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):
COMP1013 STRUCTURED PROGRAMMING, or
GCNIT1013 FOUNDATIONS OF C PROGRAMMING, or
COMP1023 FOUNDATIONS OF C PROGRAMMING, or
COMP2013 OBJECT-ORIENTED PROGRAMMING, or
STAT2043 STRUCTURED PROGRAMMING (FOR STAT STUDENTS), or
COMP3153 C++ PROGRAMMING LANGUAGE

Course Description:
This course develops students' knowledge of data structures and their associated algorithms. It introduces the concepts and techniques of structuring and operating on Abstract Data Types in problem solving. Common sorting, searching and graph algorithms will be discussed, and their complexity studied.

Pre-requisite(s):
COMP1013 STRUCTURED PROGRAMMING, or
GCIT1013 FOUNDATIONS OF C PROGRAMMING, or
COMP1023 FOUNDATIONS OF C PROGRAMMING, or
STAT2043 STRUCTURED PROGRAMMING (FOR STAT STUDENTS), or
COMP3153C++PROGRAMMING LANGUAGE

Course Description:
This course introduces how to represent the data in a database for a given application and how to manage and use a database management system. Topics include: conceptual modelling of a database, relational data model, relational algebra, database language SQL, relation database design, and emerging XML data models. In addition, hands-on DBMS experience is included.

Pre-requisite(s): None

Course Description:
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.

Pre-requisite(s): None

Course Description:
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.

Pre-requisite(s):
MATH1073 CALCULUS I

Course Description:
To introduce (1) the practical and theoretical concepts involved in computing interest; (2) sufficient knowledge to handle all normal interest computations including bonds and mortgages; and (3) the common practical methods of computing approximate interest rates for commercial transactions.

* Actuarial science course.

Pre-requisite(s): None

Course Description:
This course covers some fundamental concepts and principles of graph theory. Practical topics include the Chinese postman problem, the travelling salesman problem and the map colouring problems. Applications of the theory and some related algorithms are also discussed.

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

Course Description:
This course introduces differential equations and covers methods for solving these equations. The modelling of diverse phenomena by differential equations is demonstrated by a variety of examples.

Pre-requisite(s):
STAT2003 ADVANCED STATISTICS, or
MATH2063 PROBABILITY AND STATISTICS

Course Description:
To introduce computational methods for problems in finance, including the computation of market indicators and option prices. The market indicators include stock and option indices. The option prices are based on the Black-Scholes model. Finite difference methods, Monte Carlo Methods and Binomial Tree Methods will be introduced.

* Actuarial science course.

Pre-requisite(s):
MATH2013 INTRODUCTION TO MATHEMATICAL FINANCE

Course Description:
This course introduces the mathematics of life contingencies. Areas to be studied include survival distributions and construction of life tables; and the calculation of values of life insurance, of life annuities and of benefit premiums.

* Actuarial science course.

Pre-requisite(s):
MATH1083 CALCULUS II, and
STAT2043 STRUCTURED PROGRAMMING (FOR STAT STUDENTS), or
COMP1023 FOUNDATIONS OF C PROGRAMMING

Course Description:
This course teaches the ideas underlying commonly used numerical methods. It highlights important considerations in coding algorithms so that they are efficient and reliable. It teaches students how to choose an appropriate numerical method for a particular problem and to interpret the resulting output.

Pre-requisite(s): None 

Course Description:
To provide an understanding of major areas in Logistics as well as to illustrate how to apply various skills and techniques in Logistics to solve and analyse various real problems. The emphasis will be on learning various models and techniques in Logistics. Many practical application models will be discussed and analysed.

Pre-requisite(s):
MATH1053 LINEAR ALGEBRA I

Course Description:
To introduce fundamental theory, techniques and algorithms for linear programming and integer programming problems. It addresses both the basic as well as advanced topics in linear programming and integer programming. Several software packages will be also introduced.

Pre-requisite(s):
OR3013 LINEAR PROGRAMMING AND INTEGER PROGRAMMING

Course Description:
This is a continuation of OR3013 Linear Programming and Integer Programming. The course will introduce the basic and useful techniques in dynamic programming and inventory control. The course will be taught in a problem solving approach.

Pre-requisite(s):
OR3013 LINEAR PROGRAMMING AND INTEGER PROGRAMMING

Course Description:
This is a continuation of OR3013 Linear Programming and Integer Programming, and OR4003 Dynamic Programming and Inventory Control. Some advanced topics will be introduced to those students who are interested in mathematical models arising from industrial and commercial applications.

Pre-requisite(s):
OR3013 LINEAR PROGRAMMING AND INTEGER PROGRAMMING

Course Description:
This is a continuation of OR3013 Linear Programming and Integer Programming. Some basic topics related to networks will be introduced in this course. This course will be taught in a practical-oriented approach.

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:
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.

Pre-requisite(s):
MATH4043 ACTUARIAL MATHEMATICS

Course Description:
This course is a continuation of “Introduction to Actuarial Mathematics”. Mathematics of life contingencies as applied to insurance models including expenses, non-forfeiture benefits, dividends, and valuation theory for pension plans will be discussed.

*Actuarial science course.

Pre-requisite(s): None 

Course Description:
In this information age much data are collected, but less often analysed. This course covers methods for gleaning useful information for large data sets. These methods may be used to help improve product marketing, increase operational efficiency and discover new knowledge.

Pre-requisite(s):
STAT2003 ADVANCED STATISTICS

Course Description:
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.

Pre-requisite(s):
STAT2013 REGRESSION ANALYSIS

Course Description:
This course stresses the theory and applications of experimental designs. Various kinds of experimental designs such as factorial design, uniform design and design of computer experiments will be introduced. Statistical analysis and model identification are taught by using a number of real-life examples.

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.

# Students who continue with the final year project in the second semester of Year 4 should, with the approval of the Program Director, register Final Year Project II (STAT) as a major elective in that semester.

Pre-requisite(s):
STAT2003 ADVANCED STATISTICS

Course Description:
This course covers basic probability, generating functions, theory of recurrent events, Markov chains and Markov processes. It develops and analyses models for fixed time intervals; covers models for claim severities, models for claim frequencies, aggregate claims models and ruin theory. This course is of interest to advance actuarial science students and statistics students.

* Actuarial science course.

Pre-requisite(s):
STAT4013 MULTIVARIATE ANALYSIS

Course Description:
This course describes the logic underlying structural equation modelling (SEM) approach, also known as covariance structure analysis, and how SEM approaches relate to techniques like regression, path analysis, and factor analysis. We will analyse the strengths and shortcomings of SEM as compared to alternative methodologies, and explore the various methodologies for analysing structural equation data.

Pre-requisite(s):
STAT2003 ADVANCED STATISTICS, and
STAT2013 REGRESSION ANALYSIS

Course Description:
This course first presents parameterisations of survival distributions, in terms of hazard intensities, which lend themselves to the formulation of parametric models, including regression-type models which relate failure-time distributions to auxiliary biomedical predictors. The special features of truncation or censoring present unique challenges in the formulation of likelihoods and efficient estimation and testing in settings.

Pre-requisite(s):
MATH1083 Calculus II and MATH1063 Linear Algebra II, or
COMP1023 Foundations of C Programming, or
COMP2013 Object-Oriented Programming

Course Description:
In this information age much data are collected, but less often analysed. This course covers methods for gleaning useful information for large data sets. These methods may be used to help improve product marketing, increase operational efficiency and discover new knowledge.

Pre-requisite(s):
MATH1073 CALCULUS I, and
STAT2043 STRUCTURED PROGRAMMING (FOR STAT STUDENTS) or COMP1023 Foundations of C Programming

Course Description:
This course will expose students in upper undergraduate level to deep learning, a key discipline in artificial intelligence, with its core models and algorithms. Tools and applications using these algorithms are introduced to give the students an idea and experience of how they are implemented in Python, the most popular computer language widely used in data-mining, machine learning and artificial intelligence communities. The aim of the course is to reinforce students the basic concepts and intuition behind modern machine learning methodologies as well as a bit more formal understanding of how, why, and when they can be enabled in applications related to pattern recognition and decision making.

Pre-requisite(s):
STAT2003 ADVANCE STATISTICS

Course Description:
Nonparametric statistics includes nonparametric descriptive statistics, statistical models, inference, and statistical tests, and modern nonparametric techniques. The model structure of nonparametric models is not specified a priori but is instead determined from data.