Pre-requisite(s): None

Course Description:
This course provides students with basic knowledge of computer-oriented problem solving methodologies, algorithm development, structured programming concepts and design techniques, and implementation tools that facilitate debugging and testing. In particular, structured programming skills will be illustrated with a contemporary programming language.

Pre-requisite(s): None

Course Description:
This course introduces the basic techniques in matrix algebra, which is the foundation for more advanced mathematics and statistics subjects. Major emphasis will be on the system of linear equations, linearly independence in finite dimensional vector spaces.

Pre-requisite(s):
MATH1053 LINEAR ALGEBRA I

Course Description:
This course introduces the basic techniques in matrix algebra, which is the foundation for more advanced mathematics and statistics subjects. Major emphasis will be on the system of linear equations, linearly independence, and eigenvalue problems in finite dimensional vector spaces. Basic ideas and techniques on calculus will be introduced.

Pre-requisite(s): None

Course Description:
This course introduces the basic ideas and techniques in single variable calculus with mathematical rigour to prepare students for more advanced mathematical and statistical subjects.

Pre-requisite(s):
MATH1073 CALCULUS I

Course Description:
This course is a continuation of Calculus I. It provides a solid foundation in multivariable calculus to prepare students for more advanced mathematics and statistical subjects.

Pre-requisite(s):
MATH1083 CALCULUS II

Course Description:
This course provides an introduction to measure theory, Lebesgue integration, L P space, and Fourier analysis. Equipped with this knowledge, students are prepared for further studies in numerical analysis, functional analysis and advanced probability theory

Pre-requisite(s):
STAT2023 ADVANCED PROBABILITY or MATH2063 PROBABILITY AND STATISTICS, and
MATH1063 LINEAR ALGEBRA II, and
MATH1083 CALCULUS II

Course Description:
This course reviews basic probability theory and deals with major stochastic processes including Poisson processes, renewal theory, Markov Chains and continuous-time Markov Chains. Applications to inventory problems, equipment replacement policy and queuing theory are also dealt with through some examples.

Pre-requisite(s):
(GCNU1003 Speaking of Statistics, or
GCNU1043 Introduction to Probability and Statistics, or
GCNU1053 Statistics for Social Science, or GCNU1063 Business Statistics, or
GFQR1001 A Journey with Data, or
GFQR1002 Data Analytics for Business, or
GFQR1003 Hands On Data Analytics for Everyone, or
GFQR1004 Statistics in Our Daily Life), and
 (MATH 1073 Calculus I, or
 MATH1123 Calculus for Science and Engineering, or
 MATH1103 Calculus)

Course Description:
This course teaches students how mathematics interfaces with other disciplines. Real-life problems are solved using models in statistics, mathematics, and physics. The case studies and problem-based approaches are adopted. Programming abilities are very crucial to this course.

Pre-requisite(s):
MATH1053 Linear Algebra I or MATH1003 Linear Algebra, and
MATH1073 Calculus I or MATH1123 Calculus For Science and Engineering

Course Description:
This course introduces the fundamental theory and techniques for both unconstrained and constrained optimization. There will be an overview of the existing numerical software packages. Finally some interdisciplinary techniques and applications related to optimization will be discussed.

Pre-requisite(s)
MATH1073 CALCULUS I, or
MATH1123 Calculus For Science and Engineering

Course Description:
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.

Pre-requisite(s):
MATH1053 LINEAR ALGEBRA I & MATH1063 LINEAR ALGEBRA II, or
MATH1003 LINEAR ALGEBRA

Course Description:
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.

Pre-requisite(s):
MATH1063 LINEAR ALGEBRA II, and
MATH1083 CALCULUS II

Course Description:
The course introduces basic concepts and techniques of measuring theoretic probability, familiarise students with random variable and various probability distributions from the perspective of measuring theoretic probability theory, and introduce some basic stochastic processes, martingales and their applications.

Pre-requisite(s):
GCNU1003 Speaking of Statistics, or
GCNU1043 Introduction to Probability and Statistics, or
GCNU1053 Statistics for Social Science, or
GCNU1063 Business Statistics, or
GFQR1001 A Journey with Data or GFQR1002 Data Analytics for Business, or
GFQR1003 Hands On Data Analytics for Everyone, or
GFQR1004 Statistics in Our Daily Life

Course Description:
The course covers computer programming and data analysis in R. The emphasis of the course will be on statistics analysis based on R language. This involves: (1) exploratory data analysis; (2) specification of models to explain the data; (3) estimation and evaluation of models; (4) forecasting from the model.

Pre-requisite(s):
MATH1083 CALCULUS II or MATH1123 CALCULUS FOR SCIENCE AND ENGINEERING , and
COMP1023 FOUNDATIONS OF C PROGRAMMING

Course Description:
Computational data analysis is an essential part of modern statistics. Competent statisticians must not just be able to run existing programs, but to understand the principles on which they work. They must also be able to read, modify, and write code, so that they can assemble the computational tools needed to solve their data analysis problems. 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. They will learn how to set up and run stochastic simulations, how to fit basic statistical models and assess the results, and how to work with and filter large data sets.

Pre-requisite(s): None 

Other Condition(s):
Year 4 standing in Statistics Programme 

Course Description:
Students will undertake an individual project under the supervision of a faculty member and gain the practical experience of applying statistics and mathematics 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 open to Statistics majors only.

Pre-requisite(s):
MATH1053 LINEAR ALGEBRA I & MATH1063 LINEAR ALGEBRA II, or
MATH1003 LINEAR ALGEBRA

Course Description:
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.

Pre-requisite(s):
STAT2013 REGRESSION ANALYSIS

Course Description:
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.

Pre-requisite(s):
STAT2013 REGRESSION ANALYSIS

Course Description:
This course provides students with sophisticated statistical techniques and models for analysing time series data. Using statistical packages, such as R and MATLAB, as computational aid, students will learn to use the models for analysis and forecasting where the distributions of arrival-times and withdrawal-times are unknown and not parametrically modelled. This statistical topic has achieved great prominence in the theoretical statistical literature because it is a particularly good arena for the introduction of techniques of estimating and testing finite-dimensional parameter values - such as a treatment-effectiveness parameter in clinical studies – in the presence of infinite-dimensional unknown parameters. Such problems are referred to as semi-parametric.

Pre-requisite(s): None

Course Description:
This course provides students with basic knowledge of computer-oriented problem solving methodologies, algorithm development, structured programming concepts and design techniques, and implementation tools that facilitate debugging and testing. In particular, structured programming skills will be illustrated with a contemporary programming language.

Pre-requisite(s): None

Course Description:
This course introduces the basic techniques in matrix algebra, which is the foundation for more advanced mathematics and statistics subjects. Major emphasis will be on the system of linear equations, linearly independence in finite dimensional vector spaces.

Pre-requisite(s):
MATH1053 LINEAR ALGEBRA I

Course Description:
This course introduces the basic techniques in matrix algebra, which is the foundation for more advanced mathematics and statistics subjects. Major emphasis will be on the system of linear equations, linearly independence, and eigenvalue problems in finite dimensional vector spaces. Basic ideas and techniques on calculus will be introduced.

Pre-requisite(s): None

Course Description:
This course introduces the basic ideas and techniques in single variable calculus with mathematical rigour to prepare students for more advanced mathematical and statistical subjects.

Pre-requisite(s):
MATH1073 CALCULUS I

Course Description:
This course is a continuation of Calculus I. It provides a solid foundation in multivariable calculus to prepare students for more advanced mathematics and statistical subjects.

Pre-requisite(s):
MATH1083 CALCULUS II

Course Description:
This course provides an introduction to measure theory, Lebesgue integration, L P space, and Fourier analysis. Equipped with this knowledge, students are prepared for further studies in numerical analysis, functional analysis and advanced probability theory

Pre-requisite(s):
STAT2023 ADVANCED PROBABILITY or MATH2063 PROBABILITY AND STATISTICS, and
MATH1063 LINEAR ALGEBRA II, and
MATH1083 CALCULUS II

Course Description:
This course reviews basic probability theory and deals with major stochastic processes including Poisson processes, renewal theory, Markov Chains and continuous-time Markov Chains. Applications to inventory problems, equipment replacement policy and queuing theory are also dealt with through some examples.

Pre-requisite(s):
(GCNU1003 Speaking of Statistics, or
GCNU1043 Introduction to Probability and Statistics, or
GCNU1053 Statistics for Social Science, or GCNU1063 Business Statistics, or
GFQR1001 A Journey with Data, or
GFQR1002 Data Analytics for Business, or
GFQR1003 Hands On Data Analytics for Everyone, or
GFQR1004 Statistics in Our Daily Life), and
 (MATH 1073 Calculus I, or
 MATH1123 Calculus for Science and Engineering, or
 MATH1103 Calculus)

Course Description:
This course teaches students how mathematics interfaces with other disciplines. Real-life problems are solved using models in statistics, mathematics, and physics. The case studies and problem-based approaches are adopted. Programming abilities are very crucial to this course.

Pre-requisite(s):
MATH1053 Linear Algebra I or MATH1003 Linear Algebra, and
MATH1073 Calculus I or MATH1123 Calculus For Science and Engineering

Course Description:
This course introduces the fundamental theory and techniques for both unconstrained and constrained optimization. There will be an overview of the existing numerical software packages. Finally some interdisciplinary techniques and applications related to optimization will be discussed.

Pre-requisite(s)
MATH1073 CALCULUS I, or
MATH1123 Calculus For Science and Engineering

Course Description:
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.

Pre-requisite(s):
MATH1053 LINEAR ALGEBRA I & MATH1063 LINEAR ALGEBRA II, or
MATH1003 LINEAR ALGEBRA

Course Description:
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.

Pre-requisite(s):
MATH1063 LINEAR ALGEBRA II, and
MATH1083 CALCULUS II

Course Description:
The course introduces basic concepts and techniques of measuring theoretic probability, familiarise students with random variable and various probability distributions from the perspective of measuring theoretic probability theory, and introduce some basic stochastic processes, martingales and their applications.

Pre-requisite(s):
GCNU1003 Speaking of Statistics, or
GCNU1043 Introduction to Probability and Statistics, or
GCNU1053 Statistics for Social Science, or
GCNU1063 Business Statistics, or
GFQR1001 A Journey with Data or GFQR1002 Data Analytics for Business, or
GFQR1003 Hands On Data Analytics for Everyone, or
GFQR1004 Statistics in Our Daily Life

Course Description:
The course covers computer programming and data analysis in R. The emphasis of the course will be on statistics analysis based on R language. This involves: (1) exploratory data analysis; (2) specification of models to explain the data; (3) estimation and evaluation of models; (4) forecasting from the model.

Pre-requisite(s):
MATH1083 CALCULUS II or MATH1123 CALCULUS FOR SCIENCE AND ENGINEERING , and
COMP1023 FOUNDATIONS OF C PROGRAMMING

Course Description:
Computational data analysis is an essential part of modern statistics. Competent statisticians must not just be able to run existing programs, but to understand the principles on which they work. They must also be able to read, modify, and write code, so that they can assemble the computational tools needed to solve their data analysis problems. 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. They will learn how to set up and run stochastic simulations, how to fit basic statistical models and assess the results, and how to work with and filter large data sets.

Pre-requisite(s): None 

Other Condition(s):
Year 4 standing in Statistics Programme 

Course Description:
Students will undertake an individual project under the supervision of a faculty member and gain the practical experience of applying statistics and mathematics 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 open to Statistics majors only.

Pre-requisite(s):
MATH1053 LINEAR ALGEBRA I & MATH1063 LINEAR ALGEBRA II, or
MATH1003 LINEAR ALGEBRA

Course Description:
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.

Pre-requisite(s):
STAT2013 REGRESSION ANALYSIS

Course Description:
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.

Pre-requisite(s):
STAT2013 REGRESSION ANALYSIS

Course Description:
This course provides students with sophisticated statistical techniques and models for analysing time series data. Using statistical packages, such as R and MATLAB, as computational aid, students will learn to use the models for analysis and forecasting where the distributions of arrival-times and withdrawal-times are unknown and not parametrically modelled. This statistical topic has achieved great prominence in the theoretical statistical literature because it is a particularly good arena for the introduction of techniques of estimating and testing finite-dimensional parameter values - such as a treatment-effectiveness parameter in clinical studies – in the presence of infinite-dimensional unknown parameters. Such problems are referred to as semi-parametric.