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Bad Data

Bad Data
Author: Peter Schryvers
Publisher: Rowman & Littlefield
Total Pages: 353
Release: 2020-01-10
Genre: Business & Economics
ISBN: 1633885917

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Highlights the pitfalls of data analysis and emphasizes the importance of using the appropriate metrics before making key decisions.Big data is often touted as the key to understanding almost every aspect of contemporary life. This critique of "information hubris" shows that even more important than data is finding the right metrics to evaluate it.The author, an expert in environmental design and city planning, examines the many ways in which we measure ourselves and our world. He dissects the metrics we apply to health, worker productivity, our children's education, the quality of our environment, the effectiveness of leaders, the dynamics of the economy, and the overall well-being of the planet. Among the areas where the wrong metrics have led to poor outcomes, he cites the fee-for-service model of health care, corporate cultures that emphasize time spent on the job while overlooking key productivity measures, overreliance on standardized testing in education to the detriment of authentic learning, and a blinkered focus on carbon emissions, which underestimates the impact of industrial damage to our natural world. He also examines various communities and systems that have achieved better outcomes by adjusting the ways in which they measure data. The best results are attained by those that have learned not only what to measure and how to measure it, but what it all means. By highlighting the pitfalls inherent in data analysis, this illuminating book reminds us that not everything that can be counted really counts.


Bad Data Handbook

Bad Data Handbook
Author: Q. Ethan McCallum
Publisher: "O'Reilly Media, Inc."
Total Pages: 265
Release: 2012-11-07
Genre: Computers
ISBN: 1449324975

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What is bad data? Some people consider it a technical phenomenon, like missing values or malformed records, but bad data includes a lot more. In this handbook, data expert Q. Ethan McCallum has gathered 19 colleagues from every corner of the data arena to reveal how they’ve recovered from nasty data problems. From cranky storage to poor representation to misguided policy, there are many paths to bad data. Bottom line? Bad data is data that gets in the way. This book explains effective ways to get around it. Among the many topics covered, you’ll discover how to: Test drive your data to see if it’s ready for analysis Work spreadsheet data into a usable form Handle encoding problems that lurk in text data Develop a successful web-scraping effort Use NLP tools to reveal the real sentiment of online reviews Address cloud computing issues that can impact your analysis effort Avoid policies that create data analysis roadblocks Take a systematic approach to data quality analysis


Learning from Good and Bad Data

Learning from Good and Bad Data
Author: Philip D. Laird
Publisher: Springer Science & Business Media
Total Pages: 223
Release: 2012-12-06
Genre: Computers
ISBN: 1461316855

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This monograph is a contribution to the study of the identification problem: the problem of identifying an item from a known class us ing positive and negative examples. This problem is considered to be an important component of the process of inductive learning, and as such has been studied extensively. In the overview we shall explain the objectives of this work and its place in the overall fabric of learning research. Context. Learning occurs in many forms; the only form we are treat ing here is inductive learning, roughly characterized as the process of forming general concepts from specific examples. Computer Science has found three basic approaches to this problem: • Select a specific learning task, possibly part of a larger task, and construct a computer program to solve that task . • Study cognitive models of learning in humans and extrapolate from them general principles to explain learning behavior. Then construct machine programs to test and illustrate these models. xi Xll PREFACE • Formulate a mathematical theory to capture key features of the induction process. This work belongs to the third category. The various studies of learning utilize training examples (data) in different ways. The three principal ones are: • Similarity-based (or empirical) learning, in which a collection of examples is used to select an explanation from a class of possible rules.


Bad Data

Bad Data
Author: Georgina Sturge
Publisher: Hachette UK
Total Pages: 311
Release: 2022-11-03
Genre: Social Science
ISBN: 034912860X

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'Essential reading ... An incisive and urgently needed book' Tim Harford '[An] entertaining introduction to the uses (and misuses) of data ... a penetrating analysis of why statistical literacy matters to our politics and our daily lives' Professor Jonathan Portes Our politicians make vital decisions and declarations every day that rely on official data. But should all statistics be trusted? In BAD DATA, House of Commons Library statistician Georgina Sturge draws back the curtain on how governments of the past and present have been led astray by figures littered with inconsistency, guesswork and uncertainty. Discover how a Hungarian businessman's bright idea caused half a million people to go missing from UK migration statistics. Find out why it's possible for two politicians to disagree over whether poverty has gone up or down, using the same official numbers, and for both to be right at the same time. And hear about how policies like ID cards, super-casinos and stopping ex-convicts from reoffending failed to live up to their promise because they were based on shaky data. With stories that range from the troubling to the empowering to the downright absurd, BAD DATA reveals secrets from the usually closed-off world of policy-making. It also suggests how - once we understand the human story behind the numbers - we can make more informed choices about who to trust, and when.


Storytelling with Data

Storytelling with Data
Author: Cole Nussbaumer Knaflic
Publisher: John Wiley & Sons
Total Pages: 284
Release: 2015-10-09
Genre: Mathematics
ISBN: 1119002265

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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!


Statistics Done Wrong

Statistics Done Wrong
Author: Alex Reinhart
Publisher: No Starch Press
Total Pages: 177
Release: 2015-03-01
Genre: Mathematics
ISBN: 1593276737

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Scientific progress depends on good research, and good research needs good statistics. But statistical analysis is tricky to get right, even for the best and brightest of us. You'd be surprised how many scientists are doing it wrong. Statistics Done Wrong is a pithy, essential guide to statistical blunders in modern science that will show you how to keep your research blunder-free. You'll examine embarrassing errors and omissions in recent research, learn about the misconceptions and scientific politics that allow these mistakes to happen, and begin your quest to reform the way you and your peers do statistics. You'll find advice on: –Asking the right question, designing the right experiment, choosing the right statistical analysis, and sticking to the plan –How to think about p values, significance, insignificance, confidence intervals, and regression –Choosing the right sample size and avoiding false positives –Reporting your analysis and publishing your data and source code –Procedures to follow, precautions to take, and analytical software that can help Scientists: Read this concise, powerful guide to help you produce statistically sound research. Statisticians: Give this book to everyone you know. The first step toward statistics done right is Statistics Done Wrong.


Bad Data Handbook

Bad Data Handbook
Author: Q. Ethan McCallum
Publisher: "O'Reilly Media, Inc."
Total Pages: 265
Release: 2012-11-14
Genre: Computers
ISBN: 1449321887

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"Mapping the world of data problems"--Cover.


The Truth about Transformation

The Truth about Transformation
Author: Kevin Novak
Publisher: Kevin Novak
Total Pages: 270
Release: 2023-07-22
Genre: Business & Economics
ISBN:

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“Any organization that seeks transformation desires to take advantage of new opportunities and growth. Most organizations turn to technology as the major driver of change. But technology is an enabler, not a silver bullet. Mistaking technology for transformation will lead an organization to failure. True transformative change requires an understanding of the human factors at play, how conscious and subconscious behaviors can derail any plan, and how society is influencing your organization. Change is the only constant. An evolving reality is emerging, one that will fundamentally change who we are, how we work, and how organizations will be relevant today and in the future. The truth about transformation is not what you may think. This guide to organizational transformation will surprise, confound, provoke, and challenge every ingrained belief. The future is out there, and the truth about transformation will change how you lead.”


Smart Grid

Smart Grid
Author: James A. Momoh
Publisher: John Wiley & Sons
Total Pages: 234
Release: 2012-03-20
Genre: Technology & Engineering
ISBN: 047088939X

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The book is written as primer hand book for addressing the fundamentals of smart grid. It provides the working definition the functions, the design criteria and the tools and techniques and technology needed for building smart grid. The book is needed to provide a working guideline in the design, analysis and development of Smart Grid. It incorporates all the essential factors of Smart Grid appropriate for enabling the performance and capability of the power system. There are no comparable books which provide information on the “how to” of the design and analysis. The book provides a fundamental discussion on the motivation for the smart grid development, the working definition and the tools for analysis and development of the Smart Grid. Standards and requirements needed for designing new devices, systems and products are discussed; the automation and computational techniques need to ensure that the Smart Grid guarantees adaptability, foresight alongside capability of handling new systems and components are discussed. The interoperability of different renewable energy sources are included to ensure that there will be minimum changes in the existing legacy system. Overall the book evaluates different options of computational intelligence, communication technology and decision support system to design various aspects of Smart Grid. Strategies for demonstration of Smart Grid schemes on selected problems are presented.


Unlearning SQL

Unlearning SQL
Author: Steven F. Lott
Publisher: Steven Lott
Total Pages: 95
Release: 2024-09-10
Genre: Computers
ISBN:

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SQL is a powerful, useful way to access data; it's an essential ingredient in many databases. However, it's rarely suitable for bulk data processing that happens at the edge of the database: loads and extractions. For these operations, Python presents a much better solution than a script of SQL statements.