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Programme

Master in Computer Science​

Master in Computer Science

Sarjana Sains Komputer

  • Programme Code: (N-DL/0613/7/0020) (01/29) (MQA/FA17374)
  • Status Programme: Full Accreditation
  • Institute: IPS/Malaysian Institute of Information Technology
  • Specialisation: Computing (Computer Science)
  • Study Level: Master (Mixed Mode) (ODL)
  • Intake: March & October Only
  • Duration: 1.5 years - 3 years (Full Time), 2 years - 4 years (Part Time)
  • Tuition Fees : Malaysian Students: RM31,000(Full Time/Part Time), International Students: RM 42,000. Remarks: * Fees shown are the tuition fees only

The Master in Computer Science (Open and Distance Learning@ODL) stands out as a unique eight courses program tailored for working adults. The curriculum encompasses five high-demand focus areas to be selected: Artificial Intelligence, Data Science, Information Security, System and Networking, and Software Engineering.

The Master in Computer Science (Open and Distance Learning@ODL) stands out as a unique eight courses program tailored for working adults. The curriculum encompasses five high-demand focus areas to be selected: Artificial Intelligence, Data Science, Information Security, System and Networking, and Software Engineering.

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Entry Requirement

Career Path

Programme Educational Objectives

Programme Learning Outcomes

Programme Contents

Our Open & Distance Learning (ODL) programmes offer flexibility, complete and interactive learning content with self-directed learning strategies. With ODL, you can access courses and learning materials online, from anywhere and at any time

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Entry Requirement

  • A Bachelor’s degree (Level 6, MQF) in Computing or related fields with a minimum CGPA of 2.75 or equivalent, as accepted by the UniKL Senate;
    • OR
  • A Bachelor’s degree (Level 6, MQF) in Computing or related fields with a minimum CGPA of 2.00 and not meeting a CGPA of 2.75 can be accepted subject to a thorough rigorous assessment as determined by the UniKL; OR
  • A Bachelor’s degree (Level 6, MQF) in Non-Computing field with a minimum CGPA of 2.50 can be accepted subject to a thorough rigorous assessment as determined by the HEP to identify the appropriate prerequisite courses that equivalent to their working experience in the Computing or related fields; OR
  • A Bachelor’s degree (Level 6, MQF) in Non-Computing fields with a minimum CGPA of 2.50 can be accepted subject to appropriate prerequisite courses;
    • OR
  • Other qualifications equivalent to a Bachelor’s degree (Level 6, MQF) in Computing or related fields recognised by the Government of Malaysian must fulfill the requirement on item i or ii.
    • For English requirements is as follows IELTS 6.0 OR TOEFL 600 OR its equivalent

Career Path

  • Software Engineering Software Engineer/Developer, Software Architect, Full-stack Developer, Front-end Developer, Back-end Developer, DevOps Engineer
  • System and Networking Network Engineer, Systems Administrator, Cloud Solutions Architect, DevOps Engineer, IT Infrastructure Manager, Network Security Engineer
  • Information Security Cybersecurity Analyst, Security Consultant, Penetration Tester/Ethical Hacker, Security Architect, Incident Responder, Cryptographer
  • Artificial Intelligence Machine Learning Engineer, AI Research Scientist, Natural Language Processing (NLP) Engineer, Computer Vision Engineer, Robotics
    • Engineer, AI Product Manager
  • Data Science Data Scientist, Data Analyst, Data Engineer, Business Intelligence Analyst, Machine Learning Engineer, Data Science Manager

Programme Educational Objectives (PEOs)

  1. Computing specialists who have advanced knowledge, applying enhanced technical and numeracy skills and focusing on the specific domains in the field of study to provide innovative solutions in computing.
  2. Computing specialists who have leadership skills and good interpersonal and communication skills to interact with stakeholders effectively.
  3. Computing specialists who engage and advocate lifelong learning activities and have an entrepreneurial mindset.
  4. Computing specialists who uphold and defend ethical conduct, professional practices, and issues in maintaining integrity.

Programme Learning Outcomes

  1. Integrate advanced knowledge related to research issues in computing.
  2. Recommend innovative solutions that are at the forefront of developments in the fields of study.
  3. Construct computing solutions and tools in terms of their usability, efficiency and effectiveness.
  4. Demonstrate effective interaction within a group and with a diverse audience through project discussions and participation in discourses related to the field of study.
  5. Exhibit effective communication within a group and with a diverse audience by publishing and presenting technical materials in the field of study.
  6. Utilize digital skills to acquire, interpret and extend knowledge in computing.
  7. Apply numerical skills to acquire, interpret and extend knowledge in computing.
  8. Demonstrate leadership, teamwork, autonomy and responsibility in delivering services related to the field of study.
  9. Exhibit capabilities to extend relevant knowledge through life-long learning.
  10. Exhibit capabilities of having an entrepreneurial mindset to the related field of study.
  11. Uphold professional and ethical practices in conducting research and delivering services related to the field of study.

Programme Content

Programme Structure by Semester.

    Semester 1
  • Advanced Computer Science
  • Algorithm Complexity Analysis
  • Innovation Technology and Entrepreneurship

    Semester 2
  • Research Methodology
  • Focus Area (1)*
  • Focus Area (2)*
  • Master’s Degree Dissertation 1

    Semester 3
  • Master’s Degree Dissertation 1


    Focus Area *
    Software Engineering
  • Software Requirement and Design
  • Software Quality and Testing

    System Networking
  • Mobile Computing
  • Advanced Networking

    Artificial Intelligence
  • Artificial Intelligence and Pattern Recognition
  • Advanced Machine Learning

    Information Security
  • Cyber Security Data Analysis and Visualizations
  • Information Security Management

    Data Science
  • Probability and Statistics for Data Science
  • Big Data Architecture