Master of Data Science 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 (Taught Courses): 24 units (Code: T) 

 (ii)   Elective Courses: 12 Units (Code: E) 

       Choose any three (3) courses from the table below:

 (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 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 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 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:

• Devise a solution to a real-world problem using data science technique appropriately.
• Practice effective communication orally, the progress and achievement of the practicum.
• Perform work collaboratively in a multi-ethnic environment with superior, colleagues, staff and supervisors.
• Display professional behaviours such as trust, honest and non-violation of the predefined policy at the workplace.
• Display confidence and ability to overcome challenges in completing the project and practicum.
• Perform project tasks with proper planning to meet project milestone.
• Display high level of responsibility and accountability to lead the project independently.

The programme is offered on full-time basis with a minimum period of candidature of three (3) semesters and a maximum of six (6) semesters. The study schemes are as follows:

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

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

2.5 year (applicable to full-time study scheme only):

Course offering is given in the table below:

CDS501/4 – Principles and Practices of Data Science & Analysis

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

CDS502/4 – Big Data Storage and Management

Storing and managing big data addresses different issues compared to conventional databases. Big data involves huge amount of data (volume), supports heterogeneous data format (variety) and can be accessed at high speed (velocity). The course includes fundamental on big data storage and management related issues. Understanding of various storage infrastructure includes understanding of technologies ranging from traditional storage to cloud-based storage. The course provides exposure on recent technologies in manipulating, storing and analysing big data. The technologies include but not limited to Hadoop, MongoDB and Apache Cassandra.

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

• Compare the various data storage infrastructures, advanced concepts and technologies.
• Build a database to support big data using related big data storage system.
• Differentiate the rules of modern and traditional in storing and managing large data.
• Evaluate recent tools in big data storage.

CDS503/4 – Machine Learning

Upon successful completion of the course, students will have a broad understanding of machine learning algorithms. Students will be acquiring skills of applying relevant machine learning techniques to address real-world problems. Students will be able to adapt or combine some of the key elements of existing machine learning algorithms. Topics which will be covered in this course include supervised and unsupervised learning techniques, parametric and non-parametric methods, Bayesian learning, kernel machines, and decision trees. The course will also discuss recent applications of machine learning. Students are expected to obtain hands-on experience during labs, assignments and project to address practical challenges.

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

• Describe concepts, theories and implementation of machine learning algorithms.
• Build machine learning models which can be adapted to more complex scenarios.
• Apply relevant machine learning algorithms for typical real-world problems.
• Apply mathematical concepts to solve machine learning problems. 

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.

CDS505/4 - Data Visualisation and Visual Analytics    

This course discusses the use of computer-supported, interactive and visual representations of data in order to amplify cognition, help people reason effectively about information, find patterns and meaning in the data, and easily explore the datasets from different perspectives in particular in data-intensive environment. The course covers techniques from two branches of visual representation of data, namely data visualization and visual analytics. In data visualization, the course covers scientific visualisation techniques (representations of empirically-gathered scientific datasets) such as contours, iso-surface, and volume rendering as well as specific techniques in information visualisation (representations of abstract datasets) which include tables, networks and trees, and map-colour. In visual analytics, a visualization process features a significant amount of computational analysis and human-computer interaction. So, the topics covered in this part of the course include view manipulation, multiple views, reduction in items and attributes, and focus + context as well as analysis case studies involving a visualization system or tool.

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

• Select the right visualization techniques for any given problems or applications.
• Adapt visualization techniques and idioms, and visual analytics techniques in certain domains.
• Relate various visualization techniques with various domains and problems.
• Customise modern visualization software tools for applications in various domain.

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.

CDS513/4 - Predictive Business Analytics

The course provides the theory behind predictive analytics, and methods, principles and techniques for conducting predictive business analytics projects. The course introduces the underlying algorithms as well as the principles and best practices that govern the art of predictive analytics that translate data into meaningful, usable business information.

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

• Describe concepts, technologies and theories related to predictive business analytics.
• Design strategies relevant to predictive business analytics using appropriate technologies and tools.
• Assess the role of predictive business analytics in enhancing business performance.
• Propose a business model by incorporating predictive business analytic approaches.
• Evaluate predictive business analytics using recent tools.

CDS521/4 - Multimodal Information Retrieval

This course provides the basic concepts, principles and applications for multimodal (text, image, and video) retrieval. This course covers basic techniques for text, image and video retrieval such as indexing, representation, ranking, querying, GLCM, colour histogram, video shot detection and boundary detection and retrieval performance and evaluation. In addition, this course also covers machine learning retrieval approach techniques such as KNN, SVM and deep learning neural network for large dataset on the latest context such as mobile devices, social media and big data.

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

• Discuss techniques to multimodal information retrieval.
• Adapt the models and techniques of multimodal information retrieval in real applications.
• Solve problems in emerging multimodal applications using the learned techniques.

CDS522/4 - Text and Speech Analytics

This course focuses on the theory and tools of text and speech processing to retrieve textual features, speech signal and annotation, and linguistic information from text and speech resources. Using these resources, theory and tools are then used to perform analytic tasks to solve real-world problem.

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

• Demonstrate understanding of basic concepts and techniques in natural language text and speech processing, such as tokenization, ngram, tagging, parsing, word sense disambiguation, speech synthesis and speech decoding.
• Construct different levels of linguistics information such as word, sentence, semantics and spectrum for text and speech analytics using approaches in natural language text and speech processing.
• Propose custom solutions using natural language processing and speech processing techniques or text and speech analytics problems in organizations.

CDS523/4 - Forensic Analytics and Digital Investigations

This course introduces basic knowledge and techniques in computer forensics and digital investigation. Starting with an overview of the career of digital investigators, issues in digital forensics and investigations into public data, and current practices in the processing of criminal backgrounds and incidents will be described.

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

• Apply the principles and techniques in forensics analytics and digital investigations.
• Conduct digital investigations in computer forensics, mobile forensics, network forensics, image forensics and big data forensics.
• Demonstrate the digital investigations that adheres to professional standards and investigation processes: identification, preservation, examination, and analysis.
• Report the potential security breaches of computer data that suggest violations of legal, ethical, moral, policy and/or societal standard.

CDS590/8 - Consultancy Project and Practicum

• Click here to apply online!

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

• This programme is accredited by the Malaysian Qualifications Agency (MQA) with a reference number of MQA/SWA16882. 

• Click here to view the accreditation status listed at Malaysian Qualifications Register maintained by MQA.