Postgraduate Studentships - Search for funding opportunities.
Postgraduate Studentships - Search for funding opportunities.
Study Details: MSc 1 year full-time
This MSc course gives you the opportunity to learn computer and data science skills, as well as introducing you to functional approaches to AI.
Our focused master’s programme offers you a further engagement with core data science competencies, and functional approaches to developing AI products and services. Furthermore, the ethical dimension of data science is actively explored in this course ensuring you have a deep understanding of the power of this technology.
You will apply scientific principles to support creation of mathematical models of real-world problems through computer programming. Different competencies will be measured across the programme through project work and core programming. Special attention will be given to the development of your thesis project that targets your preferred technology sector or domain of work. This is to support your progression to industry or academic research.
What to expect
An applicant will normally be considered for admission if they have achieved an educational level equivalent to an honours degree in either the broad fields of:
Educational level may be demonstrated by: Honours degree (named above); Possession of equivalent qualifications in a design-related or creative discipline; Prior experiential learning, the outcome of which can be demonstrated to be equivalent to formal qualifications otherwise required. Your experience is assessed as a learning process and tutors will evaluate that experience for currency, validity, quality and sufficiency; Or a combination of formal qualifications and experiential learning which, taken together, can be demonstrated to be equivalent to formal qualifications otherwise required.
Applicants without the required qualifications, but with professional experience may be eligible to gain credit for previous learning and experience through the AP(E)L system.
For fees and funding information, please see website
Graduates will be well placed to work in the following areas:
STEM for creatives
This unit offers a conversion boot camp for STEM study for arts and humanities graduates including the maths that underpins the data science approaches later in the course. This unit is taught by a STEM academics who have worked in a creative industries setting.
Natural language processing for the creative industries
This practical class develops key coding skills to support NLP for the creative industries and introduces applied computer science concepts for arts and humanities graduates. Students will use coding languages such Python and JavaScript to develop approaches to text analysis, text generation, chatbots, conversational interfaces for sectors such as data journalism, art practice, social media analysis and bias in large language models.
Introduction to data science
This computing and seminar class uses programming approaches to statistics, structuring data, analysing data and explores approaches to questioning real world datasets. This units also gives a grounding in data ethics, data handling and GDPR.
Artificial intelligence for media
This practical class introduces students to practical Artificial Intelligence tools such as Tensorflow an pyTorch in order to do signal processing classification, regression, style transfer, image and video generation and includes exploring techniques such as, deep fakes, GANS, pix-2-pix and others. You will benefit from tuition from senior CCI researchers in this area and our relationships with industrial product teams such as Google Brain.
Data science in the creative industries
This unit is taught in partnership with our current industry partner (WPP) and involves an industry case study of data science approaches to product development and applied approaches to campaign insight, customer interfaces, media analysis and generation.
Personalisation and machine learning
This practical class look at extending your machine learning experience to include the building and testing of recommenders and audience analysis tools. This Python and JavaScript based applied computing experience enables you to build and test systems that specifically test clustering for audience preferences.
Thesis project
This self-directed unit ask you to build a practical project and write an associated thesis report of 8-10,000 words that documents your technical methods, process of design and development and evaluation.
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