Download or read book Principles and methods of data cleaning written by Arthur D. Chapman and published by GBIF. This book was released on 2005 with total page 75 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Principles and Methods of Data Cleaning written by Arthur D. Chapman and published by . This book was released on 2005 with total page 72 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Principles of Data Quality written by Arthur D. Chapman and published by GBIF. This book was released on 2005 with total page 61 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Educational Data Analytics for Teachers and School Leaders written by Sofia Mougiakou and published by Springer Nature. This book was released on 2022-10-28 with total page 249 pages. Available in PDF, EPUB and Kindle. Book excerpt: Educational Data Analytics (EDA) have been attributed with significant benefits for enhancing on-demand personalized educational support of individual learners as well as reflective course (re)design for achieving more authentic teaching, learning and assessment experiences integrated into real work-oriented tasks. This open access textbook is a tutorial for developing, practicing and self-assessing core competences on educational data analytics for digital teaching and learning. It combines theoretical knowledge on core issues related to collecting, analyzing, interpreting and using educational data, including ethics and privacy concerns. The textbook provides questions and teaching materials/ learning activities as quiz tests of multiple types of questions, added after each section, related to the topic studied or the video(s) referenced. These activities reproduce real-life contexts by using a suitable use case scenario (storytelling), encouraging learners to link theory with practice; self-assessed assignments enabling learners to apply their attained knowledge and acquired competences on EDL. By studying this book, you will know where to locate useful educational data in different sources and understand their limitations; know the basics for managing educational data to make them useful; understand relevant methods; and be able to use relevant tools; know the basics for organising, analysing, interpreting and presenting learner-generated data within their learning context, understand relevant learning analytics methods and be able to use relevant learning analytics tools; know the basics for analysing and interpreting educational data to facilitate educational decision making, including course and curricula design, understand relevant teaching analytics methods and be able to use relevant teaching analytics tools; understand issues related with educational data ethics and privacy. This book is intended for school leaders and teachers engaged in blended (using the flipped classroom model) and online (during COVID-19 crisis and beyond) teaching and learning; e-learning professionals (such as, instructional designers and e-tutors) of online and blended courses; instructional technologists; researchers as well as undergraduate and postgraduate university students studying education, educational technology and relevant fields.

Download or read book Gene Patents and Collaborative Licensing Models written by Geertrui van Overwalle and published by Cambridge University Press. This book was released on 2009-06-11 with total page 517 pages. Available in PDF, EPUB and Kindle. Book excerpt: The cost of patent licenses needed to design a new genetic test or treatment may ultimately prevent research projects getting started, as individual components are protected by different patent owners. This book examines legal measures which might be used to solve the problem of fragmentation of patents in genetics.

Download or read book Sweating the Small Stuff Does data cleaning and testing of assumptions really matter in the 21st century written by and published by Frontiers E-books. This book was released on with total page 158 pages. Available in PDF, EPUB and Kindle. Book excerpt:

Download or read book Statistics and Machine Learning Methods for EHR Data written by Hulin Wu and published by CRC Press. This book was released on 2020-12-10 with total page 268 pages. Available in PDF, EPUB and Kindle. Book excerpt: The use of Electronic Health Records (EHR)/Electronic Medical Records (EMR) data is becoming more prevalent for research. However, analysis of this type of data has many unique complications due to how they are collected, processed and types of questions that can be answered. This book covers many important topics related to using EHR/EMR data for research including data extraction, cleaning, processing, analysis, inference, and predictions based on many years of practical experience of the authors. The book carefully evaluates and compares the standard statistical models and approaches with those of machine learning and deep learning methods and reports the unbiased comparison results for these methods in predicting clinical outcomes based on the EHR data. Key Features: Written based on hands-on experience of contributors from multidisciplinary EHR research projects, which include methods and approaches from statistics, computing, informatics, data science and clinical/epidemiological domains. Documents the detailed experience on EHR data extraction, cleaning and preparation Provides a broad view of statistical approaches and machine learning prediction models to deal with the challenges and limitations of EHR data. Considers the complete cycle of EHR data analysis. The use of EHR/EMR analysis requires close collaborations between statisticians, informaticians, data scientists and clinical/epidemiological investigators. This book reflects that multidisciplinary perspective.

Download or read book Resources for Nursing Research written by Cynthia Clamp and published by SAGE. This book was released on 2005-01-11 with total page 432 pages. Available in PDF, EPUB and Kindle. Book excerpt: ′The 4th edition of this extensive text is an outstanding resource prepared by nurses (and a librarian) for nurses. In a structured and helpful style it presents thousands of items from the literature - published papers, reports, books and electronic resources - as a clear, accessible, and most of all useful collection. The efforts to signpost and lead the reader to the sought-for information are effective and well-conceived, and the "How to use this book" section is remarkably simple...the book should be found in every nursing and health library, every research institute and centre, and close to many career researchers′ desks′ - RCN Research This latest edition of Resources for Nursing Research provides a comprehensive bibliography of sources on nursing research, and includes references for books, journal papers and Internet resources. Designed to act as a ′signpost′ to available literature in the area, this Fourth Edition covers the disciplines of nursing, health care and the social sciences. Entries are concise, informative and accessible, and are arranged under three main sections: · ′Sources of Literature′ covers the process of literature searching, including using libraries and other tools for accessing literature · ′Methods of Inquiry′ includes an introduction to research, how to conceptualize and design nursing and health research, measurement and data collection, and the interpretation and presentation of data · ′The Background to Research in Nursing′ encompasses the development of nursing research; the profession′s responsibilities; the role of government; funding; research roles and careers; and education for research. Fully revised and updated, the Fourth Edition includes just under 3000 entries, of which 90% are new. It has extensive coverage of US, UK literature and other international resources. This new edition will be an essential guide for all those with an interest in nursing research, including students, teachers, librarians, practitioners and researchers.

Download or read book Cody s Data Cleaning Techniques Using SAS Third Edition written by Ron Cody and published by SAS Institute. This book was released on 2017-03-15 with total page 234 pages. Available in PDF, EPUB and Kindle. Book excerpt: Written in Ron Cody's signature informal, tutorial style, this book develops and demonstrates data cleaning programs and macros that you can use as written or modify which will make your job of data cleaning easier, faster, and more efficient. --

Download or read book e Learning e Education and Online Training written by Guan Gui and published by Springer Nature. This book was released on 2024-01-13 with total page 482 pages. Available in PDF, EPUB and Kindle. Book excerpt: This four-volume set constitutes the post-conference proceedings of the 9th EAI International Conference on e-Learning, e-Education, and Online Training, eLEOT 2023, held in Yantai, China, during August 17-18, 2023. The 104 full papers presented were selected from 260 submissions. The papers reflect the evolving landscape of education in the digital age. They were organized in topical sections as follows: IT promoted teaching platforms and systems; AI based educational modes and methods; automatic educational resource processing; educational information evaluation.

Download or read book Progress in Advanced Information and Communication Technology and Systems written by Mykhailo Ilchenko and published by Springer Nature. This book was released on 2022-11-17 with total page 602 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book highlights the most important research areas in information and communication technologies, namely the research in fields of modern information technologies that deal with various aspects of the analysis and solution of practically important issues of information systems in general, and contains discussion about the progression from big data to smart data, development of cloud-based architecture, practical implementation of Internet of Things (IoT), the fundamentals of information and analytical activities; studying of modern communication technologies contains original works dealing with many aspects of construction, using research and forecasting of technological and services characteristics of communication systems, as well as research of modern radio electronics technologies that contains actual papers, which show some effective technological solutions that can be used for the implementation of novel radio electronics systems. These results can be used in the implementation of novel systems and to promote information exchange in e-societies. This book offers a valuable resource for scientists, lecturers, specialists working at enterprises, and graduate and undergraduate students who engage with problems in information and communication technologies.

Download or read book Principles of Data Management and Presentation written by John P. Hoffmann and published by Univ of California Press. This book was released on 2017-07-03 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Why research? -- Developing research questions -- Data -- Principles of data management -- Finding and using secondary data -- Primary and administrative data -- Working with missing data -- Principles of data presentation -- Designing tables for data presentations -- Designing graphics for data presentations

Download or read book Principles of Data Science written by Sinan Ozdemir and published by Packt Publishing Ltd. This book was released on 2016-12-16 with total page 389 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn the techniques and math you need to start making sense of your data About This Book Enhance your knowledge of coding with data science theory for practical insight into data science and analysis More than just a math class, learn how to perform real-world data science tasks with R and Python Create actionable insights and transform raw data into tangible value Who This Book Is For You should be fairly well acquainted with basic algebra and should feel comfortable reading snippets of R/Python as well as pseudo code. You should have the urge to learn and apply the techniques put forth in this book on either your own data sets or those provided to you. If you have the basic math skills but want to apply them in data science or you have good programming skills but lack math, then this book is for you. What You Will Learn Get to know the five most important steps of data science Use your data intelligently and learn how to handle it with care Bridge the gap between mathematics and programming Learn about probability, calculus, and how to use statistical models to control and clean your data and drive actionable results Build and evaluate baseline machine learning models Explore the most effective metrics to determine the success of your machine learning models Create data visualizations that communicate actionable insights Read and apply machine learning concepts to your problems and make actual predictions In Detail Need to turn your skills at programming into effective data science skills? Principles of Data Science is created to help you join the dots between mathematics, programming, and business analysis. With this book, you'll feel confident about asking—and answering—complex and sophisticated questions of your data to move from abstract and raw statistics to actionable ideas. With a unique approach that bridges the gap between mathematics and computer science, this books takes you through the entire data science pipeline. Beginning with cleaning and preparing data, and effective data mining strategies and techniques, you'll move on to build a comprehensive picture of how every piece of the data science puzzle fits together. Learn the fundamentals of computational mathematics and statistics, as well as some pseudocode being used today by data scientists and analysts. You'll get to grips with machine learning, discover the statistical models that help you take control and navigate even the densest datasets, and find out how to create powerful visualizations that communicate what your data means. Style and approach This is an easy-to-understand and accessible tutorial. It is a step-by-step guide with use cases, examples, and illustrations to get you well-versed with the concepts of data science. Along with explaining the fundamentals, the book will also introduce you to slightly advanced concepts later on and will help you implement these techniques in the real world.

Download or read book Encyclopedia of Big Data written by Laurie A. Schintler and published by Springer. This book was released on 2022-02-23 with total page 0 pages. Available in PDF, EPUB and Kindle. Book excerpt: This encyclopedia will be an essential resource for our times, reflecting the fact that we currently are living in an expanding data-driven world. Technological advancements and other related trends are contributing to the production of an astoundingly large and exponentially increasing collection of data and information, referred to in popular vernacular as “Big Data.” Social media and crowdsourcing platforms and various applications ― “apps” ― are producing reams of information from the instantaneous transactions and input of millions and millions of people around the globe. The Internet-of-Things (IoT), which is expected to comprise tens of billions of objects by the end of this decade, is actively sensing real-time intelligence on nearly every aspect of our lives and environment. The Global Positioning System (GPS) and other location-aware technologies are producing data that is specific down to particular latitude and longitude coordinates and seconds of the day. Large-scale instruments, such as the Large Hadron Collider (LHC), are collecting massive amounts of data on our planet and even distant corners of the visible universe. Digitization is being used to convert large collections of documents from print to digital format, giving rise to large archives of unstructured data. Innovations in technology, in the areas of Cloud and molecular computing, Artificial Intelligence/Machine Learning, and Natural Language Processing (NLP), to name only a few, also are greatly expanding our capacity to store, manage, and process Big Data. In this context, the Encyclopedia of Big Data is being offered in recognition of a world that is rapidly moving from gigabytes to terabytes to petabytes and beyond. While indeed large data sets have long been around and in use in a variety of fields, the era of Big Data in which we now live departs from the past in a number of key respects and with this departure comes a fresh set of challenges and opportunities that cut across and affect multiple sectors and disciplines, and the public at large. With expanded analytical capacities at hand, Big Data is now being used for scientific inquiry and experimentation in nearly every (if not all) disciplines, from the social sciences to the humanities to the natural sciences, and more. Moreover, the use of Big Data has been well established beyond the Ivory Tower. In today’s economy, businesses simply cannot be competitive without engaging Big Data in one way or another in support of operations, management, planning, or simply basic hiring decisions. In all levels of government, Big Data is being used to engage citizens and to guide policy making in pursuit of the interests of the public and society in general. Moreover, the changing nature of Big Data also raises new issues and concerns related to, for example, privacy, liability, security, access, and even the veracity of the data itself. Given the complex issues attending Big Data, there is a real need for a reference book that covers the subject from a multi-disciplinary, cross-sectoral, comprehensive, and international perspective. The Encyclopedia of Big Data will address this need and will be the first of such reference books to do so. Featuring some 500 entries, from "Access" to "Zillow," the Encyclopedia will serve as a fundamental resource for researchers and students, for decision makers and leaders, and for business analysts and purveyors. Developed for those in academia, industry, and government, and others with a general interest in Big Data, the encyclopedia will be aimed especially at those involved in its collection, analysis, and use. Ultimately, the Encyclopedia of Big Data will provide a common platform and language covering the breadth and depth of the topic for different segments, sectors, and disciplines.

Download or read book Principles of Data Mining written by Max Bramer and published by Springer. This book was released on 2016-11-09 with total page 526 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book explains and explores the principal techniques of Data Mining, the automatic extraction of implicit and potentially useful information from data, which is increasingly used in commercial, scientific and other application areas. It focuses on classification, association rule mining and clustering. Each topic is clearly explained, with a focus on algorithms not mathematical formalism, and is illustrated by detailed worked examples. The book is written for readers without a strong background in mathematics or statistics and any formulae used are explained in detail. It can be used as a textbook to support courses at undergraduate or postgraduate levels in a wide range of subjects including Computer Science, Business Studies, Marketing, Artificial Intelligence, Bioinformatics and Forensic Science. As an aid to self study, this book aims to help general readers develop the necessary understanding of what is inside the 'black box' so they can use commercial data mining packages discriminatingly, as well as enabling advanced readers or academic researchers to understand or contribute to future technical advances in the field. Each chapter has practical exercises to enable readers to check their progress. A full glossary of technical terms used is included. This expanded third edition includes detailed descriptions of algorithms for classifying streaming data, both stationary data, where the underlying model is fixed, and data that is time-dependent, where the underlying model changes from time to time - a phenomenon known as concept drift.

Download or read book Computational Analysis and Understanding of Natural Languages Principles Methods and Applications written by and published by Elsevier. This book was released on 2018-08-27 with total page 537 pages. Available in PDF, EPUB and Kindle. Book excerpt: Computational Analysis and Understanding of Natural Languages: Principles, Methods and Applications, Volume 38, the latest release in this monograph that provides a cohesive and integrated exposition of these advances and associated applications, includes new chapters on Linguistics: Core Concepts and Principles, Grammars, Open-Source Libraries, Application Frameworks, Workflow Systems, Mathematical Essentials, Probability, Inference and Prediction Methods, Random Processes, Bayesian Methods, Machine Learning, Artificial Neural Networks for Natural Language Processing, Information Retrieval, Language Core Tasks, Language Understanding Applications, and more. The synergistic confluence of linguistics, statistics, big data, and high-performance computing is the underlying force for the recent and dramatic advances in analyzing and understanding natural languages, hence making this series all the more important. Provides a thorough treatment of open-source libraries, application frameworks and workflow systems for natural language analysis and understanding Presents new chapters on Linguistics: Core Concepts and Principles, Grammars, Open-Source Libraries, Application Frameworks, Workflow Systems, Mathematical Essentials, Probability, and more

Download or read book Data Cleaning written by Ihab F. Ilyas and published by Morgan & Claypool. This book was released on 2019-06-18 with total page 282 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data quality is one of the most important problems in data management, since dirty data often leads to inaccurate data analytics results and incorrect business decisions. Poor data across businesses and the U.S. government are reported to cost trillions of dollars a year. Multiple surveys show that dirty data is the most common barrier faced by data scientists. Not surprisingly, developing effective and efficient data cleaning solutions is challenging and is rife with deep theoretical and engineering problems. This book is about data cleaning, which is used to refer to all kinds of tasks and activities to detect and repair errors in the data. Rather than focus on a particular data cleaning task, we give an overview of the end-to-end data cleaning process, describing various error detection and repair methods, and attempt to anchor these proposals with multiple taxonomies and views. Specifically, we cover four of the most common and important data cleaning tasks, namely, outlier detection, data transformation, error repair (including imputing missing values), and data deduplication. Furthermore, due to the increasing popularity and applicability of machine learning techniques, we include a chapter that specifically explores how machine learning techniques are used for data cleaning, and how data cleaning is used to improve machine learning models. This book is intended to serve as a useful reference for researchers and practitioners who are interested in the area of data quality and data cleaning. It can also be used as a textbook for a graduate course. Although we aim at covering state-of-the-art algorithms and techniques, we recognize that data cleaning is still an active field of research and therefore provide future directions of research whenever appropriate.