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Best Practices In Data Cleaning

Author: Jason W. Osborne
Publisher: SAGE
ISBN: 1412988012
Size: 59.97 MB
Format: PDF, Docs
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Many researchers jump from data collection directly into testing hypothesis without realizing these tests can go profoundly wrong without clean data. This book provides a clear, accessible, step-by-step process of important best practices in preparing for data collection, testing assumptions, and examining and cleaning data in order to decrease error rates and increase both the power and replicability of results. Jason W. Osborne, author of the handbook Best Practices in Quantitative Methods (SAGE, 2008) provides easily-implemented suggestions that are evidence-based and will motivate change in practice by empirically demonstrating—for each topic—the benefits of following best practices and the potential consequences of not following these guidelines.

Best Practices In Data Cleaning

Author: Jason W. Osborne
Publisher: SAGE Publications
ISBN: 1452289670
Size: 76.97 MB
Format: PDF, ePub, Mobi
View: 5014
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Many researchers jump from data collection directly into testing hypothesis without realizing these tests can go profoundly wrong without clean data. This book provides a clear, accessible, step-by-step process of important best practices in preparing for data collection, testing assumptions, and examining and cleaning data in order to decrease error rates and increase both the power and replicability of results. Jason W. Osborne, author of the handbook Best Practices in Quantitative Methods (SAGE, 2008) provides easily-implemented suggestions that are evidence-based and will motivate change in practice by empirically demonstrating—for each topic—the benefits of following best practices and the potential consequences of not following these guidelines.

Best Practices In Data Cleaning

Author: Jason W. Osborne
Publisher: SAGE Publications
ISBN: 1452281041
Size: 61.55 MB
Format: PDF, ePub, Docs
View: 4203
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Many researchers jump from data collection directly into testing hypothesis without realizing these tests can go profoundly wrong without clean data. This book provides a clear, accessible, step-by-step process of important best practices in preparing for data collection, testing assumptions, and examining and cleaning data in order to decrease error rates and increase both the power and replicability of results. Jason W. Osborne, author of the handbook Best Practices in Quantitative Methods (SAGE, 2008) provides easily-implemented suggestions that are evidence-based and will motivate change in practice by empirically demonstrating—for each topic—the benefits of following best practices and the potential consequences of not following these guidelines.

Exploratory Data Mining And Data Cleaning

Author: Tamraparni Dasu
Publisher: John Wiley & Sons
ISBN: 0471458643
Size: 48.18 MB
Format: PDF, Kindle
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Written for practitioners of data mining, data cleaning and database management. Presents a technical treatment of data quality including process, metrics, tools and algorithms. Focuses on developing an evolving modeling strategy through an iterative data exploration loop and incorporation of domain knowledge. Addresses methods of detecting, quantifying and correcting data quality issues that can have a significant impact on findings and decisions, using commercially available tools as well as new algorithmic approaches. Uses case studies to illustrate applications in real life scenarios. Highlights new approaches and methodologies, such as the DataSphere space partitioning and summary based analysis techniques. Exploratory Data Mining and Data Cleaning will serve as an important reference for serious data analysts who need to analyze large amounts of unfamiliar data, managers of operations databases, and students in undergraduate or graduate level courses dealing with large scale data analys is and data mining.

Bad Data Handbook

Author: Q. Ethan McCallum
Publisher: "O'Reilly Media, Inc."
ISBN: 1449324975
Size: 52.62 MB
Format: PDF, ePub
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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

Life After College

Author: Jenny Blake
Publisher: Running Press Adult
ISBN: 0762446056
Size: 54.19 MB
Format: PDF, ePub, Docs
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Just graduated? Feeling a little lost? Life After College is like a portable life coach, giving you straightforward guidance on maneuvering the real world--along with tips, inspiration, and exercises for getting you where you want to go. Congrats, you've graduated! You have your whole life ahead of you. Do you feel overwhelmed? Unsure? Deluged with information, but no real plan? Jenny Blake's Life After College gives you practical, actionable advice, helping you to navigate every area of your life--from work, money, dating, health, family, and personal growth--to help you see the big picture. It will get you focusing on your goals, dreams, and highest aspirations so that you can create the life you really want. Now in a repackaged edition!

Clean Data

Author: Megan Squire
Publisher: Packt Publishing Ltd
ISBN: 1785289039
Size: 21.69 MB
Format: PDF, Docs
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If you are a data scientist of any level, beginners included, and interested in cleaning up your data, this is the book for you! Experience with Python or PHP is assumed, but no previous knowledge of data cleaning is needed.

Best Practices In Quantitative Methods

Author: Jason W. Osborne
Publisher: SAGE
ISBN: 1412940656
Size: 22.40 MB
Format: PDF, Kindle
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The contributors to Best Practices in Quantitative Methods envision quantitative methods in the 21st century, identify the best practices, and, where possible, demonstrate the superiority of their recommendations empirically. Editor Jason W. Osborne designed this book with the goal of providing readers with the most effective, evidence-based, modern quantitative methods and quantitative data analysis across the social and behavioral sciences. The text is divided into five main sections covering select best practices in Measurement, Research Design, Basics of Data Analysis, Quantitative Methods, and Advanced Quantitative Methods. Each chapter contains a current and expansive review of the literature, a case for best practices in terms of method, outcomes, inferences, etc., and broad-ranging examples along with any empirical evidence to show why certain techniques are better. Key Features: Describes important implicit knowledge to readers: The chapters in this volume explain the important details of seemingly mundane aspects of quantitative research, making them accessible to readers and demonstrating why it is important to pay attention to these details. Compares and contrasts analytic techniques: The book examines instances where there are multiple options for doing things, and make recommendations as to what is the "best" choice—or choices, as what is best often depends on the circumstances. Offers new procedures to update and explicate traditional techniques: The featured scholars present and explain new options for data analysis, discussing the advantages and disadvantages of the new procedures in depth, describing how to perform them, and demonstrating their use. Intended Audience: Representing the vanguard of research methods for the 21st century, this book is an invaluable resource for graduate students and researchers who want a comprehensive, authoritative resource for practical and sound advice from leading experts in quantitative methods.

Registries For Evaluating Patient Outcomes

Author: Agency for Healthcare Research and Quality/AHRQ
Publisher: Government Printing Office
ISBN: 1587634333
Size: 49.69 MB
Format: PDF, ePub
View: 1957
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This User’s Guide is intended to support the design, implementation, analysis, interpretation, and quality evaluation of registries created to increase understanding of patient outcomes. For the purposes of this guide, a patient registry is an organized system that uses observational study methods to collect uniform data (clinical and other) to evaluate specified outcomes for a population defined by a particular disease, condition, or exposure, and that serves one or more predetermined scientific, clinical, or policy purposes. A registry database is a file (or files) derived from the registry. Although registries can serve many purposes, this guide focuses on registries created for one or more of the following purposes: to describe the natural history of disease, to determine clinical effectiveness or cost-effectiveness of health care products and services, to measure or monitor safety and harm, and/or to measure quality of care. Registries are classified according to how their populations are defined. For example, product registries include patients who have been exposed to biopharmaceutical products or medical devices. Health services registries consist of patients who have had a common procedure, clinical encounter, or hospitalization. Disease or condition registries are defined by patients having the same diagnosis, such as cystic fibrosis or heart failure. The User’s Guide was created by researchers affiliated with AHRQ’s Effective Health Care Program, particularly those who participated in AHRQ’s DEcIDE (Developing Evidence to Inform Decisions About Effectiveness) program. Chapters were subject to multiple internal and external independent reviews.

Kafka The Definitive Guide

Author: Neha Narkhede
Publisher: "O'Reilly Media, Inc."
ISBN: 1491936118
Size: 27.39 MB
Format: PDF
View: 3254
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Every enterprise application creates data, whether it’s log messages, metrics, user activity, outgoing messages, or something else. And how to move all of this data becomes nearly as important as the data itself. If you’re an application architect, developer, or production engineer new to Apache Kafka, this practical guide shows you how to use this open source streaming platform to handle real-time data feeds. Engineers from Confluent and LinkedIn who are responsible for developing Kafka explain how to deploy production Kafka clusters, write reliable event-driven microservices, and build scalable stream-processing applications with this platform. Through detailed examples, you’ll learn Kafka’s design principles, reliability guarantees, key APIs, and architecture details, including the replication protocol, the controller, and the storage layer. Understand publish-subscribe messaging and how it fits in the big data ecosystem. Explore Kafka producers and consumers for writing and reading messages Understand Kafka patterns and use-case requirements to ensure reliable data delivery Get best practices for building data pipelines and applications with Kafka Manage Kafka in production, and learn to perform monitoring, tuning, and maintenance tasks Learn the most critical metrics among Kafka’s operational measurements Explore how Kafka’s stream delivery capabilities make it a perfect source for stream processing systems