With it has come vast amounts of data in a variety of fields such as medicine, biology, finance, and marketing. The challenge of understanding these data has led to the development of new tools in the field of statistics, and spawned new areas such as data mining, machine learning, and bioinformatics. Many of these tools have common underpinnings but are often expressed with different terminology. This book describes the important ideas in these areas in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of color graphics. It should be a valuable resource for statisticians and anyone interested in data mining in science or industry. The book's coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book. Trevor Hastie, Robert Tibshirani, and Jerome Friedman are professors of statistics at Stanford University. They are prominent researchers in this area: Hastie and Tibshirani developed generalized additive models and wrote a popular book of that title. Hastie wrote much of the statistical modeling software in S-PLUS and invented principal curves and surfaces. Tibshirani proposed the Lasso and is co-author of the very successful An Introduction to the Bootstrap. Friedman is the co-inventor of many data-mining tools including CART, MARS, and projection pursuit.

    of Wisconsin-Madison) and Wichern (Texas A&M U.) present the newest edition of this college text on the statistical methods for describing and analyzing multivariate data, designed for students who have taken two or more statistics courses. The fifth edition includes the addition of seve

    The reliance traders put on such quantitative analysis was catastrophic for the economy, setting off the series of financial crises that began to erupt in 2007 with the mortgage crisis and from which we're still recovering. Here Derman looks at why people--bankers in particular--still put so much faith in these models, and why it's a terrible mistake to do so.Though financial models imitate the style of physics by using the language of mathematics, ultimately they deal with human beings. Their similarity confuses the fundamental difference between the aims and possible achievements of the phsyics world and that of the financial world. When we make a model involving human beings, we are trying to force the ugly stepsister's foot into Cinderella's pretty glass slipper.  It doesn't fit without cutting off some of the essential parts. Physicists and economists have been too enthusiastic to recognize the limits of their equations in the sphere of human behavior--which of course is what economics is all about.  Models.Behaving.Badly. includes a personal account Derman's childhood encounter with failed models--the utopia of the kibbutz, his experience as a physicist on Wall Street, and a look at the models quants generated: the benefits they brought and the problems they caused. Derman takes a close look at what a model is, and then he highlights the differences between the success of modeling in physics and its relative failure in economics.  Describing the collapse of the subprime mortgage CDO market in 2007, Derman urges us to stop relying on these models where possible, and offers suggestions for mending these models where they might still do some good.  This is a fascinating, lyrical, and very human look behind the curtain at the intersection between mathematics and human nature.

    This report provides a brief yet comprehensive introduction to Python for engineers, researchers, and data scientists who are already familiar with another programming language.Author Jake VanderPlas, an interdisciplinary research director at the University of Washington, explains Python’s essential syntax and semantics, built-in data types and structures, function definitions, control flow statements, and more, using Python 3 syntax.You’ll explore:- Python syntax basics and running Python codeBasic semantics of Python variables, objects, and operators- Built-in simple types and data structures- Control flow statements for executing code blocks conditionally- Methods for creating and using reusable functionsIterators, list comprehensions, and generators- String manipulation and regular expressions- Python’s standard library and third-party modules- Python’s core data science tools- Recommended resources to help you learn more

    During that week, you will learn almost everything modern machine learning has to offer. The author and other practitioners have spent years learning these concepts.Companion wiki — the book has a continuously updated wiki that extends some book chapters with additional information: Q&A, code snippets, further reading, tools, and other relevant resources.Flexible price and formats — choose from a variety of formats and price options: Kindle, hardcover, paperback, EPUB, PDF. If you buy an EPUB or a PDF, you decide the price you pay!Read first, buy later — download book chapters for free, read them and share with your friends and colleagues. Only if you liked the book or found it useful in your work, study or business, then buy it.

    Machine learning provides these, developing methods that can automatically detect patterns in data and then use the uncovered patterns to predict future data. This textbook offers a comprehensive and self-contained introduction to the field of machine learning, based on a unified, probabilistic approach.The coverage combines breadth and depth, offering necessary background material on such topics as probability, optimization, and linear algebra as well as discussion of recent developments in the field, including conditional random fields, L1 regularization, and deep learning. The book is written in an informal, accessible style, complete with pseudo-code for the most important algorithms. All topics are copiously illustrated with color images and worked examples drawn from such application domains as biology, text processing, computer vision, and robotics. Rather than providing a cookbook of different heuristic methods, the book stresses a principled model-based approach, often using the language of graphical models to specify models in a concise and intuitive way. Almost all the models described have been implemented in a MATLAB software package—PMTK (probabilistic modeling toolkit)—that is freely available online. The book is suitable for upper-level undergraduates with an introductory-level college math background and beginning graduate students.

    The most trusted sources in the auto industry have called its Model S the most advanced, safest and best-performing car ever built - and it doesn’t use a drop of gasoline. Tesla has changed the way the public perceives electric vehicles, and inspired the major automakers to revive their own dormant efforts to sell EVs. However, even amidst the avalanche of media coverage that followed the triumph of the Model S, few have grasped the true significance of what is happening. Tesla has redefined the automobile, sparked a new wave of innovation comparable to the internet and mobile computing revolutions, and unleashed forces that will transform not just the auto industry, but every aspect of society. The Tesla story is one part of an ongoing tide of change driven by the use of information technology to eliminate “friction” such as geographic distance, middlemen and outdated regulations. Tesla is simply applying the new order to the auto industry, but the automobile is such a pervasive influence in our lives that redefining how it is designed, built, driven and sold will have sweeping effects in unexpected areas. Just as Tesla built the Model S as an electric vehicle “from the ground up,” it has taken an outsider’s approach to the way it markets its cars. Its direct sales model has drawn legal challenges from entrenched auto dealers, who fear that their outdated business model will be destroyed. Its systems approach to the software and electronics in its cars has highlighted how far behind the technological times the major automakers are. It’s easy to see why readers find Tesla irresistible. CEO Elon Musk is a superstar entrepreneur, a “nauseatingly pro-US” immigrant and the leader of two other cutting-edge companies. Tesla dares to challenge the establishment behemoths and, so far at least, has handily beaten them at their own game. In this history of the 21st century’s most exciting startup, Charles Morris begins with a brief history of EVs and a biography of Tesla’s driving force, Elon Musk. He then details the history of the company, told in the words of the Silicon Valley entrepreneurs who made it happen. There are many fascinating stories here: Martin Eberhard’s realization that there were many like himself, who loved fast cars but wanted to help the environment and bring about the post-oil age; the freewheeling first days, reminiscent of the early internet era; the incredible ingenuity of the team who built the Roadster; Tesla’s near-death experience and miraculous resurrection; the spiteful split between the company’s larger-than-life leaders; the gloves-off battles with hostile media such as Top Gear and the New York Times; and the media’s ironic about-face when the magnificent Model S won the industry’s highest honors, and naysayers became cheerleaders overnight. And the story is just beginning: Tesla has breathtakingly ambitious plans for the future.This book was updated May 1, 2015 to include the latest on the Gigafactory and the D package.

    Courses and books on basic statistics rarely cover the topic from a data science perspective. This practical guide explains how to apply various statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what's important and what's not.Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you're familiar with the R programming language, and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.With this book, you'll learn:Why exploratory data analysis is a key preliminary step in data scienceHow random sampling can reduce bias and yield a higher quality dataset, even with big dataHow the principles of experimental design yield definitive answers to questionsHow to use regression to estimate outcomes and detect anomaliesKey classification techniques for predicting which categories a record belongs toStatistical machine learning methods that "learn" from dataUnsupervised learning methods for extracting meaning from unlabeled data

    It discusses new results, along with applications of probability theory to a variety of problems. The book contains many exercises and is suitable for use as a textbook on graduate-level courses involving data analysis. Aimed at readers already familiar with applied mathematics at an advanced undergraduate level or higher, it is of interest to scientists concerned with inference from incomplete information.

    It bridges the gap between the mechanics of econometrics and modern applications of econometrics by employing a systematic approach motivated by the major problems facing applied researchers today. Throughout the text, the emphasis on examples gives a concrete reality to economic relationships and allows treatment of interesting policy questions in a realistic and accessible framework.

    It is a collection of 150 brain teasing math riddles and puzzles. Their purpose is to make children think and stretch the mind. They are designed to test logic, lateral thinking as well as memory and to engage the brain in seeing patterns and connections between different things and circumstances. They are laid out in three chapters which get more difficult as you go through the book, in the author’s opinion at least. The answers are at the back of the book if all else fails. These are more difficult riddles and are designed to be attempted by children from 10 years onwards, as well as participation from the rest of the family. Tags: Riddles and brain teasers, riddles and trick questions, riddles book, riddles book for kids, riddles for kids, riddles for kids aged 9-12, riddles and puzzles, jokes and riddles, jokes book, jokes book for kids, jokes children, jokes for kids, jokes kids, puzzle book

    The problems are challenging, well-motivated, and accessible. They are representative of the questions asked at interviews at the most exciting companies.The book begins with a summary of patterns for data structure, algorithms, and problem solving that will help you solve the most challenging interview problems. This is followed by chapters on basic and advanced data structures, algorithm design, concurrency, system design, probability and discrete mathematics. Each chapter starts with a brief review of key concepts and results followed by a deep and wide set of questions.EPI concludes with a summary of the nontechnical aspects of interviewing, including common mistakes, strategies for a great interview, perspectives from across the table, negotiating the best offer, and much more."This book is the best compilation of programming related problems I have seen. It is a great resource for a diverse set of topics when preparing for technical interviews, as a quick refresher in a subject area or when you are just looking for a brain teaser to challenge yourself." Shashank Gupta / Scaligent, formerly Engineering Manager, Amazon.com, Senior Engineering Manager, Yahoo!, Manager of Software Development, Cisco Systems

    You also know what it takes to build that talent—and you spend significant financial and human resources to make it happen. Yet somehow, your company’s beautifully designed and well-benchmarked processes don’t translate into the bottom-line talent depth you need. Why?Talent management experts Marc Effron and Miriam Ort argue that companies unwittingly add layers of complexity to their talent building models—without evaluating whether those components add any value to the overall process. Consequently, simple processes like setting employee performance goals become multi-page, headache-inducing time-wasters that turn managers off to the whole process and fail to improve results.In this revolutionary book, Effron and Ort introduce One Page Talent Management (OPTM): a powerfully simple approach that significantly accelerates a company’s ability to develop better leaders faster. The authors outline a straightforward, easy-to-use process for designing results-oriented OPTM processes: base every process on proven scientific research; eliminate complexity by including only those components that add real value to the process; and build transparency and accountability into every practice.Based on extensive research and the authors’ hands-on corporate and consulting experience with companies including Avon Products, Bank of America, and Philips, One Page Talent Management shows how to:• Quickly identify high potential talent without complex assessments• Increase the number of “ready now” successors for key roles• Generate 360 feedback that accelerates change in the most critical behaviors• Significantly reduce the time required for managers to implement talent processes• Enforce accountability for growing talent through corporate culture, compensation, etc.A radical new approach to growing talent, One Page Talent Management trades complexity and bureaucracy for simplicity and a relentless focus on adding value to create the high-quality talent you need—right now.

    This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You’ll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you’ve learned along the way. You’ll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results

    But in a culture full of bad advice, predatory banks, and splurge-now-pay-later temptations, it can also be extremely dangerous—leading you to make financial decisions that could hurt you for years to come. Combine this with a slumped economy, mounds of student loans, and dubious examples from reality TV stars to politicians to your own parents, and it’s no wonder so many twenty-somethings are struggling.Twenty-three-year-old Zac Bissonnette—the author of Debt-Free U—knows exactly what you’re going through. He demystifies the many traps young people fall victim to in their post-college years. He offers fresh insights on everything from job hunting to buying a car to saving for retirement that will give you a foundation for a secure, stable, and happy life. In the process, he reveals why FICO scores are overrated, online job applications are a waste of time, car loans are for suckers, and credit card rewards are a scam.With detours to discuss wine connoisseurs, Really Broke Housewives, and Lenny Dykstra, Zac shows you how to make better choices today so you can be richer, smarter (and better-looking!) for years to come.