Course List
DS4013 Data Mining (For DS students)
数据挖掘
This course introduces latest development of knowledge discovery and data mining concepts and emphasizes on data mining techniques, including data pre-processing, classification, clustering, data association and data warehouse. It can motivate students to analyse big data with modern software.
Check DetailsDS4023 Machine Learning
机器学习
The course will provide an introduction to Machine Learning and its core models and algorithms. The aim of the course is to give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work.
Check DetailsDS4004 Final Year Project I (DS)
毕业论文 I
This course will enable students to demonstrate an integrated understanding of Data Science principles and techniques and gain practical experience of developing and applying enabling technologies. Students will undertake an individual project under the supervision of a faculty member and gain the practical experience of applying computer systems principles and techniques acquired from the course to the solution of real-life problems. The project demands careful planning and creative application of underlying theories and enabling technologies. A thesis and an oral presentation are required upon successful completion of the project.
Check DetailsSTAT2003 Advanced Statistics
高等统计学
This course introduces the basic probability theory and theoretical statistics (probability distributions, estimation and hypothesis test criteria, etc.) so that the students can understand the foundations of general statistical practices and are also well prepared for the advanced subjects like regression analysis, multivariate analysis, and time series forecasting.
Check DetailsSTAT2013 Regression Analysis
回归分析
This course introduces the theory of regression analysis and techniques in data analysis. It will emphasise on recent developments in the regression analysis such as statistical diagnostics and nonlinear regression; and to motivate students to analyse multivariate data with the help of statistical packages such as MATLAB, R or SPSS.
Check DetailsCOMP4123 Information Retrieval and Search Engine
信息检索及搜索引擎
This course introduces the basic principles of information retrieval and search engines. Advanced models and techniques in information processing and retrieval will be covered.
Check DetailsDS4033 Text Mining and Analytics
文本挖掘与分析
This course introduces the basic concepts, principles, and major techniques in text mining. It apprehends the value of text mining in a broad spectrum of areas, including business intelligence, information acquisition, social behaviour analysis and decision making. It will enable students to discover interesting patterns, extract useful knowledge, and support decision making, with statistical approaches applied to text data.
Check DetailsDS4043 Introduction to Statistical Computing
统计计算
The course is an introduction to statistical computing taught using R. The aim of this course is to expand students’ statistical toolbox through numerical and simulation methods. Additionally, the course will teach students how to approach statistical problems from a computational perspective. Let students become proficient in everyday computational tasks with datasets of all kinds, skilled in applications of elementary statistical methods, with an emphasis on data exploration and simple graphics.
Check DetailsDS4053 Introduction to Bioinformatics
生物信息学
The course is designed to introduce the most important and basic concepts, methods, and tools used in Bioinformatics which includes an introduction to Bioinformatics, experience with select bioinformatics tools and databases currently utilized in the life sciences.
Check DetailsDS4005 Final Year Project II (DS)
毕业论文 II
Students will undertake an individual project under the supervision of a faculty member and gain the practical experience of applying computer systems principles and techniques acquired from the course to the solution of real-life problems. The project demands careful planning and creative application of underlying theories and enabling technologies. A thesis and an oral presentation are required upon successful completion of the project. This course is the extension of the course COMP3100-Final year project I. Only those students who are competent in the FYP I will be eligible to take this course.
Check DetailsSTAT3003 Survey Sampling
抽样调查
Sample survey is a popular means for gauging opinions and views of a target population. It is widely used in many areas including behavioural sciences, biomedical sciences, social research, marketing research, financial and business services, public opinions on government policies, etc. However, improperly conducted surveys or inappropriate analyses of the results could lead to seriously wrong conclusions. This course equips students with a sound understanding of survey operations, sampling methods, questionnaire design and analysis of results.
Check DetailsGCNU1003 Speaking of Statistics
统计漫谈
This course provides students with an understanding of fundamental statistical techniques commonly used in social science, business, and science today. The emphasis is on statistical thinking, concepts and data analysis. Students are required to solve a variety of problems using statistical packages.
Check DetailsSTAT3033 Bayesian Statistics
贝叶斯统计
This course will present the relevant theory, methodology and computational techniques of modern Bayesian inference and modelling. The main emphasis of the course will be on how to use the Bayesian thinking, modelling and computation to analyse data with complex structure.
Check DetailsSTAT4013 Multivariate Analysis
多元统计分析
This course provides an understanding of classical multivariate analysis and modern techniques in data mining which are useful for analysing both designed experiments and observational studies. Real data in social, life, and natural sciences are analysed using statistical packages such as R or Matlab.
Check DetailsSTAT4043 Categorical Data Analysis
属性数据分析
To equip students with statistical methods for analysing categorical data arisen from qualitative response variables which cannot be handled by methods dealing with quantitative response, such as regression and ANOVA. Some computing software, such as SAS, S-PLUS, R or MATLAB, will be used to implement the methods. The learning outcome will be the ability to formulate suitable statistical models for qualitative response variables and to analyse such data with computer software.
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