MSc of Science (Data Science & Analytics)

This page will provide an overview of the Master in Data Science courses sorted out based on the core and elective subject.

Core Courses

CDS501 ~ 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. R programming language and statistical analysis techniques are introduced based on examples such as from marketing, business intelligence and decision support.

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

  • Organize effectively all the necessary steps in any data science and analytics real-world project.
  • Adapt the R programming language and useful statistical and machine learning techniques in data science and analytics projects.
  • Practice all the skills needed by the data scientist, which include acquiring the data, managing the data, choosing the modelling technique, writing the code, and verifying and presenting the results.

CDS502 ~ 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 infrastructures includes understanding of technologies ranging from traditional storage to cloud-based storage. The course provides exposure on recent technologies in manipulating, storing and analyzing 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.
  • Identify and master the rules of modern and traditional in storing and managing large data.

CDS 503 ~ Machine Learning

Upon successful completion of the course, students will have a broad understanding of machine learning algorithms. Students will be acquiring skills in 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 that 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 and assignments to address practical challenges. An understanding of the current state-of-the-art in machine learning is done via a review of key research papers allowing students to further research in machine learning.

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

  • To apply relevant machine learning algorithms for typical real-world problems.
  • Manipulate machine learning algorithms that can be adapted to more complex scenarios.
  • Synthesise findings and recommendations.

CDS 504 ~ Enabling Technologies and Infrastructures for Data Science

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 and storage systems. This course will help students to:

  • Acquire the necessary skills as an analyst for big data system.
  • Identify the security aspects of the data and determine the appropriate measures to protect it.
  • Have an exposure and training in designing basic infrastructure for the application of big data with sensitive nature of the low-power edge devices.

This course includes parallel and distributed processing, grid and cloud computing, big data tools, big data processing techniques, network infrastructure and architecture, network performance and security for big data.

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

  • Distinguish major concepts of data science which are high-performance parallel and distributed computing; computing with emerging technologies, and network performance.
  • Identify the needs and issues for big data security to protect sensitive data and suitable access controls.
  • Design a cloud platform and efficient techniques that can support end-users running latency-sensitive big data applications on low-powered edge devices.

CDS 505 ~ Data Visualisation & 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, isosurface, and volume rendering as well as specifics techniques in information visualisation (representations of abstract datasets) which include tables, networks and trees, and mapcolour. 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 for particular application.
  • Apply several techniques either by designing or developing specific visualization techniques or using existing tools.

CDS506 ~ 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 analysis techniques. 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:

  • Combine theory and consultation techniques to effectively meet clients’ needs
  • Adapt a structured and effective research method in data science and analytics research.
  • Correlate professional issues inherent in research methods and consultancy.

Elective Courses

Coming soon….