MSc in Data Science

Study this programme anywhere in the world and receive a fully accredited University of London degree

Course Overview

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.

  • The maximum number of modules you can study in one session is six, (or four plus the final project). You will also receive comprehensive learning materials and support from online tutors.

Study materials

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.

Online support

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:

  • On the VLE you can access electronic copies of all printed study materials, resources including audio-visual, and forums to discuss course material and work collaboratively with others.
  • The Online Library(Opens in new window) provides access to over 100 million academic electronic items comprising E-books, E-journals, conference proceedings, etc. In addition, students can request items which are not held in the library via the library’s Inter-Library loans service with the British Library.
  • Senate House Library provides free reference access for all registered distance and flexible learning students.
  • Access to academic support and feedback from London-based support teams. Tutors introduce the modules, respond to queries, monitor discussions and provide guidance on assessments.

If you register for support at one of our recognised teaching centres you can attend lectures and benefit from, and receive tutor support.

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.

Student Support

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:

  • The Student Advice Centre – provides support for application and Student Portal queries.
  • TalkCampus – a peer support service that offers a safe and confidential way to talk about whatever is on your mind at any time of day or night.
  • Student Relationship Managers – a team of Student Relationship Managers (SRMs) are here to support and advise you throughout your studies. They aim to ensure that you are fully up-to-date with important and useful information about how best to complete your studies.

Time commitment

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.

Assessment

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.

What qualifications do you need?

Entry routes

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.

Entry Route 1 (MSc/PGDip/PGCert) and individual modules

To be eligible to register for any of the Data Science programmes, you must have the following:

  • A bachelor’s degree (or an acceptable equivalent) in a relevant subject which is considered at least comparable to a UK second class honours degree, from an institution acceptable to the University.
  • Previous degrees should normally include a sufficient level of programming such as Python detailed in your transcript. Whilst other degrees such as Engineering and Mathematics will be considered on a case by case basis.
  • If we consider your previous degree as non-relevant then we will request you take our MOOC, Foundations of Data Science: K-means Clustering in Python, before you start our Data Science programme. This MOOC requires approximately 30 hours of study.

Entry Route 2 (MSc/PGDip/PGCert) and individual modules

  • A bachelor’s degree (or an acceptable equivalent) in any subject which is considered at least comparable to a UK second class honours degree, from an institution acceptable to the University.In addition to the above, you will be required to complete an online preparatory course prior to registration. The online preparatory course, Foundations of Data Science: K-Means Clustering in Python, requires approximately 30 hours of study.

English Language requirements

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:

  • IELTS: at least 6.5 overall, with 6.0 in the written test.
  • TOEFL iBT: at least 92 overall, with 22+ in reading and writing and 20+ in speaking and listening.
  • Cambridge Certificate of Proficiency in English.
  • Cambridge Certificate of Advanced English (at grade C or above)
  • Duolingo: must achieve an overall score of at least 120.

Alternatively, you may satisfy the language requirements if you have at least 18 months of education or work experience conducted in English.

Computer requirements

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.

KEY INFORMATION

Course Price £8,101
Course Length 2 Years
Learning Method(s) Online learning