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Dealing With Data
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Book Synopsis Dealing with Data by : Arthur J. Lyon
Download or read book Dealing with Data written by Arthur J. Lyon and published by Pergamon. This book was released on 1970 with total page 420 pages. Available in PDF, EPUB and Kindle. Book excerpt: Dealing with Data is an introductory course to problems and techniques dealing with data analysis, with emphasis on the physical and engineering sciences. The book starts with the basics of data analysis through non-statistical and non-mathematical assessments of error and uncertainty conditions. Experimental and maximum errors and the use of simple graphical methods are briefly described. Applying quick methods on data analysis such as frequency distributions, determination of standard errors, and applications of significance tests are explained. Special attention is given to the statistical ...
Book Synopsis Managing Data in Motion by : April Reeve
Download or read book Managing Data in Motion written by April Reeve and published by Newnes. This book was released on 2013-02-26 with total page 203 pages. Available in PDF, EPUB and Kindle. Book excerpt: Managing Data in Motion describes techniques that have been developed for significantly reducing the complexity of managing system interfaces and enabling scalable architectures. Author April Reeve brings over two decades of experience to present a vendor-neutral approach to moving data between computing environments and systems. Readers will learn the techniques, technologies, and best practices for managing the passage of data between computer systems and integrating disparate data together in an enterprise environment. The average enterprise's computing environment is comprised of hundreds to thousands computer systems that have been built, purchased, and acquired over time. The data from these various systems needs to be integrated for reporting and analysis, shared for business transaction processing, and converted from one format to another when old systems are replaced and new systems are acquired. The management of the "data in motion" in organizations is rapidly becoming one of the biggest concerns for business and IT management. Data warehousing and conversion, real-time data integration, and cloud and "big data" applications are just a few of the challenges facing organizations and businesses today. Managing Data in Motion tackles these and other topics in a style easily understood by business and IT managers as well as programmers and architects. - Presents a vendor-neutral overview of the different technologies and techniques for moving data between computer systems including the emerging solutions for unstructured as well as structured data types - Explains, in non-technical terms, the architecture and components required to perform data integration - Describes how to reduce the complexity of managing system interfaces and enable a scalable data architecture that can handle the dimensions of "Big Data"
Download or read book Data Teams written by Jesse Anderson and published by . This book was released on 2020 with total page pages. Available in PDF, EPUB and Kindle. Book excerpt:
Book Synopsis Data Fusion Methodology and Applications by : Marina Cocchi
Download or read book Data Fusion Methodology and Applications written by Marina Cocchi and published by Elsevier. This book was released on 2019-05-11 with total page 398 pages. Available in PDF, EPUB and Kindle. Book excerpt: Data Fusion Methodology and Applications explores the data-driven discovery paradigm in science and the need to handle large amounts of diverse data. Drivers of this change include the increased availability and accessibility of hyphenated analytical platforms, imaging techniques, the explosion of omics data, and the development of information technology. As data-driven research deals with an inductive attitude that aims to extract information and build models capable of inferring the underlying phenomena from the data itself, this book explores the challenges and methodologies used to integrate data from multiple sources, analytical platforms, different modalities, and varying timescales. - Presents the first comprehensive textbook on data fusion, focusing on all aspects of data-driven discovery - Includes comprehensible, theoretical chapters written for large and diverse audiences - Provides a wealth of selected application to the topics included
Book Synopsis Managing Your Data Science Projects by : Robert de Graaf
Download or read book Managing Your Data Science Projects written by Robert de Graaf and published by Apress. This book was released on 2019-06-07 with total page 146 pages. Available in PDF, EPUB and Kindle. Book excerpt: At first glance, the skills required to work in the data science field appear to be self-explanatory. Do not be fooled. Impactful data science demands an interdisciplinary knowledge of business philosophy, project management, salesmanship, presentation, and more. In Managing Your Data Science Projects, author Robert de Graaf explores important concepts that are frequently overlooked in much of the instructional literature that is available to data scientists new to the field. If your completed models are to be used and maintained most effectively, you must be able to present and sell them within your organization in a compelling way. The value of data science within an organization cannot be overstated. Thus, it is vital that strategies and communication between teams are dexterously managed. Three main ways that data science strategy is used in a company is to research its customers, assess risk analytics, and log operational measurements. These all require different managerial instincts, backgrounds, and experiences, and de Graaf cogently breaks down the unique reasons behind each. They must align seamlessly to eventually be adopted as dynamic models. Data science is a relatively new discipline, and as such, internal processes for it are not as well-developed within an operational business as others. With Managing Your Data Science Projects, you will learn how to create products that solve important problems for your customers and ensure that the initial success is sustained throughout the product’s intended life. Your users will trust you and your models, and most importantly, you will be a more well-rounded and effectual data scientist throughout your career. Who This Book Is For Early-career data scientists, managers of data scientists, and those interested in entering the field of data science
Book Synopsis Principles of Statistical Data Handling by : Fred Davidson
Download or read book Principles of Statistical Data Handling written by Fred Davidson and published by SAGE Publications, Incorporated. This book was released on 1996-04-09 with total page 344 pages. Available in PDF, EPUB and Kindle. Book excerpt: Principles of Statistical Data Handling is designed to help readers understand the principles of data handling so that they can make better use of computer data in research or study.
Book Synopsis Doing Management Research by : Raymond-Alain Thietart
Download or read book Doing Management Research written by Raymond-Alain Thietart and published by SAGE. This book was released on 2001-04-18 with total page 435 pages. Available in PDF, EPUB and Kindle. Book excerpt: `This book provides refreshing and powerful insights on the challenges of conducting management research from a European perspective. Particulalrly for someone embarking on a managment research career this book will provide valuable guidelines.′ -- Ian MacMillan, Wharton School of Business, University of Pennsylvania `This comprehensive volume is distinguished by its balance and pragmatism. The authors who present the various research methods are not proponents but researchers who have applied these methods. The authors who discuss philosophical and strategic issues are not advocates but researchers who have had to confront these issues in their research′ - Bill Starbuck, New York University `Doing Management Research is a fabulous contribution to our field. Thietart and his colleagues have put together a unique and valuable guide to help management scholars more deeply understand the issues, dynamics and contradictions of executing first class managerial research. This book will hold an important place on the researcher′s desk for years to come′ - Michael Tushman, Harvard Business School ′This is an excellent in-depth examination of the conduct of management research. It will serve as a valuable resource for management scholars and researchers and is a must read for Ph.D. students in management.′ -- Michael Hitt, Arizona State University `This book will prove to be an excellent guide for those engaged in management research for the first time and an excellent refresher for more experienced scholars. Raymond Thietart and his colleagues should be thanked roundly for this comprehensive volume′ - Gordon Walker, Southern Methodist University, Cox Business School `This textbook makes an outstanding contribution to texts on management research. For researchers considering management research it offers an extensive guide to the research process′ - Paula Roberts, Nurse Researcher Doing Management Research, a major new textbook, provides answers to questions and problems which researchers invariably encounter when embarking on management research, be it quantitative or qualitative. This book will carefully guide the reader through the research process from beginning to end. An excellent tool for academics and students, it enables the reader to acquire and build upon empirical evidence, and to decide what tools to use to understand and describe what is being observed, and then, which methods of analysis to adopt. There is an entire section dedicated to writing up and communicating the research findings. Written in an accessible and easy-to-use style, this book can be read from cover to cover or dipped into, to clarify particular issues during the research process. Doing Management Research results from the ′hands-on′ experience of a large group of researchers who have all had to address the different issues raised when undertaking management research. It is anchored in real methodological problems that researchers face in their work. This work will also become one of the most useful reference tools for senior researchers who are looking for answers to epistemological or methodological problems.
Book Synopsis Statistical Design - Chemometrics by : Roy E Bruns
Download or read book Statistical Design - Chemometrics written by Roy E Bruns and published by Elsevier. This book was released on 2006-01-27 with total page 423 pages. Available in PDF, EPUB and Kindle. Book excerpt: Statistical Design-Chemometrics is applicable to researchers and professionals who wish to perform experiments in chemometrics and carry out analysis of the data in the most efficient way possible. The language is clear, direct and oriented towards real applications. The book provides 106 exercises with answers to accompany the study of theoretical principles. Forty two cases studies with real data are presented showing designs and the complete statistical analyses for problems in the areas chromatography, electroanalytical and electrochemistry, calibration, polymers, gas adsorption, semiconductors, food technology, biotechnology, photochemistry, catalysis, detergents and ceramics. These studies serve as a guide that the reader can use to perform correct data analyses.-Provides 42 case studies containing step-by-step descriptions of calculational procedures that can be applied to most real optimization problems-Contains 106 theoretical exercises to test individual learning and to provide classroom exercises and material for written tests and exams-Written in a language that facilitates learning for physical and biological scientists and engineers-Takes a practical approach for those involved in industrial optimization problems
Book Synopsis Managing and Sharing Research Data by : Louise Corti
Download or read book Managing and Sharing Research Data written by Louise Corti and published by SAGE. This book was released on 2014-02-04 with total page 258 pages. Available in PDF, EPUB and Kindle. Book excerpt: Research funders in the UK, USA and across Europe are implementing data management and sharing policies to maximize openness of data, transparency and accountability of the research they support. Written by experts from the UK Data Archive with over 20 years experience, this book gives post-graduate students, researchers and research support staff the data management skills required in today’s changing research environment. The book features guidance on: how to plan your research using a data management checklist how to format and organize data how to store and transfer data research ethics and privacy in data sharing and intellectual property rights data strategies for collaborative research how to publish and cite data how to make use of other people’s research data, illustrated with six real-life case studies of data use.
Book Synopsis Statistical Methods for Handling Incomplete Data by : Jae Kwang Kim
Download or read book Statistical Methods for Handling Incomplete Data written by Jae Kwang Kim and published by CRC Press. This book was released on 2021-11-19 with total page 380 pages. Available in PDF, EPUB and Kindle. Book excerpt: Due to recent theoretical findings and advances in statistical computing, there has been a rapid development of techniques and applications in the area of missing data analysis. Statistical Methods for Handling Incomplete Data covers the most up-to-date statistical theories and computational methods for analyzing incomplete data. Features Uses the mean score equation as a building block for developing the theory for missing data analysis Provides comprehensive coverage of computational techniques for missing data analysis Presents a rigorous treatment of imputation techniques, including multiple imputation fractional imputation Explores the most recent advances of the propensity score method and estimation techniques for nonignorable missing data Describes a survey sampling application Updated with a new chapter on Data Integration Now includes a chapter on Advanced Topics, including kernel ridge regression imputation and neural network model imputation The book is primarily aimed at researchers and graduate students from statistics, and could be used as a reference by applied researchers with a good quantitative background. It includes many real data examples and simulated examples to help readers understand the methodologies.
Book Synopsis R for Data Science by : Hadley Wickham
Download or read book R for Data Science written by Hadley Wickham and published by "O'Reilly Media, Inc.". This book was released on 2016-12-12 with total page 521 pages. Available in PDF, EPUB and Kindle. Book excerpt: Learn how to use R to turn raw data into insight, knowledge, and understanding. This book introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for readers with no previous programming experience, R for Data Science is designed to get you doing data science as quickly as possible. Authors Hadley Wickham and Garrett Grolemund guide you through the steps of importing, wrangling, exploring, and modeling your data and communicating the results. You'll get a complete, big-picture understanding of the data science cycle, along with basic tools you need to manage the details. Each section of the book is paired with exercises to help you practice what you've learned along the way. You'll learn how to: Wrangle—transform your datasets into a form convenient for analysis Program—learn powerful R tools for solving data problems with greater clarity and ease Explore—examine your data, generate hypotheses, and quickly test them Model—provide a low-dimensional summary that captures true "signals" in your dataset Communicate—learn R Markdown for integrating prose, code, and results
Book Synopsis Managing Data Science by : Kirill Dubovikov
Download or read book Managing Data Science written by Kirill Dubovikov and published by Packt Publishing Ltd. This book was released on 2019-11-12 with total page 276 pages. Available in PDF, EPUB and Kindle. Book excerpt: Understand data science concepts and methodologies to manage and deliver top-notch solutions for your organization Key FeaturesLearn the basics of data science and explore its possibilities and limitationsManage data science projects and assemble teams effectively even in the most challenging situationsUnderstand management principles and approaches for data science projects to streamline the innovation processBook Description Data science and machine learning can transform any organization and unlock new opportunities. However, employing the right management strategies is crucial to guide the solution from prototype to production. Traditional approaches often fail as they don't entirely meet the conditions and requirements necessary for current data science projects. In this book, you'll explore the right approach to data science project management, along with useful tips and best practices to guide you along the way. After understanding the practical applications of data science and artificial intelligence, you'll see how to incorporate them into your solutions. Next, you will go through the data science project life cycle, explore the common pitfalls encountered at each step, and learn how to avoid them. Any data science project requires a skilled team, and this book will offer the right advice for hiring and growing a data science team for your organization. Later, you'll be shown how to efficiently manage and improve your data science projects through the use of DevOps and ModelOps. By the end of this book, you will be well versed with various data science solutions and have gained practical insights into tackling the different challenges that you'll encounter on a daily basis. What you will learnUnderstand the underlying problems of building a strong data science pipelineExplore the different tools for building and deploying data science solutionsHire, grow, and sustain a data science teamManage data science projects through all stages, from prototype to productionLearn how to use ModelOps to improve your data science pipelinesGet up to speed with the model testing techniques used in both development and production stagesWho this book is for This book is for data scientists, analysts, and program managers who want to use data science for business productivity by incorporating data science workflows efficiently. Some understanding of basic data science concepts will be useful to get the most out of this book.
Book Synopsis Managing Reference Data in Enterprise Databases by : Malcolm Chisholm
Download or read book Managing Reference Data in Enterprise Databases written by Malcolm Chisholm and published by Morgan Kaufmann. This book was released on 2001 with total page 412 pages. Available in PDF, EPUB and Kindle. Book excerpt: "This is a great book! I have to admit I wasn't enthusiastic about the idea of a book with such a narrow topic initially, but, frankly, it's the first professional book I've read page to page in one sitting in a long time. It should be of interest to DBAs, data architects and modelers, programmers who have to write database programs, and yes, even managers. This book is a winner." - Karen Watterson, Editor SQL Server Professional "Malcolm Chisholm has produced a very readable book. It is well-written and with excellent examples. It will, I am sure, become the Reference Book on Reference Data." - Clive Finkelstein, "Father" of Information Engineering, Managing Director, Information Engineering Services Pty Ltd Reference data plays a key role in your business databases and must be free from defects of any kind. So why is it so hard to find information on this critical topic? Recognizing the dangers of taking reference data for granted, Managing Reference Data in Enterprise Databases gives you precisely what you've been seeking: A complete guide to the implementation and management of reference data of all kinds. This book begins with a thorough definition of reference data, then proceeds with a detailed examination of all reference data issues, fully describing uses, common difficulties, and practical solutions. Whether you're a database manager, architect, administrator, programmer, or analyst, be sure to keep this easy-to-use reference close at hand. Features Solves special challenges associated with maintaining reference data. Addresses a wide range of reference data issues, including acronyms, redundancy, mapping, life cycles, multiple languages, and querying. Describes how reference data interacts with other system components, what problems can arise, and how to mitigate these problems. Offers examples of standard reference data types and matrices for evaluating management methods. Provides a number of standard reference data tables and more specialized material to help you deal with reference data, via a companion Web site
Book Synopsis A Data Scientist's Guide to Acquiring, Cleaning, and Managing Data in R by : Samuel E. Buttrey
Download or read book A Data Scientist's Guide to Acquiring, Cleaning, and Managing Data in R written by Samuel E. Buttrey and published by John Wiley & Sons. This book was released on 2017-12-18 with total page 310 pages. Available in PDF, EPUB and Kindle. Book excerpt: The only how-to guide offering a unified, systemic approach to acquiring, cleaning, and managing data in R Every experienced practitioner knows that preparing data for modeling is a painstaking, time-consuming process. Adding to the difficulty is that most modelers learn the steps involved in cleaning and managing data piecemeal, often on the fly, or they develop their own ad hoc methods. This book helps simplify their task by providing a unified, systematic approach to acquiring, modeling, manipulating, cleaning, and maintaining data in R. Starting with the very basics, data scientists Samuel E. Buttrey and Lyn R. Whitaker walk readers through the entire process. From what data looks like and what it should look like, they progress through all the steps involved in getting data ready for modeling. They describe best practices for acquiring data from numerous sources; explore key issues in data handling, including text/regular expressions, big data, parallel processing, merging, matching, and checking for duplicates; and outline highly efficient and reliable techniques for documenting data and recordkeeping, including audit trails, getting data back out of R, and more. The only single-source guide to R data and its preparation, it describes best practices for acquiring, manipulating, cleaning, and maintaining data Begins with the basics and walks readers through all the steps necessary to get data ready for the modeling process Provides expert guidance on how to document the processes described so that they are reproducible Written by seasoned professionals, it provides both introductory and advanced techniques Features case studies with supporting data and R code, hosted on a companion website A Data Scientist's Guide to Acquiring, Cleaning and Managing Data in R is a valuable working resource/bench manual for practitioners who collect and analyze data, lab scientists and research associates of all levels of experience, and graduate-level data mining students.
Book Synopsis Mining Imperfect Data by : Ronald K. Pearson
Download or read book Mining Imperfect Data written by Ronald K. Pearson and published by SIAM. This book was released on 2005-04-01 with total page 309 pages. Available in PDF, EPUB and Kindle. Book excerpt: This book discusses the problems that can occur in data mining, including their sources, consequences, detection and treatment.
Book Synopsis Handling Qualitative Data by : Lyn Richards
Download or read book Handling Qualitative Data written by Lyn Richards and published by SAGE. This book was released on 2009-11-18 with total page 233 pages. Available in PDF, EPUB and Kindle. Book excerpt: Lecturers, click here to request an e-inspection copy of this text This new edition of Lyn Richards' best-selling book provides an accessible introduction to qualitative research for students and practitioners. Recognizing that for many new researchers dealing with data is the main point of departure, this book helps them to acquire a progressive understanding of the skills and methodological issues that are central to qualitative research. Lyn Richards provides clear and pragmatic guidance on how to handle, reflect on and get results from small amounts of data, while at the same time showing how a consideration of methods and their philosophical underpinnings informs how we should best handle our data. This book also covers all the processes of making, meeting, sorting, coding, documenting and exploring qualitative data, smoothly integrating software use and the discussion of the main challenges that readers are likely to encounter. It guides novice researchers to achieve valid and useful outcomes from qualitative analysis, and to ensure they do justice to their data. This second edition features: - Increased coverage of issues about the researcher's relation to their data and ethical implications - An expanded section on preparing for data collection and reflecting on the nature of data. There is also a brand new website, offering: - Live, detailed case studies of qualitative methods in practice, linking to publications and illustrative material. Researchers tell the stories of projects, from design, through what was actually done with the data, to how analysis was achieved and reported; - A software guide with links to information and tutorials in several products.
Book Synopsis Managing Scientific Information and Research Data by : Svetla Baykoucheva
Download or read book Managing Scientific Information and Research Data written by Svetla Baykoucheva and published by Chandos Publishing. This book was released on 2015-07-14 with total page 163 pages. Available in PDF, EPUB and Kindle. Book excerpt: Innovative technologies are changing the way research is performed, preserved, and communicated. Managing Scientific Information and Research Data explores how these technologies are used and provides detailed analysis of the approaches and tools developed to manage scientific information and data. Following an introduction, the book is then divided into 15 chapters discussing the changes in scientific communication; new models of publishing and peer review; ethics in scientific communication; preservation of data; discovery tools; discipline-specific practices of researchers for gathering and using scientific information; academic social networks; bibliographic management tools; information literacy and the information needs of students and researchers; the involvement of academic libraries in eScience and the new opportunities it presents to librarians; and interviews with experts in scientific information and publishing. - Promotes innovative technologies for creating, sharing and managing scientific content - Presents new models of scientific publishing, peer review, and dissemination of information - Serves as a practical guide for researchers, students, and librarians on how to discover, filter, and manage scientific information - Advocates for the adoption of unique author identifiers such as ORCID and ResearcherID - Looks into new tools that make scientific information easy to discover and manage - Shows what eScience is and why it is becoming a priority for academic libraries - Demonstrates how Electronic Laboratory Notebooks can be used to record, store, share, and manage research data - Shows how social media and the new area of Altmetrics increase researchers' visibility and measure attention to their research - Directs to sources for datasets - Provides directions on choosing and using bibliographic management tools - Critically examines the metrics used to evaluate research impact - Aids strategic thinking and informs decision making
