Learn how to apply technology to real world data science problems and gain an in depth understanding of emerging technologies, statistical analysis and computational techniques.
Study based on your interests: specialise in AI or Fin Tech and acquire transferable skills to advance your career aspirations.
You can study this online programme from anywhere in the world. The flexible approach to learning enables you to fit your studies around your commitments whilst providing the academic rigour and structure of an on-campus programme.
Modules are offered over two 22-week sessions each academic year. You choose which sessions to enter and how many modules to take in each session.
Assessment deadlines are outlined clearly in advance of the session.
We provide you with all of the resources and study materials you need to complete the course successfully, including the essential reading for each module. You can access these through the Virtual Learning Environment (VLE) on a range of devices.
Our online learning resources typically include multimedia content, activities and exercises (e.g. multiple choice quizzes, reflective exercises and self-assessment questions), as well as facilities for you to interact with your tutor and fellow students.
When you register, we will give you access to your Student Portal. You can then access your University of London email account and other key resources:
If you register for support at one of our recognised teaching centres you can attend lectures and benefit from, and receive tutor support.
All students receive tutor support and feedback while studying this programme. Tutors introduce the modules, respond to queries, monitor discussions and provide guidance on assessments.
Web-supported learning: if you register for a module as a web-supported learner, you join an online tutor group.
Institution-supported learning: if you enrol for a module with a local teaching centre, you receive face-to-face tuition. We work with several teaching centres in a number of countries and will recruit more to support the programme.
We are committed to delivering an exceptional student experience for all of our students, regardless of which of our programmes you are studying and whether you are studying independently or with a Recognised Teaching Centre.
You will have access to support through:
Study at your own pace, either part-time or full-time. Once you begin a module it is generally expected that you will complete it in the six-month session. Each module presents about 150 hours of study. Over a 22-week session, a 15 credit module will typically require five to seven hours of work/effort per week, and a 30 credit module will typically require ten to 15 hours of work/effort per week.
Each module includes a mix of assessments. During your study period you will undertake formative assessments, which help you to measure your progress but do not count towards your grade, and summative assessments Summative assessments do count towards the final grade. These include a mid-session coursework submission and an unseen written examination (or final project) at the end of the session.
Written examinations are held twice a year. You can defer sitting an exam once (subject to a fee) but you cannot defer the submission of coursework.
We offer two entry routes into the programmes, so if you do not meet the academic requirements you may still be eligible to apply through an alternative route.
To be eligible to register for any of the Data Science programmes, you must have the following:
You need a high standard of English to study this programme. You will meet our language requirements if you have achieved one of the following within the past three years:
Alternatively, you may satisfy the language requirements if you have at least 18 months of education or work experience conducted in English.
As this is a technical degree, you will need regular access to a computer with an internet connection and a minimum screen resolution of 1024×768. You will also need Adobe Flash Player to view video material and a media player (such as VLC) to play video files.