Course Information
Registration period
8 Apr 2022 (Fri) - 8 Apr 2026 (Wed)
Price
-
Study Mode
Duration
4 Year(s)
Language
English
Location
-
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Course Overview
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.
What You’ll Learn
Minimum Credit Requirement
MPhil: 15 credits
PhD: 21 credits
Credit Transfer
Students who have taken equivalent courses at HKUST 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.
Cross-disciplinary Core Courses
2 credits
IIMP 6010Cross-disciplinary Research Methods I2 Credit(s)
IIMP 6020Cross-disciplinary Research Methods II2 Credit(s)
IIMP 6030Cross-disciplinary Design Thinking I2 Credit(s)
IIMP 6040Cross-disciplinary Design Thinking II2 Credit(s)
All students are required to complete either IIMP 6010 or IIMP 6030. Students may complete the remaining courses as part of the credit requirements, as requested by the Program Planning Committee.
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 5000Information Science and Technology: Essentials and Trends2 Credit(s)
Other Hub Core Courses
FUNH 5000Introduction to Function Hub for Sustainable Future2 Credit(s)
SOCH 5000Technological Innovation and Social Entrepreneurship2 Credit(s)
SYSH 5000Model-Based Systems Engineering2 Credit(s)
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 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
MSBD 5002Data Mining and Knowledge Discovery3 Credit(s)
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 5009Deep Learning in Data Science3 Credit(s)
DSAA 5012Advanced Database Management for Data Science3 Credit(s)
DSAA 5013Advanced Machine Learning3 Credit(s)
DSAA 5020Foundation of Data Science and Analytics3 Credit(s)
DSAA 5021Data Science Computing3 Credit(s)
DSAA 5022Data Analysis and Privacy Protection on BlockChain3 Credit(s)
DSAA 5023Industrial Analytics3 Credit(s)
DSAA 5024Data Exploration and Visualization3 Credit(s)
DSAA 5025Experimental Design and Causal Inference3 Credit(s)
DSAA 5026Functional Data Analysis3 Credit(s)
DSAA 5027Spatio-Temporal Data Analysis3 Credit(s)
DSAA 5028Combination Optimization with Machine Learning3 Credit(s)
DSAA 6018Independent Study3 Credit(s)
COMP 5112Parallel Programming3 Credit(s)
IEDA 5230Deterministic Models in Operations Research3 Credit(s)
IEDA 5250Stochastic Models in Operations Research3 Credit(s)
IEDA 5270Engineering Statistics and Data Analytics3 Credit(s)
ISOM 5630Business Analytics in R2 Credit(s)
MATH 5470Statistical Machine Learning3 Credit(s)
MSBD 5003Big Data Computing3 Credit(s)
MSBD 5007Optimization and Matrix Computation3 Credit(s)
MSBD 5008Introduction to Social Computing3 Credit(s)
MSBD 5009Parallel Programming3 Credit(s)
MSBD 5011Advanced Statistics: Theory and Applications3 Credit(s)
MSBD 5012Machine Learning3 Credit(s)
MSBD 6000Special Topics3 Credit(s)
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 Committee. The credits earned cannot be counted toward the credit requirements.
Graduate Teaching Assistant Training
PDEV 6800Introduction to Teaching and Learning in Higher Education0 Credit(s)
All full-time RPg students are required to complete PDEV 6800. The course is composed of a 10-hour training offered by the Center for Education Innovation (CEI), and session(s) of instructional delivery to be assigned by the respective departments. Upon satisfactory completion of the training conducted by CEI, 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.
Professional Development Course Requirement
PDEV 6770Professional Development for Research Postgraduate Students1 Credit(s)
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 Committee.
INFH 6780Career Development for Information Hub Students1 Credit(s)
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 Committee.
English Language Requirement
LANG 5000Foundation in Listening & Speaking for Postgraduate Students1 Credit(s)
Full-time RPg students are required to take an English Language Proficiency Assessment (ELPA) Speaking Test administered by the Center for 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.
LANG 5001Postgraduate English for Engineering Research Studies1 Credit(s)
LANG 5002Postgraduate English for Business and Social Science Studies1 Credit(s)
LANG 5010Postgraduate English for Science Studies1 Credit(s)
Students are required to take one of the above three courses. 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 Committee.
Postgraduate Seminar
DSAA 6101Data Science and Analytics Program Seminar I0 Credit(s)
DSAA 6102Data Science and Analytics Program Seminar II1 Credit(s)
Students are required to complete DSAA 6101 and DSAA 6102 in two terms. The credit earned cannot be counted toward the credit requirements.
PhD Qualifying Examination
PhD students are required to pass a qualifying examination to obtain PhD candidacy following established policy.
Thesis Research
DSAA 6990MPhil Thesis Research0 Credit(s)
DSAA 7990Doctoral Thesis Research0 Credit(s)
MPhil:
Registration in DSAA 6990; and
Presentation and oral defense of the MPhil thesis.
PhD:
Registration in DSAA 7990; and
Presentation and oral defense of the PhD thesis.
