Python for Data Science For Dummies (For Dummies (Computer/Tech))

The fast and easy way to learn Python programming and statistics Python is a general-purpose programming language created in the late 1980s--and named after Monty Python--that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the PyGame library. Python For Data Science For Dummies is written for people who are new to data analysis, and discusses the basics of Python data analysis programming and statistics. The book also discusses Google Colab, which makes it possible to write Python code in the cloud. Get started with data science and Python Visualize information Wrangle data Learn from data The book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction. Everyonelovesagoodcompetition. AsIwritethis,twobillionfansareeagerly anticipating the 2006 World Cup. Meanwhile, a fan base that is somewhat smaller (but presumably includes you, dear reader) is equally eager to read all about the results of the NIPS 2003 Feature Selection Challenge, contained herein. Fans of Radford Neal and Jianguo Zhang (or of Bayesian neural n- works and Dirichlet di?usion trees) are gloating “I told you so” and looking forproofthattheirwinwasnota?uke. Butthematterisbynomeanssettled, and fans of SVMs are shouting “wait'til next year!” You know this book is a bit more edgy than your standard academic treatise as soon as you see the dedication: “To our friends and foes. ” Competition breeds improvement. Fifty years ago, the champion in 100m butter?yswimmingwas22percentslowerthantoday'schampion;thewomen's marathon champion from just 30 years ago was 26 percent slower. Who knows how much better our machine learning algorithms would be today if Turing in 1950 had proposed an e?ective competition rather than his elusive Test? But what makes an e?ective competition? The?eld of Speech Recognition hashadNIST-runcompetitionssince1988;errorrateshavebeenreducedbya factorofthreeormore,butthe?eldhasnotyethadtheimpactexpectedofit. Information Retrieval has had its TREC competition since 1992; progress has been steady and refugees from the competition have played important roles in the hundred-billion-dollar search industry. Robotics has had the DARPA Grand Challenge for only two years, but in that time we have seen the results go from complete failure to resounding success (although it may have helped that the second year's course was somewhat easier than the?rst's). This book is both a reference for engineers and scientists and a teaching resource, featuring tutorial chapters and research papers on feature extraction. "This book compiles some very promising techniques, coming from an extremely smart collection of researchers, delivering their best ideas in a competitive environment." Trevor Hastie, Stanford University "Feature selection is a key technology for making sense of the high dimensional data. Isabelle Guyon et al. have done a splendid job in designing a challenging competition, and collecting the lessons learned." Bernhard Schoelkopf, Max Planck Institute "There has been until now insufficient consideration of feature selection algorithms, no unified presentation of leading methods, and no systematic comparisons. This volume is noteworthy for the breadth of methods covered, the clarity of presentations, the unity in notation and the helpful statistical appendices." David G. Stork, Ricoh Innovations "Feature extraction finds application in biotechnology, industrial inspection, the Internet, radar, sonar, and speech recognition. This book will make a difference to the literature on machine learning." Simon Haykin, Mc Master University "This book sets a high standard as the public record of an interesting and effective competition." Peter Norvig, Google Inc The fast and easy way to learn Python programming and statisticsPython is a general-purpose programming language created in the late 1980s & mdash;and named after Monty Python & mdash;that's used by thousands of people to do things from testing microchips at Intel, to powering Instagram, to building video games with the PyGame library. Python For Data Science For Dummies is written for people who are new to data analysis, and discusses the basics of Python data analysis programming and statistics. The book also discusses Google Colab, which makes it possible to write Python code in the cloud. Get started with data science and PythonVisualize informationWrangle dataLearn from dataThe book provides the statistical background needed to get started in data science programming, including probability, random distributions, hypothesis testing, confidence intervals, and building regression models for prediction This book is both a reference for engineers and scientists and a teaching resource, featuring tutorial chapters and research papers on feature extraction. Until now there has been insufficient consideration of feature selection algorithms, no unified presentation of leading methods, and no systematic comparisons. Accompanying CD-ROM contains ... "the data of the NIPS 2003 Feature Selection Challenge and sample Matlab code."--Page 4 of cover