Master of Science in Mathematical Sciences

Course overview

Statistics
Qualification Master's Degree
Study mode Full-time, Part-time
Duration 2 years
Intakes January, July
Tuition (Local students) $ 9,773
Tuition (Foreign students) $ 10,602

About

The Master of Science in Mathematical Sciences is a research programme that empowers students with a competitive edge in research through in-depth training mediated by experts. As a postgraduate student, candidates will be integrated as members of our research groups at Sunway University with various opportunities to participate in research alongside supervisors through seminars, workshops, laboratory and field work. The programme will focus on research areas such as evolutionary computation, big data analytics, computational fluid dynamics, statistical process control, and more. 

This programme will develop the following graduate attributes which are in sync with and support the mission of the university:

  1.   highly employable graduates who are knowledgeable and technically competent in various fields of mathematics and statistics.
  2.  graduates with the mathematical and analytical skills with competencies in critical thinking and problem solving.
  3. graduates with managerial and entrepreneurial skills to be able to communicate effectively.
  4. graduates who are ethical and responsible with expertise in mathematical and statistical knowledge.
  5. graduates who recognize the need to engage in life-long learning for personal and professional growth and development.

Graduates of this programme will be equipped with a versatile range of skills that lend themselves to multiple industries. This allows graduates to pursue senior positions in fields such as finance, education, consultancy, administration, IT, business, and more.

Admissions

Intakes

Fees

Tuition

$ 9,773
Local students
$ 10,602
Foreign students

Estimated cost as reported by the Institution.

Application

$ 31
Local students
$ 146
Foreign students

Student Visa

$ 502
Foreign students

Every effort has been made to ensure that information contained in this website is correct. Changes to any aspects of the programmes may be made from time to time due to unforeseeable circumstances beyond our control and the Institution and EasyUni reserve the right to make amendments to any information contained in this website without prior notice. The Institution and EasyUni accept no liability for any loss or damage arising from any use or misuse of or reliance on any information contained in this website.

Entry Requirements

  • A Bachelor’s Degree in mathematical sciences or related fields with a minimum CGPA of 2.75 or equivalent; or
  • A Bachelor’s Degree in the mathematical sciences or related fields with a minimum CGPA of 2.50 and not meeting CGPA of 2.75, can be accepted subject to rigorous internal assessment; or
  • An APEL.A Certificate (APEL T-7) (Recognition of Prior Learning) (Click on this link for further information on APEL.A)
  • Other Qualifications - Any other qualifications will be considered on a case-to-case basis and subject to the approval and acceptance by University Senate.​

English Language Requirements

  • IELTS 6.0 or equivalent

Curriculum

Modules

Candidates are required to complete two modules, namely Research Methodology and Directed Readings, in addition to the Thesis component. By undergoing these modules, candidates will develop the necessary skills and knowledge to conduct research successfully towards the completion of the thesis.

Thesis

The Master of Science in Mathematical Sciences is awarded based on the successful completion of a thesis. The thesis should demonstrate proficiency, criticality and mastery in the subject or chosen area of research.

Areas of Research

The School has a dedicated team of academicians who  will mentor and discuss possible research topics with  you in the following areas of research interests, but  not limited to:

  • Applied econometric
  • Big data analytics
  • Computational fluid dynamics
  • Evolutionary computation
  • Graph theory and combinatorics
  • Neural networks
  • Optimal control and numerical optimisation
  • Statistical modelling
  • Statistical process control
  • Time series analysis and forecasting

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