Unit 1 - STEM for Creatives (20 Credits)
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 STEM academics who have worked in a creative industries setting.
Unit 2 - Natural Language Processing (NLP) for the Creative Industries (40 credits)
This practical class develops key coding skills to support Natural Language Processing (NLP) for the creative industries and introduces applied computer science concepts for arts and humanities graduates.
Unit 3 - Introduction to Data Science (20 credits)
This computing and seminar unit uses programming approaches to statistics, structuring data, analysing data and explores big data approaches to social media analysis including techniques such as topic modelling. It also gives a grounding in data ethics, data handling and GDPR.
Unit 4 - Artificial Intelligence for Media (20 credits)
This practical unit 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. It 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.
Unit 5 - Data Science in the Creative Industries (20 credits)
This unit is taught in partnership with our current industry partners and involves an industry case study of:
- Data science approaches to product development
- Applied approaches to campaign insight, customer interfaces, media analysis and generation
Unit 6 - Personalisation and Machine Learning (20 credits)
Unit 7 - Thesis Project (60 credits)
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.
Learning and teaching methods
- Lectures and seminars
- Studio/lab-based practice and masterclasses
- Project work
- Technical tuition
- Collaborative problem-solving and group work
- Independent study
- Project portfolio comprising of technical prototypes and presentations
- Essays and reports