Richard De Veaux
Data Science for all. Sure, but who, where, what, how and why?
In spite of the lack of consensus as to what Data Science actually is, universities across the country and around the world are rushing to create programs in Data Science at all levels. Is data science the mysterious intersection of computer science, statistics and domain expertise, or it is an overarching umbrella containing all of these disciplines? What might an introductory data science course look like and how might it fit in to a data science program? In this talk we’ll lay out the challenges for data science and examine some of the current trends.
Dick De Veaux is the C. Carlise and Margaret Tippit Professor of Statistics at Williams College. He holds degrees in Civil Engineering (B.S.E. Princeton), Mathematics (A.B. Princeton), Dance Education (M.A. Stanford) and Statistics (Ph.D., Stanford), where he studied with Persi Diaconis.
Previously, Dick taught at the Wharton School and the Engineering School at Princeton and has been a visiting researcher De Veaux has won numerous teaching awards from the Engineering Council at Princeton. He has won both the Wilcoxon and Shewell (twice) awards from the American Society for Quality, is a fellow of the American Statistical Association (ASA) and an elected member of the ISI. In 2006-2007 he was the William R. Kenan Jr. Visiting Professor for Distinguished Teaching at Princeton. In 2008 he was named the Statistician of the Year by the Boston Chapter of the ASA. He has served on the Board of Directors of the ASA and is the current Vice President.
Dick has been a consultant for many Fortune 500 companies, holds two U.S. patents, and is the author of more than 40 refereed journal articles. He is the co-author, with Paul Velleman and David Bock, of the critically acclaimed textbooks “Intro Stats”, “Stats: Modeling the World” and “Stats: Data and Models” and with Norean Sharpe and Paul Velleman of “Business Statistics” and “Business Statistics: A First Course”, all published by Pearson.
His hobbies include cycling, swimming, singing — and dancing (he was once a professional dancer and taught Modern Dance during Winter Study at Williams). He also teaches a course called “The history, geography and economics of the wines of France”. He is the father of four: two boys and two girls.
Malaysia : Wind Energy Distribution Recent Development
Both wind speed and wind velocity related to the contribution are discussed. It involves a statistical analysis of its wind regime, the probability distribution of wind speed and velocity together with power analysis. A rigorous selection of the probability distribution leads to an unambiguous power analysis. The four well known distribution are Weibull, Gama, Inverse Gamma and Burr probability distribution. The wind power equation is derived through transformation method and the outcome of wind power analysis demonstrates the feasibility for the efficient extraction of wind energy in Malaysia.
Azami Zaharim worked first 13 years as a lecturer in the Universiti Teknologi MARA (University of MARA Technology - UiTM) before joining the Universiti Kebangsaan Malaysia (National University of Malaysia - UKM) in the year 2003. He obtained his BSc(Statistics and Computing) with Honours from North London University, UK in 1988 and PhD (Statistics) in 1996 from University of Newcastle Upon Tyne, UK. He specialize in statistics, public opinion, engineering education and renewable energy resources. In the year 2009 -2011, he headed the Head of Centre for Engineering Education Research and Built Environment (P3K). At the same time, he is currently active involve in outcome based education (OBE). He is also involved actively in the Renewable Energy Resources Analysis, Policy & Energy Management. He has until now published over 300 research papers in Journals and conferences, conducted more than 20 public opinion consultancies and delivered 16 keynotes/invited speeches at national and international meetings.”