Postgraduate Programs 2024/25

Master of Philosophy and Doctor of Philosophy Programs in Data Science and Analytics

GENERAL INFORMATION
Award Title

Master of Philosophy in Data Science and Analytics
Doctor of Philosophy in Data Science and Analytics

Program Short Name

MPhil(DSA)
PhD(DSA)

Mode of Study

Both full- and part-time

Normative Program Duration

MPhil

Full-time: 2 years
Part-time: 4 years

PhD

Full-time: 3 years (with a relevant research master’s degree), 4 years (without a relevant research master’s degree)
Part-time: 6 years

Offering Unit

Data Science and Analytics Thrust Area

Information Hub

Program Advisor

Program Director:
Xiaowen CHU, Professor of Data Science and Analytics Thrust

INTRODUCTION

In the digital era, following advancements made in innovative technologies, data handling is growing at an unprecedented pace. The data-driven world opens tremendous possibilities and opportunities for companies and businesses for all industries as they can make use of the data information to create values for their business. As a disruptive consequence of the digital revolution, data science and analytics has become an emerging and cross-disciplinary field that requires knowledge and skills in many areas such as computer science, statistics and mathematics.

The Master of Philosophy (MPhil) and Doctor of Philosophy (PhD) Programs in Data Science and Analytics aim to facilitate close integration of statistical analytics, logical reasoning, and computational intelligence in the study of data processing and analytics. The programs will provide rigorous research training that prepares students to become knowledgeable researchers who are conversant in applying logic, mathematics, algorithms and computing power in the process of examining and analyzing data in academia or industry so as to derive valuable insights for making better decisions.

The MPhil Program aims to expose students to issues involved in the development of scientific, educational and commercial applications of data science and analytics. A graduate of the MPhil program should demonstrate a good working knowledge of issues in the discipline. He or she should be capable of synthesizing and creating new knowledge, making contribution to the field.

The PhD Program aims to develop the skills needed for students to identify theoretical research issues related to practical applications, formulate and undertake research that addresses issues identified, and independently find a data science and analytics related solution. A PhD graduate is expected to demonstrate mastery of knowledge in the discipline and to synthesize and create new knowledge, making original and substantial scientific contribution to the discipline.

LEARNING OUTCOMES

On successful completion of the MPhil program, graduates will be able to:

  1. Demonstrate critical thinking and analytical skills essential for solving real data science problems;
  2. Apply a range of qualitative and quantitative research methods for data science and analytics; and
  3. Translate and transform advanced research techniques effectively into data science practice in academic fields or industry.

On successful completion of the PhD program, graduates will be able to:

  1. Identify scientific and engineering correlations, significances, and insights in new data science and analytics models, algorithms, tools, principles, frameworks, solutions, and techniques;
  2. Demonstrate critical thinking and analytical skills from the perspective of data science and analytics;
  3. Apply a range of qualitative and quantitative research methods for data science and analytics;
  4. Translate and transform fundamental research insights effectively into data science practice in academic fields and industry;
  5. Exercise independent thinking and demonstrate effective communication skills in presenting and publishing scientific findings; and
  6. Conduct original research independently and competently showing in-depth knowledge in the field of data science and analytics.
CURRICULUM
  1. Minimum Credit Requirement

    MPhil: 15 credits
    PhD: 21 credits

  2. Credit Transfer

    Students who have taken equivalent courses at HKUST(GZ) or other recognized universities may be granted credit transfer on a case-by-case basis, up to a maximum of 3 credits for MPhil students, and 6 credits for PhD students.

  3. Cross-disciplinary Core Courses

2 credits

UCMP 6010
Cross-disciplinary Research Methods I
2 Credit(s)
Description
This course focuses on using various approaches to perform quantitative analysis through real-world examples. Students will learn how to use different tools in an interdisciplinary project and how to acquire new skills on their own. The course offers different modules that are multidisciplinary/multifunctional and generally applicable to a wide class of problems.
UCMP 6020
Cross-disciplinary Research Methods II
2 Credit(s)
Description
This course focuses on using various approaches to perform quantitative analysis through real-world examples. Students will learn how to use different tools in an interdisciplinary project and how to acquire new skills on their own. The course offers different modules that are multidisciplinary/multifunctional and generally applicable to a wide class of problems.
UCMP 6030
Cross-disciplinary Design Thinking I
2 Credit(s)
Description
This course focuses on user-collaborative design methods for generating inclusive product solutions that integrate stakeholder and product functionality perspectives. Students will create specified product/process/policy/protocol/plan (5P) concept models through the use of recursive user feedback engagement methods, experimental prototyping, and divergent and convergent ideation strategies. Featured topics include design thinking; stakeholder research; concept development, screening, and selection; and interaction design.
UCMP 6040
Cross-disciplinary Design Thinking II
2 Credit(s)
Description
This course focuses on user-collaborative design methods for generating inclusive product solutions that integrate stakeholder and product functionality perspectives. Students will create specified product/process/policy/protocol/plan (5P) concept models through the use of recursive user feedback engagement methods, experimental prototyping, and divergent and convergent ideation strategies. Featured topics include design thinking; stakeholder research; concept development, screening, and selection; and interaction design.
UCMP 6050
Project-driven Collaborative Design Thinking
2 Credit(s)

All MPhil students are required to complete UCMP 6050. All PhD students are required to complete either UCMP 6010 or UCMP 6030. Students may complete the remaining courses as part of the credit requirements, as requested by the Program Planning cum Thesis Supervision Committee.

PhD students who are HKUST(GZ) MPhil graduates and have completed UCMP 6010, UCMP 6030 or UCMP 6050 before may be exempted from this requirement, subject to prior approval of the Program Planning cum Thesis Supervision Committee.

  1. Hub Core Courses

4 Credits

Students are required to complete at least one Hub core course (2 credits) from the Information Hub and at least one Hub core course (2 credits) from other Hubs.

  Information Hub Core Course

INFH 5000
Information Science and Technology: Essentials and Trends
2 Credit(s)
Description
This inquiry-based course aims to introduce students to the concepts and skills needed to drive digital transformation in the information age. Students will learn to conduct research, explore real-world applications, and discuss grand challenges in the four thrust areas of the Information hub, namely Artificial Intelligence, Data Science and Analytics, Internet of Things, and Computational Media and Arts. The course incorporates various teaching and learning formats including lectures, seminars, online courses, group discussions, and a term project.

  Other Hub Core Courses

FUNH 5000
Introduction to Function Hub for Sustainable Future
2 Credit(s)
Description
This course covers background knowledge in the thrust areas of the Function Hub, including Advanced Materials, Sustainable Energy and Environment, Microelectronics, and Earth, Ocean and Atmospheric Sciences.
SOCH 5000
Technological Innovation and Social Entrepreneurship
2 Credit(s)
Description
This course discusses both opportunities and risks that technological breakthrough has brought to the human society. What would be the policy responses required to maximize its positive benefit and minimize its social costs? In particular, how could we utilize the technological advancement, entrepreneurial thinking to address the challenges our societies are facing, such as job loss/unemployment, income inequality and societal polarization, environmental degradation, health disparity, population aging, and among others. The course uses either case studies or cross-country and time-series data analyses to facilitate the discussion of various social issues and look for innovative solutions of in the real world.
SYSH 5000
Model-Based Systems Engineering
2 Credit(s)
Description
Model-based systems engineering (MBSE) is a contemporary systems engineering methodology that uses conceptual models for communication between system architects, designers, developers, and stakeholders. Object-Process Methodology (OPM) is an MBSE language and methodology for constructing domain-independent conceptual models of all kinds of systems. The course provides students with basic knowledge and tools for MBSE, focusing on conceptual modeling of systems, giving learners a competitive advantage over their peers.

  1. Courses on Domain Knowledge

MPhil: minimum 9 credits of coursework
PhD: minimum 15 credits of coursework

Under this requirement, each student is required to take one required course and other electives to form an individualized curriculum relevant to the cross-disciplinary thesis research. Only one Independent Study course may be used to satisfy the course requirements. To ensure that students will take appropriate courses to equip them with needed domain knowledge, each student has a Program Planning cum Thesis Supervision Committee to approve the courses to be taken soonest after program commencement and no later than the end of the first year. Depending on the approved curriculum, individual students may be required to complete additional credits beyond the minimal credit requirements.

  Required Course List

DSAA 5002
Data Mining and Knowledge Discovery in Data Science
3 Credit(s)
Description
With more and more data available, data mining and knowledge discovery has become a major field of research and applications in data science. Aimed at extracting useful and interesting knowledge from large data repositories such as databases, scientific data, social media and the Web, data mining and knowledge discovery integrates techniques from the fields of database, statistics and AI.

  Sample Elective Course List

To meet individual needs, students will be taking courses in different areas, which may include but not limited to courses and areas listed below.

DSAA 5003
Automatic Machine Learning
3 Credit(s)
DSAA 5009
Deep Learning in Data Science
3 Credit(s)
Description
In this course, theories, models, algorithms of deep learning and their application to data science will be introduced. The basics of machine learning will be reviewed at first, then some classical deep learning models will be discussed, including AlexNet, LeNet, CNN, RNN, LSTM, and Bert. In addition, some advanced deep learning techniques will also be studied, such as reinforcement learning, transfer learning and graph neural networks. Finally, end-to-end solutions to apply these techniques in data science applications will be discussed, including data preparation, data enhancement, data sampling and optimizing training and inference processes.
DSAA 5012
Advanced Database Management for Data Science
3 Credit(s)
Description
In this course, the concepts and implementation schemes in advanced database management systems for data science applications will be introduced, such as disk and memory management, advanced access methods, implementation of relational operators, query processing and optimization, transactions and concurrency control. It also introduces emerging database related techniques for data science.
DSAA 5013
Advanced Machine Learning
3 Credit(s)
Description
In this course, advanced algorithms for data science will be introduced. It covers most of the classical advanced topics in algorithm design, as well as some recent algorithmic developments, in particular algorithms for data science and analytics.
DSAA 5015
Parallel Programming for Data Science and Analytics
3 Credit(s)
Description
Introduction to parallel computer architectures; principles of parallel algorithm design; shared-memory programming models; message passing programming models used for cluster computing; data-parallel programming models for GPUs; case studies of parallel algorithms, systems, and applications; hands-on experience with writing parallel programs for data science and analytics.
DSAA 5020
Foundation of Data Science and Analytics
3 Credit(s)
Description
This course will introduce fundamentals techniques for data science and analytics. Specifically, it will teach students how to clean the data, how to integrate data and how to store the data. On top of these, it will also teach students knowledge to conduct data analysis, such as Bayes rule and connection to inference, linear approximation and its polynomial and high dimensional extensions, principal component analysis and dimension reduction. In addition, it will also cover advanced data analytics topics including data governance, data explanation, data privacy and data fairness.
DSAA 5021
Data Science Computing
3 Credit(s)
Description
This course will teach students data science computing techniques. Topics cover: (1) Basic concepts of Data Science Computing and Cloud; (2) MapReduce - the de facto datacenter-scale programming abstraction - and its open source implementation of Hadoop; and (3) Apache Spark - a new generation parallel processing framework - and its infrastructure, programming model, cluster deployment, tuning and debugging, as well as a number of specialized data processing systems built on top of Spark.
DSAA 5022
Data Analysis and Privacy Protection in Blockchain
3 Credit(s)
Description
This course introduces basic concepts and technologies of blockchain, such as the hash function and digital signature, as well as data analysis and privacy protection over blockchain applications. The students will learn the consensus protocols and algorithms, the incentives and politics of the block chain community, the mechanics of Bitcoin and Bitcoin mining, data analysis techniques over blockchain and user/transaction privacy protection.
DSAA 5024
Data Exploration and Visualization
3 Credit(s)
Description
This course covers essential techniques for data exploration and visualization. Students will learn the iterative process of data preprocessing techniques for getting data into a usable format, exploratory data analysis (EDA) techniques for formulating suitable hypotheses and validating them, and specific techniques for domain-related data exploration and visualization such as high-dimensional, hierarchical, and geospatial data. The course uses programing languages such as python and tools like Tableau.
DSAA 5027
Spatio-Temporal Data Analysis
3 Credit(s)
DSAA 5037
Introduction to Graph Learning
3 Credit(s)
DSAA 6000
Special Topics
3 Credit(s)
Description
The special topics course is designed for faculty to offer a course about popular research topics. The research topics in data science and analytics change and evolve very fast. The special topics will help students to know the research trend in this area and master the state-of-the-art solutions. Students will not only learn from the lectures, but also investigate the techniques by reading papers, giving presentations and working on projects.
DSAA 6018
Independent Study
1-3 Credit(s)
Description
In this course, an independent research project will be carried out under the supervision of a faculty member.
AIAA 5026
Computer Vision and Its Applications
3 Credit(s)
Description
This course covers popular topics in computer vision, which includes high-level tasks like image classification, object detection, image segmentation, and low-level tasks like image generation, image enhancement, image-to-image translation, etc.
IOTA 5501
Convex and Nonconvex Optimization I
3 Credit(s)
Description
This course covers fundamental theory, algorithms, and applications for convex and nonconvex optimization, including: 1) Theory: convex sets, convex functions, optimization problems and optimality conditions, convex optimization problems, geometric programming, duality, Lagrange multiplier theory; 2) Algorithms: disciplined convex programming, numerical linear algebra, unconstrained minimization, minimization over a convex set, equality constrained minimization, inequality constrained minimization; 3) Applications: approximation (regression), statistical estimation, geometric problems, classification, etc.

  1. Additional Foundation Courses

Individual students may be required to take foundation courses to strengthen their academic background and research capacity in related areas, which will be specified by the Program Planning cum Thesis Supervision Committee. The credits earned cannot be counted toward the credit requirements.

  1. Graduate Teaching Assistant Training
PDEV 6800
Introduction to Teaching and Learning in Higher Education
0 Credit(s)
Description
The course is designed to strengthen students’ competence in teaching. It comprises 2 parts: Part 1 aims to equip all full-time research postgraduate (RPg) students with basic teaching skills before assuming teaching assistant duties for the department. Good teaching skills can be acquired through learning and practice. This 10-hour mandatory training course provides all graduate teaching assistants (GTA) with the necessary theoretical knowledge with practical opportunities to apply and build up their knowledge, skills and confidence in taking up their teaching duties. At the end of the course, GTAs should be able to (1) facilitate teaching in tutorials and laboratory settings; (2) provide meaningful feedback to their students; and (3) design an active learning environment to engage their students. In Part 2, students are required to perform instructional delivery assigned by their respective departments to complete this course. MPhil students are required to give at least one 30-minute session of instructional delivery in front of a group of students for one term. PhD students are required to give at least one such session each in two different terms. Graded PP, P or F.

All full-time RPg students are required to complete PDEV 6800. The course is composed of a 10-hour training offered by the Institute of Educational Innovation and Practice (IEIP), and session(s) of instructional delivery to be assigned by the respective Thrusts/Base. Upon satisfactory completion of the training conducted by IEIP, MPhil students are required to give at least one 30-minute session of instructional delivery in front of a group of students for one term. PhD students are required to give at least one such session each in two different terms. The instructional delivery will be formally assessed.

  1. Professional Development Course Requirement
PDEV 6770
Professional Development for Research Postgraduate Students
1 Credit(s)
Description
This course aims at equipping research postgraduate students with transferrable skills conducive to their professional development. Students are required to attend 3 hours of mandatory training on Professional Conduct, and complete 12 hours of workshops, at their own choice, under the themes of Communication Skills, Research Competency, Entrepreneurship, Self‐Management, and Career Development. Graded PP, P or F.

Students are required to complete PDEV 6770. The 1 credit earned from PDEV 6770 cannot be counted toward the credit requirements.

PhD students who are HKUST MPhil graduates and have completed PDEV 6770 or other professional development courses offered by the University before may be exempted from taking PDEV 6770, subject to prior approval of the Program Planning cum Thesis Supervision Committee.

INFH 6780
Career Development for Information Hub Students
1 Credit(s)
Description
This course aims at equipping RPg students of the Information Hub with the skills conducive to their professional career development. Students are required to attend the 3 hours' training focusing on personality self-exploration and discipline-specific training at the thrust level, and another 10 hours' training at the hub level, at their own choice. Graded PP, P or F.

Students are required to complete INFH 6780. The 1 credit earned from INFH 6780 cannot be counted toward the credit requirements.

PhD students who are HKUST MPhil graduates and have completed INFH 6780 or other professional development courses offered by the University before may be exempted from taking INFH 6780, subject to prior approval of the Program Planning cum Thesis Supervision Committee.

  1. English Language Requirement
LANG 5000
Foundation in Listening & Speaking for Postgraduate Students
1 Credit(s)
Description
For students whose level of spoken English is lower than ELPA Level 4 (Speaking) when they enter the University. The course addresses the immediate linguistic needs of research postgraduate students for oral communication on campus using English. To complete the course, students are required to attain at least ELPA Level 4 (Speaking). Graded P or F.

Full-time RPg students are required to take an English Language Proficiency Assessment (ELPA) Speaking Test administered by the Division of Language Education before the start of their first term of study. Students whose ELPA Speaking Test score is below Level 4, or who failed to take the test in their first term of study, are required to take LANG 5000 until they pass the course by attaining at least Level 4 in the ELPA Speaking Test before graduation. The 1 credit earned from LANG 5000 cannot be counted toward the credit requirements.

DLED 5001
Communicating Research in English
1 Credit(s)

 Students are required to take DLED 5001. The credit earned cannot be counted toward the credit requirements. Students can be exempted from taking this course with the approval of the       Program Planning cum Thesis Supervision Committee.

  1. Postgraduate Seminar
DSAA 6101
Data Science and Analytics Program Seminar I
0 Credit(s)
Description
In this course, students are required to attend at least 6 seminars offered by the program. The program will offer at least 10 seminars related to the state of the art research on data science and analytics in each term. These seminars will help students to broaden the horizons of their knowledge on data science and analytics. Graded P or F.
DSAA 6102
Data Science and Analytics Program Seminar II
1 Credit(s)
Description
In this course, students are required to attend at least 6 seminars offered by the program. The program will offer at least 10 seminars related to the state-of-the-art research on data science and analytics in each term. These seminars will help students to broaden the horizons of their knowledge on data science and analytics. Graded P or F.

Students are required to complete DSAA 6101 and DSAA 6102 in two terms. The credit earned cannot be counted toward the credit requirements.

  1. PhD Qualifying Examination

PhD students are required to pass a qualifying examination to obtain PhD candidacy following established policy.

  1. Thesis Research
DSAA 6990
MPhil Thesis Research
0 Credit(s)
Description
Master's thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.
DSAA 7990
Doctoral Thesis Research
0 Credit(s)
Description
Original and independent doctoral thesis research supervised by co-advisors from different disciplines. A successful defense of the thesis leads to the grade Pass. No course credit is assigned.

  MPhil:

  1. Registration in DSAA 6990; and
  2. Presentation and oral defense of the MPhil thesis.

PhD:

  1. Registration in DSAA 7990; and
  2. Presentation and oral defense of the PhD thesis.

Last Update: 20 July 2023

ADMISSION REQUIREMENTS

To qualify for admission, applicants must meet all of the following requirements. Admission is selective and meeting these minimum requirements does not guarantee admission.

1. General Admission Requirements of the University

Please refer to Admission Requirements.

2. English Language Admission Requirements

Please refer to Admission Requirements.

APPLICATION

Admission to HKUST(GZ)

Apply online before the application deadlines.

Application Fee

RMB150

* All international students are required to obtain a student visa (X visa) for studying in China’s mainland. For details on student visa (X visa) requirements, please click here.

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