Master of Digital Transformation Coursework Mode

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

The following table provides the matrix of programme learning outcomes.

Credit requirements: 44 units

 (i) Core Courses: 24 units (Code: T)

• CDT541/4 – Industrial Digital Transformation
• CDT542/4 – Digital Entrepreneurship
• CDT543/4 – Systematic and Lean Innovation Management
• CDT544/4 – Enterprise Architecture for Digital Business Transformation
• CDS501/4 – Principles and Practices of Data Science and Analytics +
• CDS506/4 – Research, Consultancy and Professional Skills + 

(ii) Elective Courses: 8 Units (Code: E)

Choose any two (2) courses from the electives below:

(iii) Project (Core): 12 units (Code: T)

CDT594/12 – Digital Transformation Project and Practicum

This experiential work-based learning course designed to equip students to confidently help conceive, lead and execute digital transformation initiatives and develop new business models for existing organizations through the implementation of a consultancy project. Students are required to complete the practicum at their respective workplaces or their chosen/assigned organisations.  Students work under the supervision of a lecturer and an industry supervisor.  The students are required to solve a real-world problem or tap opportunities related to digital transformation 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.

The period of candidature for a full-time programme is between one-and-a-half (1.5) to three (3) years, and for a part-time programme is between two (2) to four (4) years.

The study schemes are as follows:

1.5 years (applicable to full-time study scheme only):

2 years (applicable to full-time and part-time study schemes):

2.5 years (applicable to full-time and part-time study schemes):

Course offering is given in the table below:

CDS501/4 – Principles and Practices of Data Science and Analytics

This course introduces the basic goals and techniques in data science and analytics process with some theoretical foundations which include useful statistical and machine learning concepts so that the process can transform hypotheses and data into actionable predictions. The course provides basic principles on important steps of the process which include data collecting, curating, analysing, building predictive models and reporting and presenting results to audiences of all levels. Data science programming language and techniques are introduced in the course

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

• Organize effectively all the necessary steps in any data science and analytics project.
• Adapt the data science programming language and useful statistical and machine learning techniques in data science and analytics projects.
• Demonstrate the ability to communicate and present the data science results effectively.
• Apply statistical approach for data exploration and modelling to draw conclusions in data science and analytics project

CDS504/4 – Enabling Technologies and Infrastructures for Big Data

Data science is advancing the inductive conduct of science and is driven by big data available on the Internet. This course will explain the technologies and techniques to improve the access, security, and performance of big data processing, storage systems and networks.

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

• Distinguish major concepts of data science related to high-performance parallel and distributed computing as well as computing with emerging technologies.
• Design distributed processing solution and big data network using efficient techniques.
• Analyse the needs and issues for big data networks, including security to protect sensitive data with suitable access controls.

CDS506/4 – Research, Consultancy and Professional Skills

The course provides knowledge and effective skills that are required in research, consultancy and professional practice. For the research section, it will cover literature review, development of research questions, usage of theories, research design, data collection as well as related statistical analysis techniques including quantifying use experience and usability testing. For the consultancy skills, students will be equipped with the mindset tools and skills to provide effective consulting advice to clients. In the final section, professional issues, and different aspects such as ethical, legal and social in conducting research and consultancy will also be discussed.

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

• Compose a research proposal /consultancy project to solve a real-world problem using data science and analytics technique.
• Identify communication traits in research and consultancy effectively.
• Correlate professional issues inherent in research methods and consultancy appropriately.
• Propose consultancy project with a potential client appropriately.
• Display good governance in consultancy project responsibly.
• Conclude the results from the statistical analysis appropriately.

CDS511/4 – Consumer Behavioural and Social Media Analytics

This course provides a broad and interdisciplinary research and practice focusing on two areas: behaviour and web & social media analytics. Specifically, behaviour analytics concerns the process of systematically utilizing multimodal data to model human behaviour when consuming products as consumers. This involves human-computer interaction (HCI), user behaviour modelling, computational models of emotions, and emotion sensing and recognition. Social media analytics concerns the strategies to leverage powerful social media data concerning customer needs, behaviour and preferences. Students will learn strategies to derive insights from the above-mentioned data that are crucial for business decisions. 

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

• Describe concepts, theories, technologies and metrics related to consumer behaviour and social media analytics.
• Apply any programming language (e.g., Python) to construct predictive models (by extracting, analysing and deriving insights) from the related social media data for data-informed decision-making within a business perspective using analytics model
• Explain the concept of consumer behaviour by studying the influence consumer behaviour and personality as the lifelong learning process.
• Identify human behavioural cues across a variety of contexts using digital tools to understand consumer behaviour, facilitate better interaction and decision making.

CDS512/4 – Business Intelligence and Decision Analytics

The course focuses on the knowledge and skills to select, apply and evaluate business intelligence and decision analytics techniques which discover knowledge that can add value to a company. The course will also discuss innovative applications and exploitation of the current techniques and approaches related to business intelligences and performance measurement, and mathematical model to facilitate decision-making process in business and operations.

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

• Apply concepts, technologies and theories related to business intelligence and decision analytics.
• Design strategies relevant to business intelligence and decision analytics using appropriate technology and software.
• Assess the role of business intelligence and decision analytics in enhancing business performance.
• Propose a preliminary business model by articulating business ideas and perform a SWOT analysis to assess the strengths, weaknesses, opportunities and threats of an entrepreneurial decision.

CDT541/4 – Industrial Digital Transformation 

This course introduces the concepts and process of industrial digital transformation by leveraging the emerging and next-generation technologies to accelerate the adoption of digital transformation across industries. This course navigates the world of digital ecosystem, understand the collision between traditional and digital business model, understand how technologies disrupted the industries and the impact of transformation on innovation and decision-making within industries.

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

• Describe the concepts, process and journey of digital transformation, the importance of digital transformation, transformation ecosystem, and case studies of both public and private sectors.
• Leveraging digital transformation technologies and techniques to recommend a strategy to meet the demands of digitalisation across industries.
• Identify the role of technology in digital transformation, the disruptions within industries and learn how transformation can be achieved to facilitate better interaction and decision-making.
• Demonstrate the ability to communicate and present the transformation process effectively.

CDT542/4 – Digital Entrepreneurship

Technology has enabled a new age of entrepreneurship as entrepreneurs find digital tools that enable new ventures in order to exploit commercial opportunities around the world. This course provides students with expert guidance on using digital technology platforms to start new ventures. In addition, this course also gives students a background into digital entrepreneurship, some of the established models used in constructing a marketing strategy and a focus on how they can apply digital technology.

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

• Investigate the concepts of digital entrepreneurship.
• Design an innovative digital business idea with proper digital entrepreneurship strategy.
• Demonstrate negotiation skills when pitching new digital business ventures to potential investors.
• Initiate digital businesses prototype with minimal resources.
• Perform digital business experiments, analytics, and reporting.

CDT543/4 – Systematic & Lean Innovation Management

The TRIZ methodology focuses on systematic innovation and problem solving. All TRIZ tools including structured problem solving, function analysis, cause and effect chain analysis, ideality, S-curve analysis, etc., will be introduced. For Lean processes techniques, it introduces the concepts and principles of Lean processes techniques. Students will be introduced to the integration of Lean processes techniques with Six Sigma.

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

• Identify and apply various TRIZ tools for problem solving and innovation.
• Create systematic innovation through applying the various TRIZ tools.
• Differentiate the usage and application of various Lean process techniques, their integration with Six Sigma, and demonstrate them in industry projects.
• Develop the conceptual framework with respect to the main constructs and relationship between Lean processes techniques.

CDT544/4 – Enterprise Architecture for Digital Business Transformation

Enterprise architecture (EA) is required to address the digital innovation and transformation challenges faced by today’s organizations. This course introduces students to an understanding of enterprise architecture concepts, design principles, practices, tools, and techniques so that they can stay ahead of the digital curve.

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

• Evaluate the effectiveness of current EA business processes used in organizations.
• Design suitable EA improvements to current business processes based on the problems faced by organizations.
• Display appropriate role as an actor when managing information/data in an organization according to real life scenarios.
• Develop EA solution using an EA software tool (Essential Open Source).

CDT594/12 – Digital Transformation Project & Practicum

This experiential work-based course is designed to equip students to confidently help conceive, lead and execute digital transformation initiatives and develop new business models for existing organizations through the implementation of a consultancy project. Students are required to complete the practicum at their respective workplaces or their chosen/assigned organisations. Students work under the supervision of a lecturer and an industry supervisor.  The students are required to solve a real-world problem or tap opportunities related to digital transformation during their practicum.

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

• Devise a solution to the real-world problem using digital innovation and transformation techniques appropriately.
• Practice effective oral communication regarding the progress and achievements of the practicum.
• Perform work collaboratively in a multi-ethnic environment with superior, colleagues, staff and supervisors.
• Display professional behaviour such as trust, honesty, and not violating predefined policies at the workplace.
• Display confidence and the ability to overcome challenges in completing the project and practicum.
• Perform project tasks with proper planning to meet project milestones.
• Display high level of responsibility and accountability to lead the project independently.

CDT545/4 – Cyber Security in Digital Transformation

This course introduces students to the basic knowledge on cyber security and its applicability in digital transformation. Aspects and standard methods in related cyber security risk management will be explained. Students will be exposed to different applications of the knowledge on big data, cloud computing, Internet-of-Things, digital forensics, blockchain, etc.

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

• Apply the basic principles of cyber security, standard security models, access control concept and requirement, and technologies use in cyber security.
• Reproduce solutions for an organization's cyber security risk management, digital forensics, and audit based on various cyber security models according to the suitability of the situation.
• Justify various standard methods, techniques, and approaches in cyber security risk management such as threat and attack identification, weaknesses or vulnerabilities assessment, and cyber risk valuation for an organization.
• Formulate factors which should be considered in solving cyber security issues relating to emerging technologies and applications.

• Click here to apply online! 

• Click here to get more information on admission requirements, language requirements, duration, semester intake, and fees.

• This programme is provisionally accredited by the Malaysian Qualifications Agency (MQA) with a reference number of MQA/PSA16009.

• Click here to view the provisional accreditation status listed at Provisional Accreditation portal maintained by MQA.