Graduate Diploma in Data Science (University of London)

• A *first degree completed in a minimum of three years on a full-time basis (or equivalent) from a university or other institution acceptable to the University of London

University of London graduates from the same range of degrees under the academic direction of LSE may be considered for the Graduate Diploma on a case to case basis.

Equivalent International Qualifications

For information on international qualifications, refer to SIM’s International Student Prospectus. 

English Language Requirements

• You must have a minimum Grade C6 and above in GCE 'O' Level English language examinations or an equivalent qualification to apply to the University of London.

Mathematics Requirements

• 2 satisfactory mathematical subjects** done at degree level (or its equivalent)

Notes:  

** Satisfactory mathematical subjects include: Algebra, Algorithms, Business Mathematics, Business Statistics, Calculus, Computer Mathematics, Differentials, Engineering Mathematics, Geometry, Mathematics, Quantitative Methods, Quantitative Techniques, Statistics or Trigonometry.

As the world becomes ever more data-driven, many companies are exploiting quantitative techniques for their businesses. Graduates of this programme will be highly sought after by the employers as the race to gain a competitive edge in the data arena intensifies. 

Graduates of this programmes may progress to further postgraduate studies in this field, either locally or overseas.

Structure

• Blended lectures, group discussions, workshops and online study.
• Duration of each lesson is 3 hours.
• Students will attend classes with those pursuing a Bachelor’s programme at SIM campus.
• Classes are taught by local faculty, supplemented by webinars by the London School of Economics on the Virtual Learning Environment.
• Academic materials include Virtual Learning Environment, SIMConnect portal, subject guides, past exam papers and exam commentaries, reading lists and handbooks on good study strategies.
• Learning support include intensive revision, classes on study skills, academic writing and the Peer-Assisted Learning sessions.
• Full time classes are held in three-hour blocks between Monday and Friday, starting at 8.30am, 12pm, or 3.30pm. There are occasional classes on weeknights at 7pm and weekends.
• Average teacher-student ratio: 1:79
• Minimum class size to commence is 25 students. Students will be informed within 30 days after the application period.

Assessment & Attendance

• Assessment for IS2184, ST2187, ST3188 and ST3189 is a mixture of coursework and 2-hour examinations. 
• Attendance and Coursework are part of the University's requirements to sit for the examinations every May/June. SIM students may take resits in the Oct/Nov sitting.
• Students may have a maximum of 3 attempts for each paper prior to classification. 
• Key dates:
• • SIM Preliminary Exams: Feb 2024 
  • University Exams: May – Jun 2024 
  • Results released: Mid-Aug 2024 
• Attendance requirement
  • Local: 75% 
  • International (Dependent Pass holders/ Long-term Visit Pass holders): 90% 

Modules

The programme is made up of 4 full modules. There are no prerequisites or exemptions for each module. 

Compulsory modules 

• IS2184 Information systems management 
• ST3189 Machine learning 

Two full modules from the following: 

• EC2020 Elements of econometrics 
• MT2116 Abstract mathematics 
• ST2187 Business analytics, applied modelling and prediction 
• ST2133 Advanced statistics: distribution theory (half module) 
• ST2134 Advanced statistics: statistical inference (half module) 
• ST2195 Programming for data science 
• ST3188 Statistical methods for market research 
• IS2182 Innovating digital systems and services 
• IS3159 Research project in digital innovation 
• IS3167 Management and innovation of e-business 
• IS3183 Management and social media