Data Science and Artificial Intelligence (Conversion Programme) MSc

Queen Mary University of London

Prepare for the jobs of the near future with this conversion MSc programme, designed to enable learners without computer science backgrounds to train as artificial intelligence data specialists.This programme is ideal for highly motivated applicants seeking to develop knowledge and technical expertise in data science and AI. Since applicants come from various academic backgrounds, we strongly recommend that you review our guidance for completing your application tailored specifically to this programme.Discover data science and AI principles to create solutions for business and industry challenges.Learn from experts engaged in cutting-edge research in Machine Learning, Natural Language Processing, Computer Vision, and Game AI.Receive intensive teaching on programming and statistics, to equip you for the specialised modules in this programme.This programme was developed by a collaborative partnership of universities and industry partners, brought together by the Institute of Coding.This degree programme aims to help learners without strong backgrounds in programming or statistics to acquire a level of skill in these areas, before learning to apply them to specialist AI techniques.AssessmentThe intensive preparatory modules are assessed by submission of a completed lab book and a short in-class diagnostic test.Standard modules are assessed through a combination of coursework and written examinations.The project is assessed via a written report, a formal oral presentation and, where applicable, a demonstration of any software and/or hardware you have developed.TeachingThe two preparatory intensive modules will be taught through lectures and iterative learning approaches over two weeks, taking place before the start of the academic term. Teaching for all other modules includes a combination of lectures, seminars and use of a virtual learning environment. Each module provides contact time with your lecturers, supported by lab work and self-directed further study.

School of Electronic Engineering and Computer Science