Everydata

Everydata Author John H. Johnson
ISBN-10 9781629561028
Year 2016-04-12
Pages 240
Language en
Publisher Bibliomotion, Inc.
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While everyone is talking about “big data,” the truth is that understanding the “little data” (stock reports, newspaper headlines, weather forecasts, etc.) is what will help you make smarter decisions at work, at home, and in every aspect of your life. The average person consumes approximately 30 gigabytes of data every single day, but has no idea how to interpret it correctly. Everydata explains, through the eyes of an expert economist and statistician, how to correctly interpret all of the small bytes of data we consume in a day. Readers will become effective, skeptical consumers of everyday data. • Everydata is filled with countless examples of people misinterpreting data – oftentimes with catastrophic results: • Millions of women avoid caffeine during pregnancy because they interpret correlation as causation • The initial launch of HealthCare.gov failed in part because key decision-makers couldn’t observe all of the data • A baby food company was investigated by the Federal Trade Commission for cherry picking data • Attorneys faced a $1 billion jury verdict because of outlier data • The Space Shuttle Challenger exploded because the engineers were dealing with a limited sample set • Hedge fund companies claim they can make smarter predictions – but the market data says otherwise Each chapter of Everydata highlights one commonly misunderstood data concept, using both real-world and hypothetical examples from a wide range of topics, including business, politics, advertising, law, engineering, retail, parenting, and more. Readers will get the answer to the question—“Now what?”—along with concrete ways they can use this information to immediately start making smarter decisions, today and every day.

Everydata

Everydata Author John H. Johnson
ISBN-10 9781351861830
Year 2016-10-14
Pages 224
Language en
Publisher Routledge
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While everyone is talking about "big data," the truth is that understanding the "little data"--the stats that underlie newspaper headlines, stock reports, weather forecasts, and so on--is what helps you make smarter decisions at work, at home, and in every aspect of your life. The average person consumes approximately 30 gigabytes of data every single day, but has no idea how to interpret it correctly. EVERYDATA explains, through the eyes of an expert economist and statistician, how to decipher the small bytes of data we consume in a day. EVERYDATA is filled with countless examples of people misconstruing data--with results that range from merely frustrating to catastrophic: The space shuttle Challenger exploded in part because the engineers were reviewing a limited sample set. Millions of women avoid caffeine during pregnancy because they interpret correlation as causation. Attorneys faced a $1 billion jury verdict because of outlier data. Each chapter highlights one commonly misunderstood data concept, using both realworld and hypothetical examples from a wide range of topics, including business, politics, advertising, law, engineering, retail, parenting, and more. You'll find the answer to the question--"Now what?"--along with concrete ways you can use this information to immediately start making smarter decisions, today and every day.

Beyond Basic Statistics

Beyond Basic Statistics Author Kristin H. Jarman
ISBN-10 9781118856093
Year 2015-04-22
Pages 200
Language en
Publisher John Wiley & Sons
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Features basic statistical concepts as a tool for thinking critically, wading through large quantities of information, and answering practical, everyday questions Written in an engaging and inviting manner, Beyond Basic Statistics: Tips, Tricks, and Techniques Every Data Analyst Should Know presents the more subjective side of statistics—the art of data analytics. Each chapter explores a different question using fun, common sense examples that illustrate the concepts, methods, and applications of statistical techniques. Without going into the specifics of theorems, propositions, or formulas, the book effectively demonstrates statistics as a useful problem-solving tool. In addition, the author demonstrates how statistics is a tool for thinking critically, wading through large volumes of information, and answering life’s important questions. Beyond Basic Statistics: Tips, Tricks, and Techniques Every Data Analyst Should Know also features: Plentiful examples throughout aimed to strengthen readers’ understanding of the statistical concepts and methods A step-by-step approach to elementary statistical topics such as sampling, hypothesis tests, outlier detection, normality tests, robust statistics, and multiple regression A case study in each chapter that illustrates the use of the presented techniques Highlights of well-known shortcomings that can lead to false conclusions An introduction to advanced techniques such as validation and bootstrapping Featuring examples that are engaging and non-application specific, the book appeals to a broad audience of students and professionals alike, specifically students of undergraduate statistics, managers, medical professionals, and anyone who has to make decisions based on raw data or compiled results.

The Caterpillar s Edge

The Caterpillar s Edge Author Sid Mohasseb
ISBN-10 9780996636315
Year 2017-02-28
Pages 144
Language en
Publisher Rugged Land
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Business leaders, large and small, need to learn a new game with very different rules. They must accept an ever-changing and uncertain landscape, but a landscape that can be constantly leveraged for greater profitability. They must believe that their companies are caterpillars with the potential to become butterflies. The Caterpillar's Edge shows why we must embrace a future of flux. It exposes the addictions that chain us to our past and the truths that influence our behaviors. And, it shows just how to seize breakthrough advantages by pushing through all the noise around "big data." Within its DNA, the caterpillar aspires and pushes for more, and it gets just that, evolving gracefully from one entity into another, always building a competitive edge in the process. Break free from accepted archaic business practices by cracking that secret code which demands evolving your business always.

Data Warehousing

Data Warehousing Author Mark Humphries
ISBN-10 0130809020
Year 1999
Pages 362
Language en
Publisher Prentice Hall Professional
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Strategies for Real Time System Specification

Strategies for Real Time System Specification Author Derek Hatley
ISBN-10 9780133492354
Year 2013-07-19
Pages 440
Language en
Publisher Addison-Wesley
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This is the digital version of the printed book (Copyright © 1987). Here is a casebook, a practical reference, and an indispensable guide for creating a systematic, formal methodology for large, real-time, software-based systems. The book introduces the widely implemented Hatley/Pirbhai methods, a major extension of the DeMarco analysis method describing how external events control the system's operating behavior. The techniques are used in major avionics and electronics companies worldwide, and are automated by most major CASE tools, including TurboCASE/Sys by StructSoft, Inc. Large software-based systems, especially those for real-time applications, require multi-mode operation, direct interaction with a rapidly changing physical environment, and fast response times. In the past, the development of such systems was prone to massive cost and schedule overruns, and to inadequate performance and reliability. Strategies for Real-Time System Specification addresses these problems by integrating a finite-state machine structure into classical analysis methods. The book contains nearly 200 diagrams, many of which illustrate the requirements specification of a flight management system for a major avionics developer.

Advanced Analytics with Spark

Advanced Analytics with Spark Author Sandy Ryza
ISBN-10 9781491972908
Year 2017-06-12
Pages 280
Language en
Publisher "O'Reilly Media, Inc."
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In the second edition of this practical book, four Cloudera data scientists present a set of self-contained patterns for performing large-scale data analysis with Spark. The authors bring Spark, statistical methods, and real-world data sets together to teach you how to approach analytics problems by example. Updated for Spark 2.1, this edition acts as an introduction to these techniques and other best practices in Spark programming. You’ll start with an introduction to Spark and its ecosystem, and then dive into patterns that apply common techniques—including classification, clustering, collaborative filtering, and anomaly detection—to fields such as genomics, security, and finance. If you have an entry-level understanding of machine learning and statistics, and you program in Java, Python, or Scala, you’ll find the book’s patterns useful for working on your own data applications. With this book, you will: Familiarize yourself with the Spark programming model Become comfortable within the Spark ecosystem Learn general approaches in data science Examine complete implementations that analyze large public data sets Discover which machine learning tools make sense for particular problems Acquire code that can be adapted to many uses

Big Data

Big Data Author Viktor Mayer-Schönberger
ISBN-10 9780544002937
Year 2013-03-05
Pages 240
Language en
Publisher Houghton Mifflin Harcourt
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A revelatory exploration of the hottest trend in technology and the dramatic impact it will have on the economy, science, and society at large. Which paint color is most likely to tell you that a used car is in good shape? How can officials identify the most dangerous New York City manholes before they explode? And how did Google searches predict the spread of the H1N1 flu outbreak? The key to answering these questions, and many more, is big data. “Big data” refers to our burgeoning ability to crunch vast collections of information, analyze it instantly, and draw sometimes profoundly surprising conclusions from it. This emerging science can translate myriad phenomena—from the price of airline tickets to the text of millions of books—into searchable form, and uses our increasing computing power to unearth epiphanies that we never could have seen before. A revolution on par with the Internet or perhaps even the printing press, big data will change the way we think about business, health, politics, education, and innovation in the years to come. It also poses fresh threats, from the inevitable end of privacy as we know it to the prospect of being penalized for things we haven’t even done yet, based on big data’s ability to predict our future behavior. In this brilliantly clear, often surprising work, two leading experts explain what big data is, how it will change our lives, and what we can do to protect ourselves from its hazards. Big Data is the first big book about the next big thing. www.big-data-book.com

Knowledge Discovery from Data Streams

Knowledge Discovery from Data Streams Author Joao Gama
ISBN-10 9781439826126
Year 2010-05-25
Pages 255
Language en
Publisher CRC Press
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Since the beginning of the Internet age and the increased use of ubiquitous computing devices, the large volume and continuous flow of distributed data have imposed new constraints on the design of learning algorithms. Exploring how to extract knowledge structures from evolving and time-changing data, Knowledge Discovery from Data Streams presents a coherent overview of state-of-the-art research in learning from data streams. The book covers the fundamentals that are imperative to understanding data streams and describes important applications, such as TCP/IP traffic, GPS data, sensor networks, and customer click streams. It also addresses several challenges of data mining in the future, when stream mining will be at the core of many applications. These challenges involve designing useful and efficient data mining solutions applicable to real-world problems. In the appendix, the author includes examples of publicly available software and online data sets. This practical, up-to-date book focuses on the new requirements of the next generation of data mining. Although the concepts presented in the text are mainly about data streams, they also are valid for different areas of machine learning and data mining.

Storytelling with Data

Storytelling with Data Author Cole Nussbaumer Knaflic
ISBN-10 9781119002253
Year 2015-10-26
Pages 384
Language en
Publisher John Wiley & Sons
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Don't simply show your data—tell a story with it! Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You'll discover the power of storytelling and the way to make data a pivotal point in your story. The lessons in this illuminative text are grounded in theory, but made accessible through numerous real-world examples—ready for immediate application to your next graph or presentation. Storytelling is not an inherent skill, especially when it comes to data visualization, and the tools at our disposal don't make it any easier. This book demonstrates how to go beyond conventional tools to reach the root of your data, and how to use your data to create an engaging, informative, compelling story. Specifically, you'll learn how to: Understand the importance of context and audience Determine the appropriate type of graph for your situation Recognize and eliminate the clutter clouding your information Direct your audience's attention to the most important parts of your data Think like a designer and utilize concepts of design in data visualization Leverage the power of storytelling to help your message resonate with your audience Together, the lessons in this book will help you turn your data into high impact visual stories that stick with your audience. Rid your world of ineffective graphs, one exploding 3D pie chart at a time. There is a story in your data—Storytelling with Data will give you the skills and power to tell it!

Data Analysis Using SQL and Excel

Data Analysis Using SQL and Excel Author Gordon S. Linoff
ISBN-10 9780470952528
Year 2010-09-16
Pages 744
Language en
Publisher John Wiley & Sons
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Useful business analysis requires you to effectively transform data into actionable information. This book helps you use SQL and Excel to extract business information from relational databases and use that data to define business dimensions, store transactions about customers, produce results, and more. Each chapter explains when and why to perform a particular type of business analysis in order to obtain useful results, how to design and perform the analysis using SQL and Excel, and what the results should look like.

Data Modeler s Workbench

Data Modeler s Workbench Author Steve Hoberman
ISBN-10 9780471233398
Year 2002-04-22
Pages 496
Language en
Publisher John Wiley & Sons
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A goldmine of valuable tools for data modelers! Data modelers render raw data-names, addresses, and sales totals, for instance-into information such as customer profiles and seasonal buying patterns that can be used for making critical business decisions. This book brings together thirty of the most effective tools for solving common modeling problems. The author provides an example of each tool and describes what it is, why it is needed, and how it is generally used to model data for both databases and data warehouses, along with tips and warnings. Blank sample copies of all worksheets and checklists described are provided in an appendix. Companion Web site features updates on the latest tools and techniques, plus links to related sites offering automated tools.

Introduction to Data Science

Introduction to Data Science Author Laura Igual
ISBN-10 9783319500171
Year 2017-03-27
Pages 218
Language en
Publisher Springer
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This accessible and classroom-tested textbook/reference presents an introduction to the fundamentals of the emerging and interdisciplinary field of data science. The coverage spans key concepts adopted from statistics and machine learning, useful techniques for graph analysis and parallel programming, and the practical application of data science for such tasks as building recommender systems or performing sentiment analysis. Topics and features: provides numerous practical case studies using real-world data throughout the book; supports understanding through hands-on experience of solving data science problems using Python; describes techniques and tools for statistical analysis, machine learning, graph analysis, and parallel programming; reviews a range of applications of data science, including recommender systems and sentiment analysis of text data; provides supplementary code resources and data at an associated website.

Bio Inspired Credit Risk Analysis

Bio Inspired Credit Risk Analysis Author Lean Yu
ISBN-10 3540778039
Year 2008-04-24
Pages 244
Language en
Publisher Springer Science & Business Media
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Credit risk analysis is one of the most important topics in the field of financial risk management. Due to recent financial crises and regulatory concern of Basel II, credit risk analysis has been the major focus of financial and banking industry. Especially for some credit-granting institutions such as commercial banks and credit companies, the ability to discriminate good customers from bad ones is crucial. The need for reliable quantitative models that predict defaults accurately is imperative so that the interested parties can take either preventive or corrective action. Hence credit risk analysis becomes very important for sustainability and profit of enterprises. In such backgrounds, this book tries to integrate recent emerging support vector machines and other computational intelligence techniques that replicate the principles of bio-inspired information processing to create some innovative methodologies for credit risk analysis and to provide decision support information for interested parties.