meaning and significance of data in your area, undertaking data analysis intelligently and providing a significant employment advantage.
The programme
Whether you choose the core Master of Data Science or one of our more specific programmes relating to your knowledge, discipline expertise or area interest, you will find all introductory modules are designed to bring students up to speed with the background necessary for data science. This is done on a need-to-know basis, focusing on understanding in practice rather than abstract theory. Core modules then introduce you to the full range of data science methods, building from elementary techniques to advanced modern methods such as neural networks and deep learning. Optional modules allow you to focus on an area of interest.
The course provides training in relevant areas of contemporary data science in a supportive research-led interdisciplinary learning environment. The broad aims are:
To provide you with an advanced understanding of data science, with the skills needed to apply this to your work
To develop your skills to enable you to critically review, interpret and apply relevant data science knowledge to practical situations
To develop the ability to conduct research into data science issues that requires familiarity with a range of data, research sources and appropriate methodologies and ethical issues.
A number of subjects can be identified and defined within each application domain. Whilst a Masters cannot incorporate all subjects, a selection of subjects representative of each domain ensures that the course incorporates the necessary breadth and depth of material to ensure a skilled graduate.
The Masters allows for progressive deepening in your knowledge and understanding, culminating in the research project which is an in-depth investigation of a specific topic or issue.
The global dimension is reinforced through the use of international examples and case studies where appropriate.
Course Structure
Core modules
The Master of Data Science is comprised of the following core modules:
Introduction to Computer Science (optional under certain conditions)
Introduction to Statistics for Data Science
Ethics and Bias in Data Analytics
Machine Learning
Programming for Data Science
Strategic Leadership
Introduction to Mathematics for Data Science (optional under certain conditions)
Research Project
Examples of optional modules:
Text Mining and Language Analytics
Data Exploration, Visualization, and Unsupervised Learning
The Master of Data Science is research-oriented. Data Science is a driving force behind many subject specialisations today and aspects are delivered within the context of an active and varied research culture as is demonstrated via the associated academics and researchers within the Institute for Data Science
Students are also encouraged, through a range of modules, to develop research methods, skills and ethics reflecting the wide range of methods used by the research active staff. Research methodologies are actively taught through many other modules and assessments. They are also developed through innovative teaching practices such as simulations. Overall students are encouraged and guided to be ‘research minded’ in all modules, and to develop these critical skills for the future.
All modules taught on this degree are underpinned by research, and embed elements of research training both in the delivery and in the assessment.
The Master of Data Science uses a wide range of learning and teaching methods:
Lectures
Seminars
Workshops
Computer/practical classes
Independent study, research and analysis
Structured reading
Case studies
Data Science Project
Supervisions
Group and individual oral presentations
The project is a major research project, conducted and written up as an independent piece of work with support from the student’s appointed supervisor.
Student academic support and guidance is provided through the members of the Management Board, module coordinators, and individual lecturers. This support may take the form of face-to-face contact, telephone, e-mail, or other online contact, as appropriate.
Information, requirements and expectations regarding the programme overall are provided on the course SharePoint site. This is supplemented information on module aims/learning outcomes, content, key skills, formative and summative assessments and recommended reading.
Academic support to students is initially provided through an induction programme which provides an introduction to the University, the contributing departments, the programme, and key members of staff.
Entry requirements
A UK first or upper second class honours degree or equivalent in ANY degree that doesn’t include a strong data science component including those in social sciences, the arts and humanities, business, and sciences.
Evidence of competence in written and spoken English if the applicant’s first language is not English:
minimum TOEFL requirement is 102 IBT (no element under 23)
minimum IELTS score is 7.0 overall with no element under 6.0 or equivalent
The tuition fees shown are for one complete academic year of full time study, are set according to the academic year of entry, and remain the same throughout the duration of the programme for that cohort (unless otherwise stated).
Natural Sciences is the Faculty of Science “Department” that facilitates multidisciplinary and interdisciplinary degrees.
All around us, massive amounts of increasingly complex data are being generated and collected and business, research, government, communities, and families can use that data to make informed andrational decisions that lead to better outcomes. Data science enables us to analyse large amounts of data effectively and efficiently and as a result has become one of the fastest growing career areas.
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