The Major in Mathematics (MA) offers a wide range of courses and a broad spectrum of mathematical research activities, some multidisciplinary in nature. The curriculum consists of fundamental mathematical concepts in areas including algebra; differential equations; geometry and topology; logic; number theory and combinatorics; and real and complex analysis. It also includes the study of mathematical methods and problem-solving techniques that are applied in science, engineering and computer science. Students have the option to pursue the following specializations*:
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- Operations Research and Data Analytics, which covers the application of analytical methods and mathematical models to solve problems in areas like industrial engineering, operations management and finance;
- Pure Mathematics, which covers advanced mathematics topics in algebra, analysis, mathematical logic and geometry.
*The specializations are available only to students who were admitted from AY2021/2022.
Note:
The two specializations Data Modelling & Analytics and Operations Research & Analytics will be merged and replaced by a single specialization Operations Research & Data Analytics. This applies to all students admitted from AY2021/2022 onwards.
The Major in Quantitative Finance (QF) is a multidisciplinary programme that combines mathematics, finance and computing with a practical orientation, for students who wish to become professionals in the finance industry. The curriculum covers mathematical theory and tools; statistical tools; computing theory and techniques; financial theory and principles; and core financial product knowledge. Students acquire an integrated overview of how mathematical methods and computing techniques are applied to finance, and will learn how to apply computer programming for performing interactive financial data analytics, deploying automated algorithmic trading strategies, and calibrating parameters used for financial derivatives and risk management. The curriculum also provides students solid knowledge on financial products and skills to create new structured financial products. This enables them to keep apace with changes in the financial sector, and the growing focus on new product development in response to competition, new technology and changing consumer needs.
The Major in Data Science and Economics (DSE) is a cross-disciplinary programme aimed at producing students who have strong foundation knowledge in data science and economics as well as hands-on experience with empirical analysis of economic data, to analyse and interpret the local and global impact of data on individuals, organisation, society and the global economic ecosystem. The DSE curriculum incorporates inter-disciplinary learning from data science and economics, with foundations in computer science, mathematics and statistics. In addition to higher-level courses that integrate knowledge and concepts from lower-level core foundational courses, students also read courses related to the application of data science and analytics to the financial market, labour market, and other applied economic issues in education, health, housing and industrial organisation.
The Major in Data Science and Analytics (DSA), jointly offered by the Department of Statistics and Data Science and the Department of Mathematics, in conjunction with the Department of Computer Science in the School of Computing, equips students with the skills to solve complex data-related problems in businesses and new scientific applications using novel analytical tools, and the ability to communicate insights using visualisation tools. In this interdisciplinary programme, students will read courses in mathematics, statistics and computer science, and be exposed to the interplay among these three key areas in the practice of data science. They will also delve in-depth into analytics methods such as artificial intelligence; computation and optimisation; computer algorithms; database and data processing; data mining and machine learning; and high dimensional statistics; as well as applications of analytics to various domains. This programme is hosted by the Department of Statistics and Data Science.