Best Probability & Statistics

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. “Written by four experts of the field, this book offers an excellent entry to statistical learning to a broad audience, including those without strong background in mathematics. … the book also demonstrates how to apply these methods using various R packages by providing detailed worked examples using interesting real data applications.” (Klaus Nordhausen, International Statistical Review, Vol. “The book is structured in ten chapters covering tools for modeling and mining of complex real life data sets. … The style is suitable for undergraduates and researchers … and the understanding of concepts is facilitated by the exercises, both practical and theoretical, which accompany every chapter.” (Irina Ioana Mohorianu, zbMATH, Vol. "The book excels in providing the theoretical and mathematical basis for machine learning, and now at long last, a practical view with the inclusion of R programming examples.

The companion book to COURSERA®'s wildly popular massive open online course "Learning How to Learn" Whether you are a student struggling to fulfill a math or science requirement, or you are embarking on a career change that requires a new skill set, A Mind for Numbers offers the tools you need to get a better grasp of that intimidating material. When she saw how her lack of mathematical and technical savvy severely limited her options—both to rise in the military and to explore other careers—she returned to school with a newfound determination to re-tool her brain to master the very subjects that had given her so much trouble throughout her entire life. “An ingeniously accessible introduction to the science of human cognition—along with practical advice on how to think better.” —James Taranto, The Wall Street Journal “In my book The Math Instinct, I described how we have known since the early 1990s that all ordinary people can do mathematics, and in The Math Gene , I explained why the capacity for mathematical thinking is both a natural consequence of evolution and yet requires effort to unleash it. How do you come to love math and science, and how do you come to learn math and science? Barbara Oakley is the magician who will help you do both.” —Francisco J. Ayala, University Professor and Donald Bren Professor of Biological Sciences, University of California, Irvine, and former President and Chairman of the Board, American Association for the Advancement of Science. But now that learners have a handy guide for ‘knowing better’ they will also be able to ‘do better.’” —Shirley Malcom, Head of Education and Human Resources Programs, American Association for the Advancement of Science “ A Mind for Numbers is an excellent book about how to approach mathematics, science, or any realm where problem solving plays a prominent role.” —J. Given the urgent need for America to improve its science and math education so it can stay competitive, A Mind for Numbers is a welcome find.” —Geoffrey Canada, President, Harlem Children's Zone "It's easy to say 'work smarter, not harder,' but Barbara Oakley actually shows you how to do just that, in a fast-paced and accessible book that collects tips based on experience and sound science. This is a must-read for anyone who has struggled with mathematics and anyone interested in enhancing their learning experience.” —David C. Geary, Curators’ Professor of Psychological Sciences and Interdisciplinary Neuroscience, University o f Missouri. “For students afraid of math and science and for those who love the subjects, this engaging book provides guidance in establishing study habits that take advantage of how the brain works.” —Deborah Schifter, Principal Research Scientist, Science and Mathematics Programs, Education Development Center, Inc.

Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. His research focuses on how to make data analysis better, faster and easier, with a particular emphasis on the use of visualization to better understand data and models. Garrett received his Ph.D at Rice University in Hadley Wickham's lab, where his research traced the origins of data analysis as a cognitive process and identified how attentional and epistemological concerns guide every data analysis.

An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. “Written by four experts of the field, this book offers an excellent entry to statistical learning to a broad audience, including those without strong background in mathematics. … the book also demonstrates how to apply these methods using various R packages by providing detailed worked examples using interesting real data applications.” (Klaus Nordhausen, International Statistical Review, Vol. “The book is structured in ten chapters covering tools for modeling and mining of complex real life data sets. … The style is suitable for undergraduates and researchers … and the understanding of concepts is facilitated by the exercises, both practical and theoretical, which accompany every chapter.” (Irina Ioana Mohorianu, zbMATH, Vol. "The book excels in providing the theoretical and mathematical basis for machine learning, and now at long last, a practical view with the inclusion of R programming examples.

In this stunning new book, Malcolm Gladwell takes us on an intellectual journey through the world of "outliers"--the best and the brightest, the most famous and the most successful. "In the vast world of nonfiction writing, Malcolm Gladwell is as close to a singular talent as exists today... Outliers is a pleasure to read and leaves you mulling over its inventive theories for days afterward.

It maintains a consistent level throughout so that graduate students can use it to gain a foothold into this dynamic research area. "Murray's Mathematical Biology belongs on the shelf of any person with a serious interest in mathematical biology." I recommend the new and expanded third edition to any serious young student interested in mathematical biology who already has a solid basis in applied mathematics." "Mathematical Biology would be eminently suitable as a text for a final year undergraduate or postgraduate course in mathematical biology … .

This new fourth edition of the acclaimed and bestselling Div, Grad, Curl, and All That has been carefully revised and now includes updated notations and seven new example exercises. H. M. Schey is Professor of Mathematics and Statistics at the Rochester Institute of Technology.

Doing Data Science is collaboration between course instructor Rachel Schutt, Senior VP of Data Science at News Corp, and data science consultant Cathy O’Neil, a senior data scientist at Johnson Research Labs, who attended and blogged about the course. Rachel Schutt is the Senior Vice President for Data Science at News Corp. She earned a PhD in Statistics from Columbia University, and was a statistician at Google Research for several years. She holds several pending patents based on her work at Google, where she helped build user-facing products by prototyping algorithms and building models to understand user behavior.

Written from the perspective of the applied mathematician, the latest edition of this bestselling book focuses on the theory and practical applications of Differential Equations to engineering and the sciences.

This book provides a unified, insightful, and modern treatment of linear optimization, that is, linear programming, network flow problems, and discrete optimization. "The true merit of this book, however, lies in its pedagogical qualities which are so impressive..." "Throughout the book, the authors make serious efforts to give geometric and intuitive explanations of various algebraic concepts, and they are widely successful in this effort."

We use tables and graphs to communicate quantitative information: the critical numbers that measure the health, identify the opportunities, and forecast the future of our organizations. He is also the author of Information Dashboard Design: Displaying Data for At-a-Glance Monitoring, Now You See It: Simple Visualization Techniques for Quantitative Analysis , and Signal: Understanding What Matters in a World of Noise .