The world is experiencing an explosion of digital data. It is without doubt that the era of data science has arrived, however, flourishing in today’s data-rich information age requires a strong analytical capability as well as advanced modelling skills to enable reliable and actionable knowledge to be extracted from data. The new and exciting research field of data science sees mathematical and statistical methods interact with information technologies to improve the knowledge mined and the outcomes inferred from the massive amounts of data generated worldwide each day. Understanding the computational foundations of data science will place you in the unique position of being able to tackle the fundamental challenge of “turning data into knowledge”.
The aim of AMSI Winter School 2017 is to develop the next generation of quantitative scientists who can thrive in tomorrow’s information age. Our impressive lineup of international and national speakers will build your knowledge in large-scale computational methods for data science, and introduce you to a range of topical applications. The School will feature modules on Nonlinear Optimisation, Numerical Linear Algebra, Machine Learning, Bayesian Inference & Data Assimilation, Model Reduction Methods and Inverse Problems.
Hosted by Queensland University of Technology this winter, the School is designed for postgraduate students and early-career researchers in the mathematical sciences and cognate disciplines. Students and early-career researchers working specifically in computational aspects of data science are of course encouraged to attend: however, the school is a great opportunity for those working in other areas of the mathematical sciences to strengthen their computational mathematics toolkit.