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, machine learning (including classification and clustering), statistical estimation and inference methods, and case studies.

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

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6372) MoWe 04:30PM - 05:50PMRm 102, W4LI, Lei
WANG, Wei
ZHANG, Yongqi		7538370 
LA01 (6373) Fr 04:30PM - 05:20PMRm 101, E1LI, Lei
WANG, Wei
ZHANG, Yongqi		7538370 
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. Basic knowledge on mathematics (e.g., basic of probability theory, linear algebra, calculus and optimization), programming (e.g., Python / C++ / Matlab) and data science are essential and will benefit the study of this course.

DSAA 2011 - Machine Learning (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6374) Mo 06:00PM - 08:50PMRm 102, E4ZHONG, Zixin7538370 
LA01 (6375) Th 07:30PM - 08:20PMRm 101, W1ZHONG, Zixin7538370 
COURSE INFO
PRE-REQUISITEDSAA 2011 or AIAA 3111
DESCRIPTIONThis course provides students with an extensive exposure to deep learning. Topics include shallow and deep neural networks, activation functions and rectified linear unit, construction of deep neural networks and matrix representations including deep convolutional neural networks and deep recursive neural networks, computational issues including backpropagation, automatic differentiation, stochastic gradient descent, complexity analysis, approximation analysis including universality of approximation, design of deep neural network architectures and programming according to various applications.

DSAA 2012 - Deep Learning (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6376) MoWe 04:30PM - 05:50PMRm 101, E4GUO, Zhijiang484620 
LA01 (6377) Fr 04:30PM - 05:20PMRm 101, E4GUO, Zhijiang484620 
[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 (6378) MoWe 09:00AM - 10:20AMRm 101, E1ZHANG, Yanlin484260 
L02 (6380) TuTh 04:30PM - 05:50PMRm 102, W4LU, Shangqi4814340 
LA01 (6379) Fr 12:00PM - 12:50PMRm 227, E1ZHANG, Yanlin484260 
LA02 (6381) Fr 04:30PM - 05:20PMRm 227, E1LU, Shangqi4814340 
COURSE INFO
PRE-REQUISITEDSAA 1001 OR AIAA 2205
DESCRIPTIONIn this course, students will work in teams to design, implement, and deliver a software system that addresses a real-world data science problem. Projects will be sourced from domains such as finance, healthcare, transportation, and manufacturing. Each team will apply data science tools and techniques to develop functional software solutions, integrating data-driven models into real applications. The course also introduces agile project management, teamwork, and communication skills. Students will work from requirements through to implementation and delivery, following a structured process with a focus on collaboration, iteration, and clear reporting.

DSAA 2044 - Data Science Project (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6382) TuTh 01:30PM - 02:50PMRm 122, E1DING, Zishuo6019410 
LA01 (6383) Fr 01:30PM - 02:20PMRm 122, E1DING, Zishuo6019410 
COURSE INFO
PRE-REQUISITE(DSAA 2011 OR DSAA 2012 OR AIAA 3111) AND UFUG 2103 AND UFUG 2104
DESCRIPTIONTopics include: advanced linear algebra, advanced geometry (e.g., manifold), vector calculus, advanced mathematics models (regression, latent-variable model) etc.. Also includes topics related to discrete mathematics (e.g., enumeration techniques, basic number theory, logic and proofs, recursion and recurrences, probability theory and graph theory. The approach of this course is specifically computer science application oriented.)

DSAA 2088 - Mathematics for Data Science (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6384) MoWe 10:30AM - 11:50AMRm 148, E1DING, Ningning
WANG, Wenjia		6010500 
T01 (6385) Th 12:30PM - 01:20PMRm 147, E1DING, Ningning
WANG, Wenjia		6010500 
COURSE INFO
PRE-REQUISITEDSAA 2031 AND UFUG 2601
DESCRIPTIONThis course introduces the fundamentals of high-performance and parallel computing. It targets at scientists, engineers, scholars, and everyone seeking to develop the software skills necessary for work in parallel software environments. These skills include big-data analysis, machine learning, parallel programming, and optimization. The course covers the basics of Linux environments and bash scripting all the way to high throughput computing and parallelizing code.

DSAA 3032 - Introduction to High-Performance and Parallel Computing (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6413) MoWe 12:00PM - 01:20PMRm 148, E1LUO, Qiong4810380 
LA01 (6414) Fr 10:30AM - 11:20AMRm 227, E1LUO, Qiong4810380 
COURSE INFO
PRE-REQUISITEDSAA 2012
EXCLUSIONAIAA 4051
DESCRIPTIONThis course provides a comprehensive overview of modern approaches for (1) natural language processing and (2) knowledge graphs. Students will explore text processing, linguistic preprocessing, and foundational language models, progressing from ngrams to word embeddings and neural language models. The curriculum covers semantic and discourse analysis, information extraction, and the integration of knowledge bases. Key topics include knowledge-intensive tasks, and the construction and application of knowledge graphs, along with ontologies and semantic web technologies like RDF, OWL, and SPARQL. The course also delves into large language models, emphasizing transformer architectures and ethical considerations of NLP and knowledge systems such as bias, fairness, and privacy. Through lectures, discussions, and practical exercises, students will gain critical insights into the development, maintenance, and application of NLP and knowledge systems, preparing them to address real-world challenges in these fields.

DSAA 3051 - Introduction to Natural Language Processing and Knowledge Engineering (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6415) TuTh 06:00PM - 07:20PMRm 101, W1WEI, Jiaheng6011490 
T01 (6416) Fr 03:00PM - 03:50PMRm 122, E1WEI, Jiaheng6011490 
COURSE INFO
PRE-REQUISITEDSAA 2012 OR AIAA 3111
DESCRIPTIONThis course provides hands-on experience with technology-based productivity tools as well as the foundational knowledge to help students to understand system design and development. The course is designed to integrate concepts of hardware, software, and the Internet. This course will include the following topics: data privacy and security landscape, managing data privacy, k-anonymity, l-diversity, k-l diversity and differential privacy, security risk exposure, AI privacy/security, content safety, and deepfake detection. Students will investigate technology career paths and some of the various certifications available in the industry.

DSAA 3055 - Data Privacy and Security (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6417) TBATBATBA600600 
LA01 (6418) TBATBATBA600600 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONWith 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.

DSAA 5002 - Data Mining and Knowledge Discovery in Data Science (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6136) Mo 03:00PM - 05:50PMRm 102, E4LI, Jia1009910 
L02 (6685) Mo 03:00PM - 05:50PMLecture Hall CYU, JEFFREY XU1009460 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONA recent trend in Data Science and Machine Learning communities is to further boost the accessibility of Machine Learning techniques by reducing the tedious effort on learning model selection, hyper-parameter tuning, etc. Automatic Machine Learning (AutoML) aims to reduce this tedious effort and make Machine Learning easier to use. In this course, students will master the basics of AutoML, and understand key techniques including hyper-parameter optimization, feature engineering and meta-learning. This course also introduces common AutoML systems and covers real-world case studies on the applications of AutoML.

DSAA 5003 - Automatic Machine Learning (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6138) Th 09:00AM - 11:50AMRm 102, W4WEN, Zeyi969600 
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 (6139) Fr 01:30PM - 04:20PMRm 102, W4LI, Lei9654420 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis 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 5020 - Foundation of Data Science and Analytics (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6141) Tu 01:30PM - 04:20PMLecture Hall BTANG, Jing15014190 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis 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 5024 - Data Exploration and Visualization (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6143) Th 03:00PM - 05:50PMRm 102, E4ZENG, Wei6050100 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONGenetics and genomics are closely related fields, both analyzing massive biological data. Genetics studies specific genes and their roles in heredity, while genomics encompasses the entire genome of an organism to understand its complex systems. This course aims to introduce traditional computational biology and revolutionary advancements in machine learning that are transforming the fields of genetics and genomics. It begins with a very brief overview of conventional algorithms in genetics and genomics, setting the stage for a deeper exploration of how genetics and genomics have evolved with the advent of machine learning techniques. As the course progresses, students will be introduced to the latest machine learning methods, with an emphasis on deep learning, which has dramatically changed the landscape of genomics research. Through a combination of theoretical instruction and hands-on teamwork projects, this course is designed to equip students with the necessary skills to navigate and contribute to the field of genomics, leveraging machine learning to uncover new insights into genetic data and its implications for human health and disease.

DSAA 6000M - Machine Learning in Genetics and Genomics (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6147) Fr 09:00AM - 11:50AMRm 101, E1ZHANG, Yanlin3015150 
COURSE INFO
VECTOR[3-0-0:3]
DESCRIPTIONThis course explores the dynamic relationship between database technologies and artificial intelligence, providing students with advanced knowledge and practical skills in this rapidly evolving field. Students will learn foundational concepts in data intelligence, AI-driven database optimization, intelligent querying methods such as Natural Language to SQL, and retrieval-augmented generation (RAG) techniques for data applications. Through practical exercises and real-world case studies, students will understand how AI improves data management, analytics, visualization, and decision-making processes. The course includes a final project in which students apply AI methods to solve practical database challenges, preparing them to innovate effectively in data intelligence.

DSAA 6000R - Data Intelligence: When Databases Meet AI (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6149) Mo 06:00PM - 08:50PMLecture Hall BLUO, Yuyu10080200 
COURSE INFO
VECTOR[1 credit]
DESCRIPTIONIn this course, an independent research project will be carried out under the supervision of a faculty member.

DSAA 6018B - Independent Study (1 unit)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
R01 (6699) TBATBATBA201280
Instructor Consent Required
 
COURSE INFO
VECTOR[1-0-6:3]
DESCRIPTIONThis course will teach students practical programming and parallel processing skills on implementing various deep learning or machine learning models, starting from preparing data, feature selection to model choosing, hyperparameter tuning, and final result analysis and explaining. This course is only available for MSc(DCAI) students.

DSAA 6100 - Practical Lab Course (3 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6150) Mo 09:00AM - 10:20AMRm 101, W1LUO, Yuyu1009550 
LA01 (6151) MoWe 10:30AM - 01:20PMRm 101, W1LUO, Yuyu1009550 
MoWe 10:30AM - 01:20PMRm 227, E1LUO, Yuyu
COURSE INFO
VECTOR[0 credit]
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 6101 - Data Science and Analytics Program Seminar I (0 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
L01 (6144) Tu 11:00AM - 11:50AMLecture Hall CTANG, Nan12011550 
COURSE INFO
VECTOR[2 credits]
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 I, students are required to complete an Open Topic report with oral examination on the scientific value, feasibility and technical challenges of the proposed topic. This course is only available for MSc(DCAI) students. Graded PP, P or F.

DSAA 6920 - Industry Internship Ⅰ (2 units)

SectionDate & TimeRoomInstructorQuotaEnrolAvailWaitRemarks
R01 (6152) TBANo room requiredTBA504370 
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 (6040) TBANo room requiredTBA10067330 
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 (6041) TBANo room requiredTBA10040600