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.

Data Structures and Algorithms

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.

COMP2003
Data Structures and Algorithms
3Units

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.

COMP3013
Database Management Systems
3Units

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.

DS4033
Text Mining and Analytics
3Units

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.

DS4053
Introduction to Bioinformatics
3Units

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.

MATH2013
Introduction to Mathematical Finance
3Units

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.

MATH4003
Graph Theory
3Units

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.

MATH4023
Differential Equation
3Units

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.

MATH4033
Computational Finance
3Units

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.

MATH4043
Actuarial Mathematics
3Units

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.

MATH4053
Numerical Methods
3Units

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.

OR3003
Logistics
3Units

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.

OR3013
Linear Programming and Integer Programming
3Units

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.

OR4003
Dynamic Programming Inventory Control
3Units

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.

OR4013
Advanced Topics in Operations Research
3Units

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.

OR4033
Network and Transportation Models
3Units

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.

STAT3003
Survey Sampling
3Units

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.

STAT3013
Life Contingencies
3Units

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.

STAT3023
Quality Control - Six Sigma
3Units

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.

STAT3033
Bayesian Statistics
3Units

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.

STAT4003
Experimental Design
3Units

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.

STAT4005
Final Year Project II (STAT)
3Units

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.

STAT4023
Loss Models
3Units

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.


STAT4033
Structural Equation Modelling
3Units

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.

STAT4053
Survival Analysis
3Units

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.

STAT4073
Data Mining
3Units

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.

STAT4103
Introduction to Deep Learning with Python
3Units

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.


STAT4113
Nonparametric Statistics
3Units

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.

Data Structures and Algorithms

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.

COMP2003
Data Structures and Algorithms
3Units

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.

COMP3013
Database Management Systems
3Units

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.

DS4033
Text Mining and Analytics
3Units

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.

DS4053
Introduction to Bioinformatics
3Units

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.

MATH2013
Introduction to Mathematical Finance
3Units

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.

MATH4003
Graph Theory
3Units

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.

MATH4023
Differential Equation
3Units

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.

MATH4033
Computational Finance
3Units

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.

MATH4043
Actuarial Mathematics
3Units

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.

MATH4053
Numerical Methods
3Units

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.

OR3003
Logistics
3Units

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.

OR3013
Linear Programming and Integer Programming
3Units

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.

OR4003
Dynamic Programming Inventory Control
3Units

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.

OR4013
Advanced Topics in Operations Research
3Units

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.

OR4033
Network and Transportation Models
3Units

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.

STAT3003
Survey Sampling
3Units

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.

STAT3013
Life Contingencies
3Units

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.

STAT3023
Quality Control - Six Sigma
3Units

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.

STAT3033
Bayesian Statistics
3Units

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.

STAT4003
Experimental Design
3Units

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.

STAT4005
Final Year Project II (STAT)
3Units

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.

STAT4023
Loss Models
3Units

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.


STAT4033
Structural Equation Modelling
3Units

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.

STAT4053
Survival Analysis
3Units

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.

STAT4073
Data Mining
3Units

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.

STAT4103
Introduction to Deep Learning with Python
3Units

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.


STAT4113
Nonparametric Statistics
3Units

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.

Data Structures and Algorithms

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.

Database Management Systems

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.

Text Mining and Analytics

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.

Introduction to Bioinformatics

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.

Introduction to Mathematical Finance

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.

Graph Theory

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.

Differential Equation

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.

Computational Finance

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.

Actuarial Mathematics

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.

Numerical Methods

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.

Logistics

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.

Linear Programming and Integer Programming

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.

Dynamic Programming Inventory Control

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.

Advanced Topics in Operations Research

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.

Network and Transportation Models

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.

Survey Sampling

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.

Life Contingencies

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.

Quality Control - Six Sigma

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.

Bayesian Statistics

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.

Experimental Design

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.

Final Year Project II (STAT)

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.

Loss Models

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.


Structural Equation Modelling

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.

Survival Analysis

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.

Data Mining

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.

Introduction to Deep Learning with Python

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.


Nonparametric Statistics

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.