Physical and Engineering Sciences Section News for May 2019

Deterministic simulators (“simulators”) based on microlevel mathematical descriptions of the physics or biology of a system are in the forefront of innovations for studying many engineering, biomechanics, and biological systems. This course will provide statistical tools to design and analyze experiments using simulators to identify the important factors controlling a given system, determine the manner in which the factors affect the system, and optimize the system. The course describes methods for experiments using data from either a simulator-only study or combined data from a physical system and simulator of the system. The course contains four sections. The first three sections provide tools to design and analyze simulator-only studies; the last uses the material of the first three sections to perform Bayesian calibration analysis using physical/simulator data. The first section provides methods for prediction based on given training data. The second section shows how to design computer experiments. The third section describes methods for conducting “sensitivity analyses” to identify the influential inputs to a simulator. The final section provides tools to conduct a Bayesian calibration analysis. This course is based on the second edition of the book The Design and Analysis of Computer Experiments by Santner, Williams, and Notz.

With the recent big data, data science, and deep learning revolution, enterprises ranging from FORTUNE 100 companies to startups across the world are hungry for data scientists and machine learning scientists to bring actionable insight from the vast amount of data collected. In the past couple of years, deep learning has gained traction in many application areas and become an essential tool in the data scientist’s toolbox.

In this course, participants will develop a clear understanding of the big data cloud platform, technical skills in data sciences and machine learning, and motivation and use cases of deep learning through hands-on exercises. We will also cover the “art” part of data science and machine learning to guide participants to learn typical agile data science project flow, general pitfalls in data science and machine learning, and soft skills to effectively communicate with business stakeholders.

The big data platform, data science, and deep learning overviews are specifically designed for an audience with a statistics education background. This course will prepare statisticians to be successful data scientists and machine learning scientists in various industries and business sectors with deep learning as the focus.