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Professor Qiwei Yao is a  Professor of Statistics at ÐÓ°ÉÂÛ̳ and a Distinguished Visiting Professor at Guanghua School of Management at PKU. Professor Yao is a leading expert in high-dimensional time series analysis and nonlinear time series analysis, and is also a Fellow of the Institute of Mathematics Statistics, Fellow of the American Statistical Association, and Elected Member of the International Statistical Institute. His current research focuses on modelling and forecasting with vast time series data. Professor Yao has undertaken extensive data analytics consultancy projects from major industry companies including Barclays Bank, Electricité de France (EDF), and Winton Capital Management Ltd.

"The best part of this course was that I could choose to study something that I am passionate about/will benefit me in the future even though it doesn't necessarily fit with my educational background. This freedom, combined with exceptionally good educational materials and teachers, resulted in a very steep learning curve which has ultimately helped me create a better sense of direction for my future (extra-curricular) education and job. Furthermore, I liked how the lectures were an interplay between theory and practice. We were lectured on state of the art theories and relevant case-examples of data analysis and we were also able to apply some of this to practice by utilising R, a programming program. Prior to the programme I never heard of R, and now being able to understand the basics of writing code is very satisfactory. Given the relevance of the topic in this contemporary world, and how it complements my current degree (studying for an MSc in Strategy & organisation) I am certain that this course will benefit me greatly in the future. I lacked quantitative/data analytical skills and this course was definitely a step into the right direction. " Maxim Roben, Vrije Universiteit, Netherlands

"I enjoyed the way the material was delivered. Professor Qiwei Yao was always more than happy and available to answer all our questions after class or during breaks. In addition, the course assistants were very helpful in the afternoon sessions. Briefly, I achieved all the learning outcomes I set out to accomplish both due to my dedication and to course staff. In addition, this course greatly improved my insight on where I would like to be in the future especially in terms of my professional career" Mohammad Mouzannar, Bridge Engineer, Saudi Arabia

Click here to read more of our alumni testimonials.

Knowledge of calculus and statistics at an undergraduate freshman level. Participants should also bring a laptop and a calculator (calculators will be needed in the final examination). 

Assessment will be based on coursework (worth 30% of the final mark) and a final exam (worth 70% of the final mark).

The list below provides an indication of some of the main recommended texts for the course, but a full reading list and electronic course pack will be provided to registered students approximately six weeks before the beginning of the programme.

1. Provost, F. and Fawcett, T. (2013). Data Science for Business. O’Reilly. 
2. Runkler, T.A. (2012). Data Analytics. Springer.
3. James, G., Witten, D., Hastie, T. and Tibshirani, R. (2013). An Introduction to Statistical Learning with Applications in R. Springer.  

Students may choose to read any one of the above three books. They are listed in the ascending order in terms of the technical level, the third is technically the most advanced and also illustrates how to implement data analytic methods in R.   

References for R

• Venables N. et. al. (2019). . (Online).
• Zuur, A., Ieno, E. and Meesters, E. (2009). A Beginners Guide to R. Springer.