Statistics and Data Science

The Department of Statistics and Data Science has been among the top statistics departments in the world for many years and continues to be at the top in Asia. The combination of quality research and active industry involvement provides the basis to continue delivering scholarly excellence in the years to come.

  Shaping
Future Talent

Our commitment to education and industry-relevant training is evident with the pronounced interest in the Data Science and Analytics and the Data Science and Economics majors, as well as the Masters in Statistics by Coursework programme, with hundreds of students in these programmes combined. In addition, hundreds of College of Humanities and Sciences (CHS) students are choosing second majors and minors related to statistics and data science.   

Data Science and Analytics graduates consistently secure sought-after jobs and achieve high starting salaries. Such trends underscore the industry’s trust in the quality of our graduates and the impact of our academic programmes.

We also trained over 1,900 participants under our Continuing Education and Training (CET) programmes in the past academic year. A standout initiative was the SkillsFuture Career Transition Programme in Data Analytics, which enabled nearly three dozen midcareer professionals to transition their careers into the data analytics domain. The training helped them to find jobs in new sectors.

We broadened our collaborations with corporate partners, by taking steps towards integrating academic expertise with industry know-how. We welcomed professionals from organisations such as Grab, Standard Chartered Bank, GXS Bank, Great Eastern Life and H2O.ai via adjunct appointments. These industry specialists enrich the curriculum with hands-on applications and insightful perspectives, complementing the foundational training we provide. 

  Shaping
Future Solutions

Our research continues to advance knowledge and shed new insights in the field of statistical science.

Accurate Bayesian spatial prediction using Gaussian process models

Large volumes of high-resolution geospatial data collected using remote sensing technology for scientific research present notable challenges for the commonly used Gaussian process models in spatial statistics. Assoc Prof LI Cheng‘s team studied the fixed-domain asymptotic theory from a Bayesian perspective to accurately depict the phenomenon that spatial data are collected at a higher resolution in a given region. In two papers, one published in the Annals of Statistics (December 2022) and the other forthcoming in the Journal of the American Statistical Association, the team reveals that only part of the model parameters in the Gaussian process model can be estimated accurately, while Bayesian spatial prediction at new locations remains efficient as the volume of data increases. These findings establish a solid theoretical foundation for Bayesian analysis of spatial data using Gaussian process models.

Designing efficient algorithms using data augmentation

State space models provide a unified approach for treating a diverse range of problems in time series analysis. The goal of analysis is to infer the model parameters and properties of the latent states to perform signal extraction and forecasting. State space models have prominent applications in many areas such as econometrics and ecology. Bayesian analysis of state space models has the advantage of providing uncertainty measures but is challenging due to the high dimensionality of the model. In a paper published in Statistical Science (May 2023), Asst Prof Linda TAN’s team proposed a data augmentation scheme to design efficient Markov chain Monte Carlo algorithms for Bayesian inference of some non-Gaussian and nonlinear state space models. Applications on exchange rates and trading transactions data demonstrate that the proposed methodology yields significant improvements on state-of-the-art sampling strategies.

  Shaping
Future Society

Statisticians are skilled in unlocking value from data. Our alumni make significant contributions in virtually any sector which seeks to extract business and scientific insights from Big Data for decision-making. Others apply their expertise to solve problems facing society.