Learning from Good and Bad Data
Learning from Good and Bad Data

Author: Philip D. Laird

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 223

ISBN-13: 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 Handbook
Bad Data Handbook

Author: Q. Ethan McCallum

Publisher: "O'Reilly Media, Inc."

Published: 2012-11-07

Total Pages: 265

ISBN-13: 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

Storytelling with Data
Storytelling with Data

Author: Cole Nussbaumer Knaflic

Publisher: John Wiley & Sons

Published: 2015-10-09

Total Pages: 284

ISBN-13: 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!

How to Lie with Statistics
How to Lie with Statistics

Author: Darrell Huff

Publisher: W. W. Norton & Company

Published: 2010-12-07

Total Pages: 144

ISBN-13: 0393070875

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If you want to outsmart a crook, learn his tricks—Darrell Huff explains exactly how in the classic How to Lie with Statistics. From distorted graphs and biased samples to misleading averages, there are countless statistical dodges that lend cover to anyone with an ax to grind or a product to sell. With abundant examples and illustrations, Darrell Huff’s lively and engaging primer clarifies the basic principles of statistics and explains how they’re used to present information in honest and not-so-honest ways. Now even more indispensable in our data-driven world than it was when first published, How to Lie with Statistics is the book that generations of readers have relied on to keep from being fooled.

Data Visualization
Data Visualization

Author: Kieran Healy

Publisher: Princeton University Press

Published: 2018-12-18

Total Pages: 292

ISBN-13: 0691181624

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An accessible primer on how to create effective graphics from data This book provides students and researchers a hands-on introduction to the principles and practice of data visualization. It explains what makes some graphs succeed while others fail, how to make high-quality figures from data using powerful and reproducible methods, and how to think about data visualization in an honest and effective way. Data Visualization builds the reader’s expertise in ggplot2, a versatile visualization library for the R programming language. Through a series of worked examples, this accessible primer then demonstrates how to create plots piece by piece, beginning with summaries of single variables and moving on to more complex graphics. Topics include plotting continuous and categorical variables; layering information on graphics; producing effective “small multiple” plots; grouping, summarizing, and transforming data for plotting; creating maps; working with the output of statistical models; and refining plots to make them more comprehensible. Effective graphics are essential to communicating ideas and a great way to better understand data. This book provides the practical skills students and practitioners need to visualize quantitative data and get the most out of their research findings. Provides hands-on instruction using R and ggplot2 Shows how the “tidyverse” of data analysis tools makes working with R easier and more consistent Includes a library of data sets, code, and functions

Data Science from Scratch
Data Science from Scratch

Author: Joel Grus

Publisher: "O'Reilly Media, Inc."

Published: 2015-04-14

Total Pages: 336

ISBN-13: 1491904399

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Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. 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

Bad Data
Bad Data

Author: Peter Schryvers

Publisher: Rowman & Littlefield

Published: 2020-01-10

Total Pages: 353

ISBN-13: 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.

Learning from Data
Learning from Data

Author: Doug Fisher

Publisher: Springer Science & Business Media

Published: 2012-12-06

Total Pages: 444

ISBN-13: 1461224047

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Ten years ago Bill Gale of AT&T Bell Laboratories was primary organizer of the first Workshop on Artificial Intelligence and Statistics. In the early days of the Workshop series it seemed clear that researchers in AI and statistics had common interests, though with different emphases, goals, and vocabularies. In learning and model selection, for example, a historical goal of AI to build autonomous agents probably contributed to a focus on parameter-free learning systems, which relied little on an external analyst's assumptions about the data. This seemed at odds with statistical strategy, which stemmed from a view that model selection methods were tools to augment, not replace, the abilities of a human analyst. Thus, statisticians have traditionally spent considerably more time exploiting prior information of the environment to model data and exploratory data analysis methods tailored to their assumptions. In statistics, special emphasis is placed on model checking, making extensive use of residual analysis, because all models are 'wrong', but some are better than others. It is increasingly recognized that AI researchers and/or AI programs can exploit the same kind of statistical strategies to good effect. Often AI researchers and statisticians emphasized different aspects of what in retrospect we might now regard as the same overriding tasks.

Java Application Frameworks
Java Application Frameworks

Author: Darren Govoni

Publisher: John Wiley & Sons

Published: 1999-06-25

Total Pages: 440

ISBN-13:

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A complete guide to designing and using frameworks for Java applications. Java(TM) Application Frameworks. Application frameworks are a major leap forward in systems design and software reusability. Java has provided an enormous foundation upon which highly reusable components and applications can be built successfully. Building object-oriented systems is complex. Building highly reusable frameworks and components is even more challenging. This book substantially reduces the application frameworks' learning curve. Expert Darren Govoni follows a natural progression, from concept, to practice, to implementation. Building on examples of existing frameworks, he walks you through all the steps involved in designing frameworks, and provides guidelines on how to use frameworks within large architectures and systems. Important topics covered include: * Basic framework concepts and design techniques. * Using 8 design patterns-illustrated with UML-for framework design and implementation. * Constructing JavaBean components as the building blocks for a reusableframework . * A complete discussion of two powerful Java frameworks-Java Foundation Classes (JFC/Swing) and InfoBus-and how they adhere to the important traits found in good frameworks: proper use of abstraction through interfaces and abstract classes, substitution of application objects, extension or enhancement of key objects within the framework through abstract and default implementations. * How to manage complexity and reusability with abstracted foundations. * A complete methodology and architecture-Composite Foundation Architecture-for organizing and developing frameworks, components, and subsystems within a larger complex system. * Key considerations for developing frameworks within distributed architectures, including data access, GUIs, business objects, and distributed objects using JDBC, JFC, and RMI. * How to use enterprise frameworks such as Enterprise JavaBeans and CORBA (via Java IDL) to access, retrieve, and store information across a network. Visit the Companion Web site at www.wiley.com/compbooks/govoni/ for: * Source code from the application objects presented in this book. * Links to more information on frameworks.

The Good, the Bad, and the Data
The Good, the Bad, and the Data

Author: Sally Campbell Galman

Publisher: Routledge

Published: 2013

Total Pages: 0

ISBN-13: 9781598746327

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An entertaining introductory guide to conducting qualitative data analysis in comic book format, following the character of Shane the Lone Ethnographer.