Aligned with Singapore’s National Agenda to retrain mid-career staff through continuous and lifelong learning, the National University of Singapore (NUS) has developed a Master of Science (MSc) in Applied Data Science (ADS). This degree is designed to upskill and re-skill Executive & Administrative (E&A) staff, enabling them to apply data science techniques to solve real-world workplace challenges. The initiative strategically develops internal talent, fostering a data-literate workforce that supports data-driven decision-making within existing workplace roles.
This talk highlights the innovative design and strategic intent and value of the NUS MSc in ADS, outlining its background, rationale and significant benefits. It shows how stackable credentials can be created in its curriculum design and specific skills can be acquired to meet industry and organisational needs in applied data science and analytics. This has been rolled out, implemented and tested in NUS to ensure our workforce remain relevant and are able to contribute to a data-driven organisational culture.
报告人简介：陈俊意教授，新加坡国立大学副校长助理。Professor Roger Tan currently serves as the Associate Vice President in the NUS President’s Office, where he assists the President with various strategic initiatives. His most recent project involved up-skilling and re-skilling the NUS workforce in Learning and Development (L&D) to align with national agenda and needs. This initiative includes the introduction of a Data Literacy and AI Competency Programme, rolled out for all its 4,300 NUS Executive and Administrative workforce. This programme has now been expanded to stackable courses leading to micro-credential certifications.
Prior to this work, Prof Tan had played a key role in establishing the NUS Organizational Excellence Transformation Unit in April 2018 (now reposition as the NUS Agility Office since 2023).
In the realm of academic research, Prof Tan is well-known for his work on sensitivity analysis of matrix eigensystems. He has developed numerous novel numerical methods that play a crucial role in the optimum design of structures in engineering, as well as in solving inverse problems—such as when information on the normal modes of vibration of a structure is used to detect structural damage. Beyond engineering, derivatives of eigenvalues and eigenvectors have applications in quantum mechanics, diffraction grating theory, medical imaging, social network theory, and spin dynamics simulations.
In his other areas of research, he has also collaborated extensively with Prof. Duan Huoyuan and others to develop highly efficient multiscale and stabilized finite element methods for the Stokes and Maxwell equations. These methods are essential for simulating semiconductor materials, particularly in heat transportation caused by phonons and magnetic interactions that feature varying lengths and time scales.
