Best Graph Theory

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 .

Beautiful Evidence concludes with 2 chapters that leave the world of pixel and paper flatland representations - and move onto seeing and thinking in space land, the real-land of three-space and time. The Galileo of graphics has done it again.

Describes design strategies - the proper arrangement in space and time of images, words, and numbers - for presenting information about motion, process, mechanism, cause, and effect. With Visual Explanations , Edward R. Tufte adds a third volume to his indispensable series on information display.

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.

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 … .

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.

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."

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 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.

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.