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
- A Bachelor’s Degree of Computing or its equivalent with minimum CGPA of 2.75 from higher institutions recognized by the Senate; OR
- 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
- 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.
- 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 |
2 |
6 |
| Core |
6 |
18 |
| Project |
2 |
10 |
| Elective |
2 |
6 |
| Total |
12 |
40 |
List of Courses
University’s Course
| No. | Course’s Code | Core Course | Credit |
| 1 | MPSW5013 | Research Methodology | 3 |
| 2 | MPSW5063 | Entrepreneurship | 3 |
Core Course
| No. | Course’s Code | Core Course | Credit |
| 1 | MTDS 5113 | Fundamental of Data Science | 3 |
| 2 | MTDS 5123 | Big Data Management | 3 |
| 3 | MTDS 5133 | Applied Statistical Methods | 3 |
| 4 | MTDS 5143 | Applied Machine Learning | 3 |
| 5 | MTDS 5153 | Big Data Analytics and Visualization | 3 |
| 6 | MTDS 5163 | Modelling and Decision Making | 3 |
Elective Courses (Select TWO only)
| No. | Course’s Code | Credit | |
| 1 | MTDS 5213 | Special Topics in Applied Data Science | 3 |
| 2 | MTDS 5223 | Manufacturing Analytics | 3 |
| 3 | MTDS 5233 | Social Media Analytics | 3 |
| 4 | MTDS 5243 | Geospatial Analytics | 3 |
| 5 | MTDS 5253 | Healthcare Analytics | 3 |
| 6 | MTDS 5263 | Tourism Analytics | 3 |
| 7 | MTDS 5273 | Logistics and Transportation Analytics | 3 |
| 8 | MTDS 5283 | Customer and Financial Analytics | 3 |
Project
| No. | Course’s Code | Project | Credit |
| 1 | MTPU 5314 | Project I | 4 |
| 2 | MTPU 5326 | Project II | 6 |
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)