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por Kenneth Cukier

Oxford professor and author Viktor Mayer-SchOnberger joins Economist data editor and commentator Kenneth Cukier to deliver insight into the hottest trend in technology. "Big data" makes it possible to instantly analyze and draw conclusions from vast stores of information, enabling revolutionary breakthroughs in business, health, politics and education. But big data also raises troubling social and privacy concerns sure to be a major talking point in the years ahead.

por Tim Harford

From “one of the great (greatest?) contemporary popular writers on economics” (Tyler Cowen) comes a smart, lively, and encouraging rethinking of how to use statistics.

Today we think statistics are the enemy, numbers used to mislead and confuse us. That’s a mistake, Tim Harford says in The Data Detective. We shouldn’t be suspicious of statistics—we need to understand what they mean and how they can improve our lives: they are, at heart, human behavior seen through the prism see more of numbers and are often “the only way of grasping much of what is going on around us.” If we can toss aside our fears and learn to approach them clearly—understanding how our own preconceptions lead us astray—statistics can point to ways we can live better and work smarter.

As “perhaps the best popular economics writer in the world” (New Statesman), Tim Harford is an expert at taking complicated ideas and untangling them for millions of readers. In The Data Detective, he uses new research in science and psychology to set out ten strategies for using statistics to erase our biases and replace them with new ideas that use virtues like patience, curiosity, and good sense to better understand ourselves and the world. As a result, The Data Detective is a big-idea book about statistics and human behavior that is fresh, unexpected, and insightful.

por Lillian Pierson

Jobs in data science abound, but few people have the data science skills needed to fill these increasingly important roles. Data Science For Dummies is the perfect starting point for IT professionals and students who want a quick primer on all areas of the expansive data science space. With a focus on business cases, the book explores topics in big data, data science, and data engineering, and how these three areas are combined to produce tremendous value. If you want to pick-up the skills you see more need to begin a new career or initiate a new project, listening to this book will help you understand what technologies, programming languages, and mathematical methods on which to focus. While this book serves as a wildly fantastic guide through the broad, sometimes intimidating field of big data and data science, it is not an instruction manual for hands-on implementation. Here's what to expect: ● Provides a background in big data and data engineering before moving on to data science and how it's applied to generate value ● Includes coverage of big data frameworks like Hadoop, MapReduce, Spark, MPP platforms, and NoSQL ● And more It's a big, big data world out there-let Data Science For Dummies help you harness its power and gain a competitive edge for your organization.

por John W. Foreman

Data Science gets thrown around in the press like it's magic. Major retailers are predicting everything from when their customers are pregnant to when they want a new pair of Chuck Taylors. It's a brave new world where seemingly meaningless data can be transformed into valuable insight to drive smart business decisions. But how does one exactly do data science? Do you have to hire one of these priests of the dark arts, the "data scientist," to extract this gold from your data? see more Nope. Data science is little more than using straight-forward steps to process raw data into actionable insight. And in Data Smart, author and data scientist John Foreman will show you how that's done within the familiar environment of a spreadsheet.

por David Feldspar

Book 1:
Big data can tremendously improve a company’s performance.
Many company owners or accountants don’t look at all the numbers, misread the numbers, or don’t know how to scale faster and more effectively. In this audiobook, you can listen to information on topics like:
Obstacles most businesses face when trying to process technological data Customer analysis based on data to understand them better Using databases to create more wealth, higher profit margins, and happier see more customers Improving business data storage and using video marketing services the right way Human resources data and challenges when looking at small and big data Better performance and security though online data and protective systems Safeguarding your data from various security attacks

Book 2:
Discover the correct data and solutions for your organization now.

All organizations, especially larger ones, face this one dilemma: sorting and reading the big data. Whether it’s a business, NGO, or another organization, patterns and numbers need to be understood to keep management as effective as possible. This audio solves all kinds of problems and addresses topics for entrepreneurs, accountants, and managers, such as:
The importance of big data and its significance to companiesWhy investing in more effective big data analysis can help firms scale up massively Big data for small and midsize businessesThe top factors to take into account when viewing and interpreting the details of big data The two problems that can happen when implementing Big Data HadoopHow big data help any organization compete in a crazy, competitive society

por Bruce Schneier

A fellow at the Harvard Center for Internet and Society, Bruce Schneier has been called "one of the world's foremost security experts" (Wired). In Data and Goliath, Schneier offers a sobering look at government/corporate surveillance and the ever-rising threat to personal privacy and freedom.

por John D. Kelleher

It has never been easier for organizations to gather, store, and process data. Use of data science is driven by the rise of big data and social media, the development of high-performance computing, and the emergence of such powerful methods for data analysis and modeling as deep learning. Data science encompasses a set of principles, problem definitions, algorithms, and processes for extracting non-obvious and useful patterns from large datasets. It is closely related to the fields of data see more mining and machine learning, but broader in scope. This book offers a brief history of the field, introduces fundamental data concepts, and describes the stages in a data science project. It considers data infrastructure and the challenges posed by integrating data from multiple sources, introduces the basics of machine learning, and discusses how to link machine learning expertise with real-world problems. The book also reviews ethical and legal issues, developments in data regulation, and computational approaches to preserving privacy. Finally, it considers the future impact of data science and offers principles for success in data science projects.

por David T. Olson

Groundbreaking research based on a national database of over 200,000 churches shows that the overall United States population is growing faster than the church. The director of the American Church Research Project, Dave Olson, has worked to analyze church attendance, showing that it is virtually unchanged from fifteen years ago while our population has grown by fifty-two million people.What does this mean for you, your church, and the future of Christianity in North America? The American Church see more in Crisis offers unprecedented access to data that helps you understand the state of the church today. “We live in a world that is post-Christian, postmodern, and multiethnic, whether we realize it or not,” says the author. This book not only gives a realistic picture that confirms hunches and explodes myths, but it provides insight into how the church must change to reach a new and changed world with the hope of the gospel.Readers will find a richly textured mosaic with optimistic and challenging stories. Charts, diagrams, and worksheets provide church leaders and motivated church members with a stimulating read that will provoke much discussion. Questions for discussion accompany the chapters.

por Travis Sawchik

After twenty consecutive losing seasons for the Pittsburgh Pirates, team morale was low, the club's payroll ranked near the bottom of the sport, game attendance was down, and the city was becoming increasingly disenchanted with its team. Pittsburghers joked their town was the city of champions...and the Pirates. Big Data Baseball is the story of how the 2013 Pirates, mired in the longest losing streak in North American pro sports history, adopted drastic big-data strategies to end the see more drought, make the playoffs, and turn around the franchise's fortunes.
Award-winning journalist Travis Sawchik takes you behind the scenes to expertly weave together the stories of the key figures who changed the way the small-market Pirates played the game. For manager Clint Hurdle and the front office staff to save their jobs, they could not rely on a free agent spending spree, instead they had to improve the sum of their parts and find hidden value. They had to change. From Hurdle shedding his old-school ways to work closely with Neal Huntington, the forward-thinking data-driven GM and his team of talented analysts; to pitchers like A. J. Burnett and Gerrit Cole changing what and where they threw; to Russell Martin, the undervalued catcher whose expert use of the nearly-invisible skill of pitch framing helped the team's pitchers turn more balls into strikes; to Clint Barmes, a solid shortstop and one of the early adopters of the unconventional on-field shift which forced the entire infield to realign into positions they never stood in before. Under Hurdle's leadership, a culture of collaboration and creativity flourished as he successfully blended whiz kid analysts with graybeard coaches—a kind of symbiotic teamwork which was unique to the sport.Big Data Baseball is Moneyball on steroids. It is an entertaining and enlightening underdog story that uses the 2013 Pirates season as the perfect lens to examine the sport's burgeoning big-data movement. With the help of data-tracking systems like PitchF/X and TrackMan, the Pirates collected millions of data points on every pitch and ball in play to create a tome of color-coded reports that revealed groundbreaking insights for how to win more games without spending a dime. In the process, they discovered that most batters struggled to hit two-seam fastballs, that an aggressive defensive shift on the field could turn more batted balls into outs, and that a catcher's most valuable skill was hidden. All these data points which aren't immediately visible to players and spectators, are the bit of magic that led the Pirates to spin straw in to gold, finish the 2013 season in second place, end a twenty-year losing streak.

por Gordon S. Linoff

The leading introductory book on data mining, fully updated and revised! When Berry and Linoff wrote the first edition of Data Mining Techniques in the late 1990s, data mining was just starting to move out of the lab and into the office and has since grown to become an indispensable tool of modern business. This new edition-more than 50% new and revised-is a significant update from the previous one, and shows you how to harness the newest data mining methods and techniques to solve common see more business problems. The duo of unparalleled authors share invaluable advice for improving response rates to direct marketing campaigns, identifying new customer segments, and estimating credit risk. In addition, they cover more advanced topics such as preparing data for analysis and creating the necessary infrastructure for data mining at your company. Features significant updates since the previous edition and updates you on best practices for using data mining methods and techniques for solving common business problems Covers a new data mining technique in every chapter along with clear, concise explanations on how to apply each technique immediately Touches on core data mining techniques, including decision trees, neural networks, collaborative filtering, association rules, link analysis, survival analysis, and more Provides best practices for performing data mining using simple tools such as Excel Data Mining Techniques, Third Edition covers a new data mining technique with each successive chapter and then demonstrates how you can apply that technique for improved marketing, sales, and customer support to get immediate results.