Bachelor of Science (Honours) in Data Science and Business Analytics

The programme aims to develop competence in students in the application of statistical and machine learning techniques at a high level. It will provide students with a sound knowledge of the principles underlying standard applications of probability and statistics. 

• Awarded by University of London, UK and Developed by federation member, London School of Economics and Political Science, UK.
• As the world becomes ever more data-driven, analytical skills are in high demand but very short in supply. Few companies are exploiting quantitative techniques to their full potential, often due to a lack of in-house expertise.
• They will be taught to demonstrate statistical software to analyse datasets and interpret the output, using a variety of algorithmic and model-based methods. It is important for graduates to learn to draw appropriate conclusions following empirical analysis and form the basis for managerial decision-making.

Prerequisite(s)

• This statistics degree programme requires candidates to have attained greater competency in mathematics prior to admission. 

Learning Outcomes
Upon successful completion of this programme, you will be able to: 

• Conduct data analysis and draw actional conclusions from computer output.
• Use complex mathematical and statistical models to tackle real-world problems.
• Navigate the intersection of business, management and data. 

Further Studies & Career Prospects
Graduates may progress to postgraduate studies either locally or overseas.

• Upon completing this programme, graduates will be highly sought-after by employers as the race to gain a competitive edge in the data arena intensifies. From asset management to pharmaceuticals, retail to insurance, the future is bright for any aspiring data scientist.
• Possible jobs would be Data Scientist, Data Analyst, Business Analyst and Data Engineer.

Why study at SIM x University of London

• SIM is the leading private education institution in Singapore.
• LSE is one of the foremost social science universities in the world with a worldwide reputation for the quality of its research.
• Excellent value, with opportunities for overseas study.
• Globally recognised – Programmes offered have the same academic standards as the London School of Economics and University of London, UK.

Structure

• Blended lectures, group discussions, workshops and online study. 
• Duration of each lesson is 3 hours. 
• 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. 
• Minimum class size to commence is 25 students. Students will be informed within 30 days after the application period. 
• 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 

Assessment & Attendance

• Assessment for IS2184, ST2187, ST2195, ST3188 and ST3189 is a mixture of coursework and 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.
• A pass or exemption from at least two Level 100 modules is required before they attempt Level 200/300 modules. 
• Students may have a maximum of 3 attempts for each paper prior to classification. 
• Key dates: 
• University Exams: May – Jun 2025
• Results released: Mid-Aug 2025
• Attendance requirement: 
• Local: 75% 
• International (Dependent Pass holders/ Long-term Visit Pass holders): 90% 

Standard Entry Route
100 modules 

• EC1002 Introduction to economics 
• MT1186 Mathematical methods 
• MN1178 Business and management in a global context 
• ST1215 Introduction to mathematical statistics, or
• ST104A Statistics 1 (half module) and **ST104B Statistics 2 (half module)

200 and 300 modules 

• ST2195 Programming for data science 
• ST2187 Business analytics, applied modelling and prediction (ST104A) or (ST1215) + (MT1174 or MT1186) 
• ST2133 Advanced statistics: distribution theory (half module) (ST104A + ST104B) or (ST1215) + (MT1174 or MT1186) and ST2134 Advanced statistics: statistical inference (half module) (ST104A + ST104B) or (ST1215) + (MT1174 or MT1186) 
• EC2020 Elements of econometrics (EC1002) + (ST104A) or (ST1215) + (MT1186), or
• MT2116 Abstract mathematics (MT1186), or
• IS2184 Information systems management 
• ST3188 Statistical methods for market research (ST104A) or (ST1215)
• ST3189 Machine learning (ST104A + ST104B) or (ST1215) + (MT1174 or MT1186) 
• One 300 module from Selection groups E, M or N 
• One 100, 200 or 300 module (or two half modules) chosen from any Selection group