Business Webinar

On Demand Webinar

Hosted by QSLEAP

NUS MSc Data Science & Machine Learning Info Session

On Demand 1 Hour

By: Assoc Prof Vincent Tan

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About This Webinar

Today, we face a massive explosion in the amount of data generated and retained by organisations, the government, and even individuals like you and me. In this age of “big data”, the ability to analyse and process data has become a critical skill set – data scientists make sense out of all this data and use it to make good business decisions. This webinar will make you understand how to build a solid foundation in data science and machine learning, and computing skills in data analytics which is achieved by integrating statistics, mathematics and computing, as well as machine learning and AI.

Register and watch this on-demand webinar video titled "MSc Data Science & Machine Learning Info Session" by National University of Singapore and learn how the programme will guide you to meet the growing demands for big-data professionals by transforming graduates with Bachelor degrees in quantitative science (e.g. mathematics, applied mathematics, statistics and physics) into practitioners. 

Key takeaways:

  • Learn about the National University of Singapore (NUS) Master of Science in Data Science and Machine Learning, an interdisciplinary programme designed to nurture the next generation of leaders in data science by incorporating learning from fields such as computer science, mathematics and statistics, as well as data analytics and machine learning
  • Discover how data has become a necessity in the age of the fourth industrial revolution (Industry 4.0)
  • Hear an expert share how data science has transformed the world, and revolutionised multiple industries as well as our everyday lives
  • Explore the role of data science in sports and examine a specific case study where it is used to predict performance and rank the greatest tennis player
  • Learn about the programme curriculum, structure and admission requirements

Webinar Speakers

Assoc Prof Vincent Tan

Dean’s Chair, Dept of Mathematics

Dr Vincent Tan is an Associate Professor (Dean’s Chair) at the Department of Electrical and Computer Engineering and the Department of Mathematics, National University of Singapore. 
He has a PhD in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology, and completed his BA and MEng in the Electrical and Information Sciences Tripos from Cambridge University. 

With his expertise in multiple areas, Associate Professor Tan wears many hats, including serving as a Singapore National Research Foundation Fellow and an affiliate of the Institute of Data Science.

At QS LEAP, Institutes/Universities from across the world conduct webinars to connect with aspiring candidates like you and share information about their programs, alumni, faculty & teams involved.

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About The Institute

The National University of Singapore (NUS) is Singapore’s flagship university, which offers a global approach to education, research and entrepreneurship, with a focus on Asian perspectives and expertise. We have 17 faculties across three campuses in Singapore, as well as 12 NUS Overseas Colleges across the world. Close to 40,000 students from 100 countries enrich our vibrant and diverse campus community. 

Our multidisciplinary and real-world approach to education, research and entrepreneurship enables us to work closely with industry, governments and academia to address crucial and complex issues relevant to Asia and the world. Researchers in our faculties, 30 university-level research institutes, research centres of excellence and corporate labs focus on themes that include energy, environmental and urban sustainability; treatment and prevention of diseases common among Asians; active ageing; advanced materials; as well as risk management and resilience of financial systems. Our latest research focus is on the use of data science, operations research and cybersecurity to support Singapore's Smart Nation initiative.

Program intend to offer:

  • Master of Science in Data Science and Machine Learning

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