'Big data', 'data science', and 'machine learning' have become familiar terms in the news, as statistical methods are brought to bear upon the enormous data sets of modern science and commerce. How did we get here? And where are we going? This book takes us on an exhilarating journey through the revolution in data analysis following the introduction of electronic computation in the 1950s. Beginning with classical inferential theories - Bayesian, frequentist, Fisherian - individual chapters take up a series of influential topics: survival analysis, logistic regression, empirical Bayes, the jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern approach integrates methodology and algorithms with statistical inference. The book ends with speculation on the future direction of statistics and data science.

    This guide also helps you understand the many data-mining techniques in use today.Based on an MBA course Provost has taught at New York University over the past ten years, Data Science for Business provides examples of real-world business problems to illustrate these principles. You’ll not only learn how to improve communication between business stakeholders and data scientists, but also how participate intelligently in your company’s data science projects. You’ll also discover how to think data-analytically, and fully appreciate how data science methods can support business decision-making.Understand how data science fits in your organization—and how you can use it for competitive advantageTreat data as a business asset that requires careful investment if you’re to gain real valueApproach business problems data-analytically, using the data-mining process to gather good data in the most appropriate wayLearn general concepts for actually extracting knowledge from dataApply data science principles when interviewing data science job candidates

    The book begins by introducing the R language, including the development environment. Focusing on practical solutions, the book also offers a crash course in practical statistics and covers elegant methods for dealing with messy and incomplete data using features of R.About the TechnologyR is a powerful language for statistical computing and graphics that can handle virtually any data-crunching task. It runs on all important platforms and provides thousands of useful specialized modules and utilities. This makes R a great way to get meaningful information from mountains of raw data.About the BookR in Action is a language tutorial focused on practical problems. It presents useful statistics examples and includes elegant methods for handling messy, incomplete, and non-normal data that are difficult to analyze using traditional methods. And statistical analysis is only part of the story. You'll also master R's extensive graphical capabilities for exploring and presenting data visually. Purchase of the print book comes with an offer of a free PDF, ePub, and Kindle eBook from Manning. Also available is all code from the book. What's InsidePractical data analysis, step by stepInterfacing R with other softwareUsing R to visualize dataOver 130 graphsEight reference appendixes================================Table of ContentsPart I Getting startedIntroduction to RCreating a datasetGetting started with graphsBasic data managementAdvanced data managementPart II Basic methodsBasic graphsBasic statisticsPart III Intermediate methodsRegressionAnalysis of variancePower analysisIntermediate graphsRe-sampling statistics and bootstrappingPart IV Advanced methodsGeneralized linear modelsPrincipal components and factor analysisAdvanced methods for missing dataAdvanced graphics

    

    Renowned for her lucid, accessible prose, Epp explains complex, abstract concepts with clarity and precision. This book presents not only the major themes of discrete mathematics, but also the reasoning that underlies mathematical thought. Students develop the ability to think abstractly as they study the ideas of logic and proof. While learning about such concepts as logic circuits and computer addition, algorithm analysis, recursive thinking, computability, automata, cryptography, and combinatorics, students discover that the ideas of discrete mathematics underlie and are essential to the science and technology of the computer age. Overall, Epp's emphasis on reasoning provides students with a strong foundation for computer science and upper-level mathematics courses.

    This brief, accessible introduction describes some of the most interesting research in the field and also enables readers to implement and experiment with genetic algorithms on their own. It focuses in depth on a small set of important and interesting topics--particularly in machine learning, scientific modeling, and artificial life--and reviews a broad span of research, including the work of Mitchell and her colleagues.The descriptions of applications and modeling projects stretch beyond the strict boundaries of computer science to include dynamical systems theory, game theory, molecular biology, ecology, evolutionary biology, and population genetics, underscoring the exciting general purpose nature of genetic algorithms as search methods that can be employed across disciplines.An Introduction to Genetic Algorithms is accessible to students and researchers in any scientific discipline. It includes many thought and computer exercises that build on and reinforce the reader's understanding of the text. The first chapter introduces genetic algorithms and their terminology and describes two provocative applications in detail. The second and third chapters look at the use of genetic algorithms in machine learning (computer programs, data analysis and prediction, neural networks) and in scientific models (interactions among learning, evolution, and culture; sexual selection; ecosystems; evolutionary activity). Several approaches to the theory of genetic algorithms are discussed in depth in the fourth chapter. The fifth chapter takes up implementation, and the last chapter poses some currently unanswered questions and surveys prospects for the future of evolutionary computation.

    It brings together all the knowledge needed to write any program, use any standard or third-party Python 3 library, and create new library modules of your own.

    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.

    You’ll learn how to combine social web data, analysis techniques, and visualization to find what you’ve been looking for in the social haystack—as well as useful information you didn’t know existed.Each standalone chapter introduces techniques for mining data in different areas of the social Web, including blogs and email. All you need to get started is a programming background and a willingness to learn basic Python tools.Get a straightforward synopsis of the social web landscapeUse adaptable scripts on GitHub to harvest data from social network APIs such as Twitter, Facebook, LinkedIn, and Google+Learn how to employ easy-to-use Python tools to slice and dice the data you collectExplore social connections in microformats with the XHTML Friends NetworkApply advanced mining techniques such as TF-IDF, cosine similarity, collocation analysis, document summarization, and clique detectionBuild interactive visualizations with web technologies based upon HTML5 and JavaScript toolkits"A rich, compact, useful, practical introduction to a galaxy of tools, techniques, and theories for exploring structured and unstructured data." --Alex Martelli, Senior Staff Engineer, Google

    "Java Programming & Application Development for Google/Open Handset Alliance Mobile Phones & Internet Devices."

    This great beginner's guide also provides an easy introduction to reading and playing music - so whether you have no previous musical experience OR are simply new to the keyboard, this clear and concise guide will have you playing tunes on your new keyboard in no time ! For kids under the age of 10, you may prefer Ben's My First Keyboard book instead - created especially for the youngest beginner. Search under Ben Parker. * Easy to follow instructions and illustrations * Simple exercises to follow and practice * Learn at your own pace * Fun and easy songs, exercises and tunes to play * Simply follow the lessons in the book and you will be playing Keyboard in no time ! Ben Parker's best-selling range of beginner's music tuition books for adults and children include the 'How To Play'...and also the 'My First' series of books covering: Keyboard, Piano, Guitar, Ukulele, Banjo, Harmonica and Recorder.

    With the king and queen away, she has to manage the Kingdom of Kod's humongous fruit-selling empire. Overseas departments, scads of inventory, conflicting prices, and so many customers! It's all such a confusing mess. But a mysterious book and a helpful fairy promise to solve her organizational problems-with the practical magic of databases.In The Manga Guide to Databases, Tico the fairy teaches the Princess how to simplify her data management. We follow along as they design a relational database, understand the entity-relationship model, perform basic database operations, and delve into more advanced topics. Once the Princess is familiar with transactions and basic SQL statements, she can keep her data timely and accurate for the entire kingdom. Finally, Tico explains ways to make the database more efficient and secure, and they discuss methods for concurrency and replication.Examples and exercises (with answer keys) help you learn, and an appendix of frequently used SQL statements gives the tools you need to create and maintain full-featured databases.(Of course, it wouldn't be a royal kingdom without some drama, so read on to find out who gets the girl-the arrogant prince or the humble servant.)This EduManga book is a translation of a bestselling series in Japan, co-published with Ohmsha, Ltd., of Tokyo, Japan.

    It's about learning to eat properly for your health." -Vitor Belfort, UFC two-time world champion"Mike Dolce's the best in the business."-Chael Sonnen, UFC world title contender"Mike Dolce's knowledge of nutrition and strength & conditioning has led him to be one of the most highly sought-after coaches in the sport." -Joshua Carey, Bleacher Report"You can learn a lot from this man right here." -Ariel Helwani, AOL's MMAFighting.comABOUT THE DOLCE DIET: LIVING LEANCalled "the patron saint of weight cutting," Mike Dolce has coordinated the high-profile weight loss for many of the world's top athletes, including...* Quinton "Rampage" Jackson, UFC / Pride FC world champion* Vitor "The Phenom" Belfort, UFC two-time world champion* Thiago "Pitbull" Alves, UFC world title contender* Chael Sonnen WEC / UFC world title contender* Gray "Bully" Maynard, UFC world title contender* Nate "Rock" Quarry, UFC world title contender* Mike "Quicksand" Pyle, WEC world champion* Jay "Thorobred" Hieron, IFL world championAs well as fan favorites...* Michael "The Count" Bisping, The Ultimate Fighter 3 winner* Jake "Juggernaut" Ellenberger, UFC veteran* Ed "Shortfuse" Herman, The Ultimate Fighter 3 runner-up* Chris "The Crippler" Leben, UFC veteran* Duane "BANG" Ludwig, UFC & K-1 veteranand many more!For the first time in print, Mike Dolce shares the same the principles, recipes, and strength-training workouts he uses in MMA's elite fight camps and how they can be used by YOU!INSIDE you will learn:* Recipes used in MMA's top fight camps with gluten-free & vegan options* Easy to follow sample meal plans with gluten-free & vegan options* Strength & Conditioning exercises with instructions & photos* Workout plans used by today's top athletesWHAT PEOPLE ARE SAYING ABOUT THE DOLCE DIETThe Dolce Diet, three words about Living Lean: 1. Simple 2. Inspirational 3. Effective. Thank you, Mike Dolce! You've made staying in shape easy! ~STEWART M. The Dolce Diet, Love it! My Little-Boy-2-B has been on it for 5.5 months! This diet is truly amazing for moms pre & post baby! Yes, The DolceDiet is prego friendly! Plenty of the RIGHT kind of food that tastes great! ~THE H2H WAITRESSStarted two weeks ago. Lost 13 pounds so far. Yea! Love the recipes! So do my kids! Thank you! ~DAWN H.Body fat down 4% in 2 months?! Yessss! #LIVING LEAN ~MOLLY C.The Dolce Diet, started 410, down 50 lbs. so far. ~ JOSH W.The Dolce Diet, 13 lbs. lost in 4 weeks! People are asking what I'm doing...Telling them LIVING LEAN! ~MIKE S.Real talk! The Dolce Diet is the Einstein, da Vinci and Jesus of losing weight all wrapped up in one...gluten free wrap that is. ~MIKEY F.Another 5 (lbs. lost) on The Dolce Diet. 25 pounds down in 2 weeks, 100 to go! #LivingLean! ~JOHN P.

    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

    A comprehensive introduction to the subject, this book shows in detail how such problems can be solved numerically with great efficiency. The focus is on recognizing convex optimization problems and then finding the most appropriate technique for solving them. The text contains many worked examples and homework exercises and will appeal to students, researchers and practitioners in fields such as engineering, computer science, mathematics, statistics, finance, and economics.