Master of Science in Data Science and Artificial Intelligence (University of London)

Entry Route 1

Applicants 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 of London.

Entry Route 2

If applicants do not meet the above academic requirements, their applications may be considered based on the following

• 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 of london and the successful completion of the **online preparatory course, Foundations of Data Science, prior to registration.
• There is no entry test requirement for the MOOC course. However, there will be assessment during and at the end of the MOOC course.

*The subjects that would be considered as relevant are: Computing, Data Science, Computer Science, Business Computing, Games Programming, Physics, Engineering, Mathematics and statistics, Marketing and Finance.

**Students should sign up with Coursera at least 3 months before the intended intake. Do aim to have the results at least one month before the applications close. While completing the MOOC, students may submit an SIM application.

English Language Requirements

Applicants must provide proof of competence in English acceptable to the University such as a minimum Grade C6 and above in the GCE 'O' Level English Language examination or its equivalent.

Exemptions

Recognition of Prior Learning (RPL) is the recognition of previously acquired learning which can be mapped against particular learning outcomes of modules within a programme. RPL may be awarded if you have previously studied a similar module in the same depth, at degree level, and you achieved good marks in the corresponding examination. A student who is awarded RPL for a specific module is considered to be exempted from the module.

The qualification on which your RPL is based must have been obtained within the five years preceding the application. Candidates must have completed all coursework and assessment for their course.

Visit course website for qualifications eligible for exemption and their corresponding RPL

There is a need for AI talents to serve the industry and to drive future research. These skills lead naturally to embarking on a variety of careers, with employers from leading technology firms, robotics, military, academia, and public research sector.

Graduates can see themselves working as software developers and engineers, programmers and data analysts; other variety of specialisms, from fraud detection to spacecraft control; and other wide range of AI-related industrial and academic posts.

Structure

• All class are conducted on SIM campus unless otherwise stated.
• Duration of each lesson is typically 3 hours. 
• Programme comprises of the following activities:
  • Blended Lectures
  • Online Resources
  • Workshops
  • Consultations
• Classes are taught by experienced lecturers from SIM.
• Average teacher-student ratio: 1:18
• There must be a minimum of 25 students for the programme to commence. Students will be informed at least one month prior if there is insufficient enrolment.

Assessment & Attendance

• A recommended rate of 75% attendance is to be maintained.
• Assessment by the University is made up of coursework and examination (for selected modules). 

Visit course webpage for a detailed Assessment criteria.

*The written examination is three hours in length. It comprises three sections with a mix of qualitative and quantitative questions in total.

Modules

The Master of Science in Data Science and Artificial Intelligence is a 180-credit programme. A student must complete:

• Four core modules (60 credits total)
• Three compulsory modules (30 credits total) 
• Three optional modules (60 credits total)

Core Modules

(Must pass all assessment elements)

• Artificial Intelligence
• Data Programming in Python
• Machine Learning
• Statistics and Statistical Data Mining

Compulsory Modules*

• Big Data Analysis
• Data Science Research Topics
• Neural Networks

Optional Modules

(Choose any three)

• Blockchain Programming
• Data Visualisation
• Financial Data Modelling
• Financial Markets
• Mathematics for Data Science
• Natural Language Processing
• R for Data Science
• Social Media and Network Science

Project

• Final Project

*Rules for Compensation 

The University will allow compensation for an assessment element within optional and compulsory modules if: 

• The mark awarded for one of the assessment elements is between 45%-49%; AND 
• The mark for the other assessment elements is sufficient to produce an overall combined weighted pass mark for the module 

The University will NOT allow compensation for any assessment elements within core modules and the final project.