Duke's new man was Mike Krzyzewski. The only problem was, no one knew who Krzyzewski was, he had a so-so record in his short time as head coach of Army, and worst of all, no one could even pronounce his name. The announcement caused head scratches . . . if not immediate calls for his head . . . and on this note his career at Duke began.      The table was set nine days later, when on March 27, 1980, Jim Valvano was hired by North Carolina State to be their new head coach. The hiring didn't raise as many eyebrows, but with the exuberant Valvano on board, two new coaches were now in place to challenge Dean Smith—and the most sensational competitive decade in history was about to unfold.      In the skillful hands of John Feinstein, this extraordinary rivalry—and the men behind it—come to life in a unique, intimate way. The Legends Club is a sports book that captures an era in American sport and culture, documenting the inside view of a decade of absolutely incredible competition. Feinstein pulls back the curtain on the recruiting wars, the intensely personal competition that wasn't always friendly, the enormous pressure and national stakes, and the battle for the very soul of college basketball allegiance in a hot-bed area. Getting to the roots of the NCAA goliath that is followed religiously by millions of fans today, Feinstein uses his unprecedented access to all three coaches to paint a portrait only he could conjure. The Legends Club is destined to be one of Feinstein's biggest bestsellers.

    Though this was only one regular season game, the match-up of these two teams demonstrated how Major League Baseball has changed since the early days of Athletics general manager Billy Beane and the publication of Michael Lewis’ classic book.Over the past twenty years, power and analytics have taken over the game, driving carefully calibrated teams like the Astros to victory. Seemingly every pitcher now throws mid-90s heat and studiously compares their mechanics against the ideal. Every batter in the lineup can crack homers and knows their launch angles. Teams are relying on unorthodox strategies, including using power-losing—purposely tanking a few seasons to get the best players in the draft.As he chronicles each inning and the unfolding drama as these two teams continually trade the lead—culminating in a 9-8 Oakland victory in the bottom of the ninth—Neyer considers the players and managers, the front office machinations, the role of sabermetrics, and the current thinking about what it takes to build a great team, to answer the most pressing questions fans have about the sport today.

    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

    While such visualizations can better inform us, they can also deceive by displaying incomplete or inaccurate data, suggesting misleading patterns—or simply misinform us by being poorly designed, such as the confusing “eye of the storm” maps shown on TV every hurricane season.Many of us are ill equipped to interpret the visuals that politicians, journalists, advertisers, and even employers present each day, enabling bad actors to easily manipulate visuals to promote their own agendas. Public conversations are increasingly driven by numbers, and to make sense of them we must be able to decode and use visual information. By examining contemporary examples ranging from election-result infographics to global GDP maps and box-office record charts, How Charts Lie teaches us how to do just that.

    In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. If you have an aptitude for mathematics and some programming skills, author Joel Grus will help you get comfortable with the math and statistics at the core of data science, and with hacking skills you need to get started as a data scientist. Today’s messy glut of data holds answers to questions no one’s even thought to ask. This book provides you with the know-how to dig those answers out. Get a crash course in Python Learn the basics of linear algebra, statistics, and probability—and understand how and when they're used in data science Collect, explore, clean, munge, and manipulate data Dive into the fundamentals of machine learning Implement models such as k-nearest Neighbors, Naive Bayes, linear and logistic regression, decision trees, neural networks, and clustering Explore recommender systems, natural language processing, network analysis, MapReduce, and databases

    Mitchell covers the field of machine learning, the study of algorithms that allow computer programs to automatically improve through experience and that automatically infer general laws from specific data.

    Free of review and ramp-up material, Statistics Essentials For Dummies sticks to the point, with content focused on key course topics only. It provides discrete explanations of essential concepts taught in a typical first semester college-level statistics course, from odds and error margins to confidence intervals and conclusions. This guide is also a perfect reference for parents who need to review critical statistics concepts as they help high school students with homework assignments, as well as for adult learners headed back into the classroom who just need a refresher of the core concepts. The Essentials For Dummies Series Dummies is proud to present our new series, The Essentials For Dummies. Now students who are prepping for exams, preparing to study new material, or who just need a refresher can have a concise, easy-to-understand review guide that covers an entire course by concentrating solely on the most important concepts. From algebra and chemistry to grammar and Spanish, our expert authors focus on the skills students most need to succeed in a subject.

    With its unique combination of Japanese-style comics called manga and serious educational content, the EduManga format is already a hit in Japan.In The Manga Guide to Statistics, our heroine Rui is determined to learn about statistics to impress the dreamy Mr. Igarashi and begs her father for a tutor. Soon she's spending her Saturdays with geeky, bespectacled Mr. Yamamoto, who patiently teaches her all about the fundamentals of statistics: topics like data categorization, averages, graphing, and standard deviation.After all her studying, Rui is confident in her knowledge of statistics, including complex concepts like probability, coefficients of correlation, hypothesis tests, and tests of independence. But is it enough to impress her dream guy? Or maybe there's someone better, right in front of her?Reluctant statistics students of all ages will enjoy learning along with Rui in this charming, easy-to-read guide, which uses real-world examples like teen magazine quizzes, bowling games, test scores, and ramen noodle prices. Examples, exercises, and answer keys help you follow along and check your work. An appendix showing how to perform statistics calculations in Microsoft Excel makes it easy to put Rui's lessons into practice.This EduManga book is a translation from a bestselling series in Japan, co-published with Ohmsha, Ltd. of Tokyo, Japan.

    Prior to the tournament, a statistician calculated the Bulldogs as a 200-to-1 shot to win. But as fascinating as what Butler accomplished was how they did it. Underdawgs tells the incredible and uplifting story. Butler’s coach, 33-year-old Brad Stevens, looked so young he was often mistaken for one of the players, but he had quickly become one of the best coaches in the nation by employing the “Butler Way.” This philosophy of basketball and life, adopted by former coach Barry Collier, is based on five principles: humility, passion, unity, servanthood, and thankfulness. Even the most casual observer could see this in every player, on the court and off, from NBA first-round draft pick Gordon Hayward to the last guy on the bench. Butler was coming off a great 2009–10 regular season, but its longtime existence on the periphery of major college basketball fostered doubt as March Madness set in. But after two historic upsets, one of top-seeded Syracuse and another of second-seeded Kansas State, and making it to the Final Four, the Bulldogs came within the diameter of a shoelace of beating the perennial leaders of college basketball: the Duke Blue Devils. Much more than a sports story, Underdawgs is the consummate David versus Goliath tale. Despite Duke’s winning the championship, the Bulldogs proved they belonged in the game and, in the process, won the respect of people who were not even sports fans.

    Polya, How to Solve It will show anyone in any field how to think straight. In lucid and appealing prose, Polya reveals how the mathematical method of demonstrating a proof or finding an unknown can be of help in attacking any problem that can be reasoned out--from building a bridge to winning a game of anagrams. Generations of readers have relished Polya's deft--indeed, brilliant--instructions on stripping away irrelevancies and going straight to the heart of the problem.

    But many people use data in ways they don't even understand, meaning they aren't getting the most from it. Bayesian Statistics the Fun Way will change that.This book will give you a complete understanding of Bayesian statistics through simple explanations and un-boring examples. Find out the probability of UFOs landing in your garden, how likely Han Solo is to survive a flight through an asteroid shower, how to win an argument about conspiracy theories, and whether a burglary really was a burglary, to name a few examples.By using these off-the-beaten-track examples, the author actually makes learning statistics fun. And you'll learn real skills, like how to:- How to measure your own level of uncertainty in a conclusion or belief- Calculate Bayes theorem and understand what it's useful for- Find the posterior, likelihood, and prior to check the accuracy of your conclusions- Calculate distributions to see the range of your data- Compare hypotheses and draw reliable conclusions from themNext time you find yourself with a sheaf of survey results and no idea what to do with them, turn to Bayesian Statistics the Fun Way to get the most value from your data.

    There’s a famous piece of writing advice—offered by Ernest Hemingway, Stephen King, and myriad writers in between—not to use -ly adverbs like “quickly” or “fitfully.” It sounds like solid advice, but can we actually test it? If we were to count all the -ly adverbs these authors used in their careers, do they follow their own advice compared to other celebrated authors? What’s more, do great books in general—the classics and the bestsellers—share this trait?In Nabokov’s Favorite Word Is Mauve, statistician and journalist Ben Blatt brings big data to the literary canon, exploring the wealth of fun findings that remain hidden in the works of the world’s greatest writers. He assembles a database of thousands of books and hundreds of millions of words, and starts asking the questions that have intrigued curious word nerds and book lovers for generations: What are our favorite authors’ favorite words? Do men and women write differently? Are bestsellers getting dumber over time? Which bestselling writer uses the most clichés? What makes a great opening sentence? How can we judge a book by its cover? And which writerly advice is worth following or ignoring?

    During its nine seasons, the ABA generated scorn and laughter--and made a lasting impact on how the game is played. 24 pages of photographs.

    Less talked about is O'Brien's other winning record: Nearly every one of the players who stuck with his program--poor kids growing up in high-crime neighborhoods and saddled with the lousy educational system available in urban America--managed to get to college. But O'Brien is no saint. Saints give without expecting anything in return. O'Brien needs his players and their problems as much as they need him. Revolving around fascinating, complex characters, The Assist is a captivating narrative of a basketball team in pursuit of a championship that also drills down into the legacy of desegregation and explores issues of education, family, and race. O'Brien is a middle-aged white guy coaching an all-black team playing in an all-white neighborhood that three decades ago was at the center of the busing wars dividing cities across the country--a time and place indelibly described in J. Anthony Lukas's powerful book Common Ground. It's the inspiring story of a man who makes a difference, and of boys surmounting nearly impossible odds; it is also the story of the ones who don't make it, and why.

    This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.You'll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you'll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.Develop your understanding of probability and statistics by writing and testing codeRun experiments to test statistical behavior, such as generating samples from several distributionsUse simulations to understand concepts that are hard to grasp mathematicallyLearn topics not usually covered in an introductory course, such as Bayesian estimationImport data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics toolsUse statistical inference to answer questions about real-world data