Master of Data Science - School of Computer Sciences

Introduction and Objective

The goal of this programme is to produce workforce/human resource in the field of Big Data Analytics who are capable of making decisions based on the availability of comprehensive data. Therefore, the objective of this programme is to produce graduates who:

• have a deep understanding of the core concepts, practices and tools in the domain of Data Science and Analytics.
• have the knowledge and skills in collecting and consolidating data, data modelling and advanced analysis of data which can be applied in jobs across various sectors, especially the service, business, marketing, manufacturing and healthcare sectors.
• can act as high-power data (open source or industry standards) users in a variety of industries as well as have social, ethical and effective communication skills, and good leadership attributes.
• are innovative in the application and use of big data analytic tools in various fields or as researchers who are capable of making decisions based on the availability of comprehensive data as well as able to realise lifelong learning and pursue studies at a higher level in cross-disciplinary research involving ICT.

Programme Structure

Credit requirements: 44 units

Core Courses: 24 units (Code: T)

1. CDS501/4 – Principles and Practices of Data Science and Analytics
2. CDS502/4 – Big Data Storage and Management
3. CDS503/4 – Machine Learning
4. CDS504/4 – Enabling Technologies and Infrastructures for Data Science
5. CDS505/4 – Data Visualisation and Visual Analytics
6. CDS506/4 – Research, Consultancy and Professional Skills

Elective Courses: 12 Units (Code: E)

Choose any three (3) courses from the below:

(i) Business Analytics

1. CDS511/4 – Consumer Behavioural and Social Media Analytics
2. CDS512/4 – Business Intelligence and Decision Analytics
3. CDS513/4 – Predictive Business Analytics

(ii) Multimodal Analytics

1. CDS521/4 – Multimodal Information Retrieval
2. CDS522/4 – Text and Speech Analytics
3. CDS523/4 – Forensic Analytics and Digital Investigations

(iii) Project (Core): 8 units (Code: T)

CDS590 – Consultancy Project and Practicum

This experiential work-based learning course prepares students to be a data scientist/analytics consultant by enhancing the students’ knowledge and skills in research, planning and implementation of a consultancy project in the field of data science/analytics, which can be applied to real-life situation. Students are required to complete the practicum at their respective workplace or their chosen/assigned organisation. Students work under the supervision of a lecturer and an industry mentor. The students are required to solve a real-world problem or tap opportunities related to data science and analytics during their practicum. The prerequisite of this course is CDS506 which must be taken in the preceding semester. The students are required to secure practicum placement together with project proposal during CDS506.

At the end of this course, the students will be able to:

• Perform work collaboratively in a multi-ethnic environment with superior, colleagues, staff and supervisors.
• Analyse the needs and/or problems related to data analytics in the workplace.
• Identify suitable quantitative and analytical tools (including software, technology and technical know-how) to propose usable solution to complex, ambiguous, unstructured problem in real world setting.
• Practice effective communication in writing and orally the progress and achievement of the practicum.
• Display leadership behaviours such as initiative, focussed, and high-performance standards.

Applicants should possess the following:

A. Bachelor degree

• A BSc degree in Computer Science/Information Technology degree or equivalent; or
  a) CGPA of 2.5 - 2.74/4.00 with
  • Research experience – 1 year; or
  • Professional experience in related field- 1 year; or
  • One(1) academic publication in related field; or 
  • Grade B for major/ elective courses; or
  • Grade B+ for final year project.

b) CGPA of 2.00 - 2.49/4.00 with

• Research experience - 5 years; or
• Professional experience in related field - 5 years, AND
• One (1) academic publications in related field; or
• Grade B for major/elective courses; or
• Grade B+ for final year project.
  
  
• A Bachelor's degree in Electronic Engineering/Electrical Engineering/Computer Engineering (60% of the core component must be Computer Science related) with a minimum CGPA of 2.75 or equivalent.
  
  
• Degrees awarded by local private institutions of higher learning, including those awarded under any collaborative/franchising schemes with local or foreign partners must be accredited by the Malaysian Qualifications Agency (MQA).

B. APEL A (Level 7)

(Applicable for International Applicants Only)

The minimum score for each programme can be vary from the below list, candidates are required to check for each programme requirements.

• A minimum of Band 6 for IELTS; or
• A minimum score of 60 for TOEFL (Internet-based); or
• A minimum of Band 8.5 for TOEFL Essentials (Online); or
• A minimum score of 169 for Cambridge English: Advance (CAE)/Proficiency (CPE) /Preliminary (PET) /First (FCE)/ Linguaskill Online min. score 169/ Occupational English Test (OET) min. score 250; or
• A minimum score of 59 for Pearson Test of English (PTE); or
• A minimum of Band 109 for CIEP Level (ELS); or
• A minimum of Band 4 for Malaysian University English Test (MUET)

Exemption is given to candidate if:

• English is the candidate’s mother tongue or National Language; or
• Candidate graduated from an Institution of Higher Learning in which the medium of instruction at Bachelor and/or Master degree level is English (statement of proof required)

IMPORTANT: Candidates are required to provide any of the above English result upon application. Incomplete application will be rejected.

• Full-time: Min 3 semesters / Max 6 semesters

• April & October

** Fees are subject to change

** New fees to be imposed starting from Academic Session 2025/2026 (October 2025 intake onward)

** Fees are subject to change