COURSE INFO
DESCRIPTIONData science changes the way people process data in different areas. It has promoted the development of many subjects. This course introduces beginners to the whole lifecycle of data science problems and solutions. The course will help students comprehensively understand the basic knowledge of data science and use computer techniques to handle the real-life data science problems. Topics covered include: data collection and processing, computer-oriented modelling techniques, relationship with other mathematics, and case studies.

DSAA 1001 - Introduction to Data Science and Analytics (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6251) TuTh 04:30PM - 05:50PMRm 101, E1LI, Jia
LI, Lei
WANG, Wei		800800 
LA01 (6252) Fr 12:00PM - 12:50PMRm 101, E1LI, Jia
LI, Lei
WANG, Wei		800800 
[Matching between Lecture & Lab required]
COURSE INFO
PRE-REQUISITE(DSAA 1001 or AIAA 2205) OR (FTEC 3130 for FTEC Major Only)
DESCRIPTIONMachine learning is an exciting and fast-growing field that leverages data to build models which can make predictions or decisions. This is an introductory machine learning course that covers fundamental topics in model assessment and selection, supervised learning (e.g. linear regression, logistic regression, neural networks, deep learning, support vector machines, Bayes classifiers, decision trees, ensemble methods); unsupervised learning (e.g. clustering, dimensionality reduction); and reinforcement learning. Students will also gain practical programming skills in machine learning to tackle real-world problems.

DSAA 2011 - Machine Learning (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6255) Tu 03:00PM - 05:50PMRm 134, E1YANG, Weikai
ZHONG, Zixin		600600 
L02 (6256) Fr 12:00PM - 02:50PMRm 134, E1YANG, Weikai
ZHONG, Zixin		600600 
LA01 (6258) Th 04:30PM - 05:20PMRm 134, E1YANG, Weikai
ZHONG, Zixin		600600 
LA02 (6260) Fr 08:00PM - 08:50PMRm 134, E1YANG, Weikai
ZHONG, Zixin		600600 
COURSE INFO
PRE-REQUISITEDSAA 2043
DESCRIPTIONTopics include: principles of database systems; conceptual modeling and data models; logical and physical database design; query languages and query processing; database services including concurrency, crash recovery, security and integrity. Hands-on DBMS experience.

DSAA 2031 - Database Management Systems (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6264) TuTh 10:30AM - 11:50AMRm 134, E1TANG, Nan600600 
LA01 (6265) Fr 10:30AM - 11:20AMRm 149, E1TANG, Nan600600 
[Matching between Lecture & Lab required]
COURSE INFO
PRE-REQUISITEUFUG 2601 OR UFUG 2602
DESCRIPTIONThis course introduces core data structure and algorithms, which are fundamental to data science and analytics. As an important course that bridges students to a number of advanced courses, it covers topics in asymptotic complexity analysis, typical data structures (stacks, queues, trees, and graphs), sorting, searching, data structure-specific algorithms, algorithmic strategies (e.g., divide-and-conquer, greedy, and dynamic programming), analysis and measurement of programs.

DSAA 2043 - Design and Analysis of Algorithms (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6268) TuTh 10:30AM - 11:50AMRm 222, W1WANG, Wei400400 
L02 (6269) MoWe 09:00AM - 10:20AMRm 102, W1ZHANG, Yanlin400400 
LA01 (6270) Fr 10:30AM - 11:20AMRm 228, E1WANG, Wei400400 
LA02 (6271) Fr 02:00PM - 02:50PMRm 228, E1ZHANG, Yanlin400400 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONIn 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 5009 - Deep Learning in Data Science (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6056) Tu 01:30PM - 04:20PMRm 101, W1WANG, Wenjia12001200 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONIn 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 5012 - Advanced Database Management for Data Science (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6058) Fr 01:30PM - 04:20PMRm 122, E1LI, Lei500500 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONIn 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 5013 - Advanced Machine Learning (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6060) Fr 09:00AM - 11:50AMRm 102, W4CHU, Xiaowen
ZHONG, Zixin		600600 
COURSE INFO
VECTOR[3-0-0:3]
PREVIOUS CODEDSAA 6000B
DESCRIPTIONGraph, as a very expressive model, has been widely used to model real-world entities and their relationships in application-specific networks. In this course, students will gain a thorough introduction to the basics of graph theories, as well as cutting-edge research in deep learning for graphs. The topics include graph embeddings, graph neural networks, graph clustering models, graph generative models, adversarial attacks on graphs, graph reasoning, etc.

DSAA 5037 - Introduction to Graph Learning (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6062) Mo 03:00PM - 05:50PMRm 228, E2ZHANG, Yongqi600600 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONTables are a crucial component of data management and analysis, yet their potential is often underutilized. This project-driven course addresses this gap by equipping students with both theoretical knowledge and practical skills in table representation learning. The course is divided into four parts: (1) Theory: Core concepts of table representation learning, including tabular data analysis, representation learning, multimodal integration (with code and text), and retrieval-augmented generation (RAG). (2) Tools and Techniques: Exploration of advanced tools such as deep learning models, large language models (LLMs), multimodal LLMs, and RAG methods, with a focus on pre-training paradigms for table-related tasks. (3) Table Analysis Applications: Practical applications, including Natural language to SQL (NL2SQL), Table Question Answering (TableQA), Table Visualization, and Data storytelling, demonstrating the use of table representation learning in real-world scenarios. (4) Course Project: A solo project where students apply the concepts and tools learned to address a real-world problem related to table representation learning.

DSAA 6000O - Table Representation Learning (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6074) Tu 09:00AM - 11:50AMRm 202, E3LUO, Yuyu400400 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONGame theory offers powerful insights into strategic decision-making, with promising applications in fields such as human-AI interaction, multi-agent systems, adversarial machine learning, and social networks. This course introduces the core concepts of game theory, focusing on the study of interactions among rational decision-makers. Students will explore foundational topics such as utility, Nash equilibrium, dominant strategies, mixed strategies, and both static and dynamic games. Through practical examples, the course will demonstrate how game theory is applied to optimize strategies in artificial intelligence, network systems, economics, and our daily life. By the end of the course, students will be equipped to analyze and model interactions, making it a vital tool for modern problem-solving in AI, data science, and beyond.

DSAA 6000P - Game Theory (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6075) Tu 04:30PM - 07:20PMRm 201, E1DING, Ningning300300 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis course delves into the foundational theories and cutting-edge advancements in large language models (LLMs). The course emphasizes LLM's application in real-world scenarios, focusing on topics such as domain-specific LLMs (e.g., continuous pretraining, SFT, and RLHF), data-intensive and document-intensive scenarios (e.g., NL2SQL RAG). Active student participation is encouraged through mini-projects, presentations, and discussions, providing hands-on experience in leveraging LLMs for real-world challenges.

DSAA 6000Q - Large Language Models: Theory and Practice (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6077) Th 09:00AM - 11:50AMRm 228, E2WEI, Jiaheng60
Quota/Enrol/Avail
For MPhil (DSA) Students only: 20/0/20
For MSc(DCAI) Students Only: 30/0/30
For PhD (DSA) students only: 10/0/10
0600 
COURSE INFO
VECTOR[1 credit]
DESCRIPTIONThis course offers students opportunities to learn the possible industry topics and supervisors that they will work with during their internship. The goals are: (a) understand (i) problems in industry, (ii) existing data-sets, (iii) AI models, (iv) service scenario and KPI, (v) challenges; (b) propose industrial projects based on the understanding; and (c) matching for their industrial projects. This course is only available for MSc(DCAI) students. Graded P or F.

DSAA 6010 - Industry Round Table (1 unit)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6063) Tu 03:00PM - 04:50PMRm 122, E1TBA600600 
COURSE INFO
VECTOR[1-3 credit(s)]
DESCRIPTIONIn this course, an independent research project will be carried out under the supervision of a faculty member.

DSAA 6018A - Independent Study (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
R01 (6514) TBATBATBA200200
Instructor Consent Required
 
COURSE INFO
VECTOR[1-0-0:1]
DESCRIPTIONIn 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 unit)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6065) We 09:30AM - 10:20AMRm 102, E4TANG, Nan12001200 
COURSE INFO
VECTOR[6 credits]
DESCRIPTIONIn this course, the student will work on a practical project. The independent project is related to real problems existing in data science and AI application domains. The student needs to conduct literature survey, method comparison, solution selection and implementation, experimental study and write the final report. The course will train students skills on proposing end-to-end solution for a realapplications problem. This course is only available for MSc(DCAI) students. Graded P or F.

DSAA 6800 - Independent Project (6 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
R01 (6067) TBANo room requiredTBA600600 
COURSE INFO
VECTOR[3 credits]
PRE-REQUISITEDSAA 6920
DESCRIPTIONIn this course, students will be trained in the industry. They will work under the guidance of their supervisors (industry and academia) to practice what they have learned in the program, and apply the data science and AI knowledge and techniques to various real-life problems. In Part II, students are required to complete an Intermediate report with oral examination on the student’s progress and industry collaboration progress. This course is only available for MSc(DCAI) students. Graded PP, P or F.

DSAA 6921 - Industry Internship Ⅱ (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
R01 (6068) TBANo room requiredTBA600600 
COURSE INFO
VECTOR[3 credits]
PRE-REQUISITEDSAA 6920 and DSAA 6921
DESCRIPTIONIn this course, students will be trained in the industry. They will work under the guidance of their supervisors (industry and academia) to practice what they have learned in the program, and apply the data science and AI knowledge and techniques to various real-life problems. In Part III, students are required to complete a Final report with oral examination on the final output from the internship project and whether the student indeed knows how to apply AI techniques to concrete data science applications. This course is only available for MSc(DCAI) students. Graded PP, P or F.

DSAA 6922 - Industry Internship Ⅲ (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
R01 (6070) TBANo room requiredTBA600600 
COURSE INFO
DESCRIPTIONMaster'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 6990 - MPhil Thesis Research (0 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
R01 (6071) TBANo room requiredTBA10059410 
COURSE INFO
DESCRIPTIONOriginal 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.

DSAA 7990 - Doctoral Thesis Research (0 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
R01 (6073) TBANo room requiredTBA10018820