Postgraduate Courses
DSAA
Data Science and Analytics
- DSAA 5009Deep Learning in Data Science[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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Identify basic knowledge of machine learning and deep learning.
- 2.Apply deep learning modes.
- 3.Generate data for deep learning tasks.
- 4.Design optimized training and inference process.
- DSAA 5012Advanced Database Management for Data Science[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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Identify basic knowledge of data model and relational algebra.
- 2.Identify data storage and indexing structures.
- 3.Implement Query processing techniques.
- 4.Carry out Physical database design and advanced transaction management.
- DSAA 5013Advanced Machine Learning[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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Identify basic knowledge of algorithm and its complexity analysis.
- 2.Apply advanced algorithms.
- 3.Carry out optimization by randomized and sampling algorithms.
- 4.Apply parallel and distributed algorithms.
- DSAA 5027Spatio-Temporal Data Analysis[3-0-0:3]DescriptionIn this course, we will introduce spatial and multimedia database management concepts, theories and technologies, from data representation, indexing, fundamental operations to advanced query processing. Challenges and solution for high dimensional data will also be introduced.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Master basic knowledge of Relational Data Indexing and Query Processing.
- 2.Master knowledge of Spatial Databases.
- 3.Apply Spatial Data Organisation and Indexing; Spatial Query Processing.
- 4.Managing Spatiotemporal Data, Multimedia Databases and High Dimensional Data.
- DSAA 6000Special Topics[3-0-0:3]DescriptionThe 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Gain the knowledge of current research trend in Data Science and Analytics area.
- 2.Master the state-of–the-art techniques of the chosen topic.
- 3.Apply the learned techniques to the real problem on the chosen topic.
- DSAA 6018Independent Study[3 credits]DescriptionIn this course, an independent research project will be carried out under the supervision of a faculty member.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Identify knowledge related to the proposed topic.
- DSAA 6101Data Science and Analytics Program Seminar I[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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Identify knowledge of state-of-the-art research topics and techniques on data science and analytics.
- DSAA 6102Data Science and Analytics Program Seminar II[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 6990MPhil Thesis ResearchDescriptionMaster'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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Design, develop and conduct cross-disciplinary research in Data Science and Analytics.
- 2.Communicate research findings effectively in written and oral presentations.
- DSAA 7990Doctoral Thesis ResearchDescriptionOriginal 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.Intended Learning Outcomes
On successful completion of the course, students will be able to:
- 1.Design, develop and conduct cross-disciplinary research in Data Science and Analytics.
- 2.Communicate research findings effectively in written and oral presentations.
