The Master of Technology (Data Science and Analytics) is aimed at recent graduates and industry practitioners from various academic discipline with strong analytic and computing skills or experience. The programme is designed to equip the students with fundamental and applied knowledge, technical skill, and current technologies in Data Science and Analytics area. These include the fundamental principles of data science, the capability to analyse a diversity of big data, the skills of using data science tools and applying the data analytics techniques to various domain, as well as the capability to present the analytics results to the intended audience. The programme’s delivery modes are through lectures, lab session, and industrial projects, emphasise on state-of-the-practice techniques, tools and technology, and recognised methodology through university-industry collaborations. Graduates from this programme will have career opportunities as data scientists, data analysts and many more.

Entry Requirement

  1. A Bachelor’s Degree of Computing or its equivalent with minimum CGPA of 2.75 from higher institutions recognized by the Senate; OR
  2. A Bachelor’s Degree of Computing or its equivalent, with a minimum CGPA of 2.50 and not meeting CGPA of 2.75, may be accepted subject to rigorous internal assessment process; OR
  3. A Bachelor’s Degree of Computing or its equivalent, with CGPA less than 2.50, with a minimum of five (5) years working experience in a relevant field may be accepted and subject to rigorous internal assessment process.
  4. A Bachelor’s Degree not related to the field of Computing must undergo appropriate prerequisite courses determined by the University and meet the minimum CGPA based on (i), (ii) to (iii).

English Requirement (for foreign student): 

  • Minimum TOEFL score: Master (520); OR 
  • Minimum IELTS score: Master  (5.0); OR 
  • Minimum MUET score: Master (3.0)

Mode of Learning & Duration of Study

  • Full-Time
    Minimum: 1 year (2 normal semesters and 1 short semester)
    Maximum: 3 years (6 normal semesters and 3 short semesters)
  • Part-Time
    Minimum: 2 years (4 normal semesters and 2 short semesters)
    Maximum: 4 years (8 normal semesters and 4 short semesters)

Curriculum Structure

Course Category

Number of courses

Credit Hours

University’s Course















 List of Courses

University’s Course

No.Course’s CodeCore CourseCredit
1MPSW5013Research Methodology3

 Core Course

No.Course’s CodeCore CourseCredit
1MTDS 5113Fundamental of Data Science3
2MTDS 5123Big Data Management3
3MTDS 5133Applied Statistical Methods3
4MTDS 5143Applied Machine Learning3
5MTDS 5153Big Data Analytics and Visualization3
6MTDS 5163Modelling and Decision Making3

Elective Courses (Select TWO only)

No.Course’s Code Credit
1MTDS 5213Special Topics in Applied Data Science3
2MTDS 5223Manufacturing Analytics3
3MTDS 5233Social Media Analytics3
4MTDS 5243Geospatial Analytics3
5MTDS 5253Healthcare Analytics3
6MTDS 5263Tourism Analytics3
7MTDS 5273Logistics and Transportation Analytics3
8MTDS 5283Customer and Financial Analytics3


No.Course’s CodeProjectCredit
1MTPU 5314Project I4
2MTPU 5326Project II6

Mode of Learning & Duration of Study:

  • Full-Time
    Minimum: 1 year (2 long semester and 1 short/special semester)
    Maximum: 3 year (6 long semester and 3 short/special semester)
  • Part-Time
    Minimum: 2 year (4 long semester and 2 short/special semester )
    Maximum: 4 year (8 long semester and 4 short/special semester)