Programme Schedule 2024/25

The MTD (Data Science) is a one-year full-time coursework-based Master programme. It comprises eight courses (96 credits): two core design courses, six specialised courses, including one design project, organised as follows:

Term 1 (Sep - Dec)

Course Title Credit Points Course Type
Innovation by Design 12 Design Core
Data, Technology and Design 12 Specialisation Core
Statistical Learning for Data Science 12 Specialisation Core

Term 2 (Jan - Apr)

Course Title Credit Points Course Type
Design Science 12 Design Core
Machine Learning and Analytics 12 Specialisation Core
Optimisation for Data Science 12 Specialisation Core

Term 3 (May - Aug)

Course Title Credit Points Course Type
Digital Twins in Data-Driven Decisions 12 Specialisation Core
Design Project 12 Experiential Learning

Course Descriptions

Innovation by Design (Term 1)

The focus of this course is the integration of marketing, design, engineering and manufacturing functions in creating and developing a new product, system or service. The course will go through the different phases of designing a new product, system or service and will focus on some of the critical success factors for new product development, with an early emphasis on design thinking. The overall framework of this programme and its accelerated pace will be set by this introductory course, where students will be given a design challenge to complete during the term.

Data, Technology and Design (Term 1)

This course addresses three key issues with regard to the active use of data: (i) the theory and practice of data curation, data scrubbing, data preparation, and data (ii) data visualisation (dashboards and infographics) with effective and aesthetic techniques, (iii) data governance, data stewardship, and data ethics. Basic knowledge of a programming language like Python/R is expected for this class.

Statistical Learning for Data Science (Term 1)

In this course, we will study how to imbue machines with intelligence, focusing on foundational principles and mathematical theories of real-world modeling, problem-solving, and statistical learning. We will draw upon strategies from biological intelligence such as neural networks and reinforcement learning. Students will learn powerful concepts from decision theory, information theory, generative models, deep learning, dimensionality reduction, expectation-maximization, time-series prediction, control theory, and machine reasoning, and will exploit software tools for building intelligent systems. Algortihms will be implemented using Python/ R programming.

Design Science (Term 2)

This course will introduce students to design science. Many design principles and methods are reviewed, applied and analysed. Students will learn to make connections between design science and other fields in, for example, engineering and how principles in design science can be used to advance these fields. The class will cover a broad set of design methods such as customer needs analysis, methods in creativity, functional modelling, design for X, design for testing & verification.

Machine Learning and Analytics (Term 2)

This course offers an in-depth exploration of the theory and algorithms underpinning modern machine learning, with a primary focus on their applications in the realm of data science. Beyond a purely statistical perspective, the course adopts a multidisciplinary approach, encompassing concepts from approximation theory in high dimensions, dynamical systems, stochastic processes, and other relevant areas. This broad perspective allows students to appreciate the interdisciplinary nature of modern machine learning and its implications for addressing complex real-world challenges. This course also goes beyond traditional domains such as computer vision and natural language processing (NLP), exploring novel applications of machine learning across engineering, sciences and more. Through research and practical case studies, students will gain insight into the transformative potential of advanced analytics in diverse fields.

Optimisation for Data Science (Term 2)

The course will start by providing basic principles of linear optimisation and gradually cover parts of discrete, convex, robust, non-linear optimisation methods and algorithms. Topics used extensively in data and science and machine learning to be covered include linear programming, duality, gradient descent, sensitivity analysis, two-player zero-sum games, integer programming, branch, and bound methods, backpropagation, and so on. Throughout the course, a number of applications from various areas will be discussed.

Digital Twins in Data-Driven Decisions (Term 3)

This course will introduce students to various aspects of analysing complex systems using digital twins. Four main forms of simulation of systems namely event simulation for solving queuing and inventory problems, multi-agent system simulation involving agent-based simulation for solving complex engineering problems, Monte Carlo simulation with applications in financial engineering, and System Dynamics to model physical and business phenomena.
Case studies with applications to airport facility design, financial engineering, healthcare (A&E), and inventory management will be discussed. Implementation of simulation techniques, comparison of competing designs and statistical analysis of output will be conducted using a variety of programming tools including AnyLogic, Arena, Flexsim and R.

Design Project (Term 3)

The project provides students with hands-on experience working with data and is conducted in groups of two to three. Each group will be assigned a faculty advisor. They will work on a topic of their interest mutually decided upon with their faculty advisor which will involve elements of data science and design.

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