Best Biomathematics

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 irreverent and illuminating book, acclaimed writer and scientist Leonard Mlodinow shows us how randomness, change, and probability reveal a tremendous amount about our daily lives, and how we misunderstand the significance of everything from a casual conversation to a major financial setback. Offering readers not only a tour of randomness, chance, and probability but also a new way of looking at the world, this original, unexpected journey reminds us that much in our lives is about as predictable as the steps of a stumbling man fresh from a night at the bar. Mlodinow, a visiting lecturer at Caltech and coauthor with Stephen Hawking of A Briefer History of Time, leads readers on a walk through the hills and valleys of randomness and how it directs our lives more than we realize.

This textbook is aimed at newcomers to nonlinear dynamics and chaos, especially students taking a first course in the subject. Steven H. Strogatz is professor of applied mathematics at Cornell University.

Since different candidates have different time constraints, EPI includes a study guide with several scenarios, ranging from weekend Hackathon to semester long preparation with a recommended a subset of problems for each scenario. All problems are classified in terms of their difficulty level and include many variants to help you apply what you have learned more widely. It emphasizes problems that stem from real-world applications. and can be coded up in a reasonable time, and is a wonderful complement to. a traditional computer science algorithms and data structures course. ".

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.

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.

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.

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 .