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Data Clean-Up and Management by Margaret Hogarth,Kenneth Furuta Pdf
Data use in the library has specific characteristics and common problems. Data Clean-up and Management addresses these, and provides methods to clean up frequently-occurring data problems using readily-available applications. The authors highlight the importance and methods of data analysis and presentation, and offer guidelines and recommendations for a data quality policy. The book gives step-by-step how-to directions for common dirty data issues. Focused towards libraries and practicing librarians Deals with practical, real-life issues and addresses common problems that all libraries face Offers cradle-to-grave treatment for preparing and using data, including download, clean-up, management, analysis and presentation
Data Cleaning by Venkatesh Ganti,Anish Das Sarma Pdf
Data warehouses consolidate various activities of a business and often form the backbone for generating reports that support important business decisions. Errors in data tend to creep in for a variety of reasons. Some of these reasons include errors during input data collection and errors while merging data collected independently across different databases. These errors in data warehouses often result in erroneous upstream reports, and could impact business decisions negatively. Therefore, one of the critical challenges while maintaining large data warehouses is that of ensuring the quality of data in the data warehouse remains high. The process of maintaining high data quality is commonly referred to as data cleaning. In this book, we first discuss the goals of data cleaning. Often, the goals of data cleaning are not well defined and could mean different solutions in different scenarios. Toward clarifying these goals, we abstract out a common set of data cleaning tasks that often need to be addressed. This abstraction allows us to develop solutions for these common data cleaning tasks. We then discuss a few popular approaches for developing such solutions. In particular, we focus on an operator-centric approach for developing a data cleaning platform. The operator-centric approach involves the development of customizable operators that could be used as building blocks for developing common solutions. This is similar to the approach of relational algebra for query processing. The basic set of operators can be put together to build complex queries. Finally, we discuss the development of custom scripts which leverage the basic data cleaning operators along with relational operators to implement effective solutions for data cleaning tasks.
Cody's Data Cleaning Techniques Using SAS, Third Edition by Ron Cody Pdf
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. --
Development Research in Practice by Kristoffer Bjärkefur,Luíza Cardoso de Andrade,Benjamin Daniels,Maria Ruth Jones Pdf
Development Research in Practice leads the reader through a complete empirical research project, providing links to continuously updated resources on the DIME Wiki as well as illustrative examples from the Demand for Safe Spaces study. The handbook is intended to train users of development data how to handle data effectively, efficiently, and ethically. “In the DIME Analytics Data Handbook, the DIME team has produced an extraordinary public good: a detailed, comprehensive, yet easy-to-read manual for how to manage a data-oriented research project from beginning to end. It offers everything from big-picture guidance on the determinants of high-quality empirical research, to specific practical guidance on how to implement specific workflows—and includes computer code! I think it will prove durably useful to a broad range of researchers in international development and beyond, and I learned new practices that I plan on adopting in my own research group.†? —Marshall Burke, Associate Professor, Department of Earth System Science, and Deputy Director, Center on Food Security and the Environment, Stanford University “Data are the essential ingredient in any research or evaluation project, yet there has been too little attention to standardized practices to ensure high-quality data collection, handling, documentation, and exchange. Development Research in Practice: The DIME Analytics Data Handbook seeks to fill that gap with practical guidance and tools, grounded in ethics and efficiency, for data management at every stage in a research project. This excellent resource sets a new standard for the field and is an essential reference for all empirical researchers.†? —Ruth E. Levine, PhD, CEO, IDinsight “Development Research in Practice: The DIME Analytics Data Handbook is an important resource and a must-read for all development economists, empirical social scientists, and public policy analysts. Based on decades of pioneering work at the World Bank on data collection, measurement, and analysis, the handbook provides valuable tools to allow research teams to more efficiently and transparently manage their work flows—yielding more credible analytical conclusions as a result.†? —Edward Miguel, Oxfam Professor in Environmental and Resource Economics and Faculty Director of the Center for Effective Global Action, University of California, Berkeley “The DIME Analytics Data Handbook is a must-read for any data-driven researcher looking to create credible research outcomes and policy advice. By meticulously describing detailed steps, from project planning via ethical and responsible code and data practices to the publication of research papers and associated replication packages, the DIME handbook makes the complexities of transparent and credible research easier.†? —Lars Vilhuber, Data Editor, American Economic Association, and Executive Director, Labor Dynamics Institute, Cornell University
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.
Data Management for Researchers by Kristin Briney Pdf
A comprehensive guide to everything scientists need to know about data management, this book is essential for researchers who need to learn how to organize, document and take care of their own data. Researchers in all disciplines are faced with the challenge of managing the growing amounts of digital data that are the foundation of their research. Kristin Briney offers practical advice and clearly explains policies and principles, in an accessible and in-depth text that will allow researchers to understand and achieve the goal of better research data management. Data Management for Researchers includes sections on: * The data problem – an introduction to the growing importance and challenges of using digital data in research. Covers both the inherent problems with managing digital information, as well as how the research landscape is changing to give more value to research datasets and code. * The data lifecycle – a framework for data’s place within the research process and how data’s role is changing. Greater emphasis on data sharing and data reuse will not only change the way we conduct research but also how we manage research data. * Planning for data management – covers the many aspects of data management and how to put them together in a data management plan. This section also includes sample data management plans. * Documenting your data – an often overlooked part of the data management process, but one that is critical to good management; data without documentation are frequently unusable. * Organizing your data – explains how to keep your data in order using organizational systems and file naming conventions. This section also covers using a database to organize and analyze content. * Improving data analysis – covers managing information through the analysis process. This section starts by comparing the management of raw and analyzed data and then describes ways to make analysis easier, such as spreadsheet best practices. It also examines practices for research code, including version control systems. * Managing secure and private data – many researchers are dealing with data that require extra security. This section outlines what data falls into this category and some of the policies that apply, before addressing the best practices for keeping data secure. * Short-term storage – deals with the practical matters of storage and backup and covers the many options available. This section also goes through the best practices to insure that data are not lost. * Preserving and archiving your data – digital data can have a long life if properly cared for. This section covers managing data in the long term including choosing good file formats and media, as well as determining who will manage the data after the end of the project. * Sharing/publishing your data – addresses how to make data sharing across research groups easier, as well as how and why to publicly share data. This section covers intellectual property and licenses for datasets, before ending with the altmetrics that measure the impact of publicly shared data. * Reusing data – as more data are shared, it becomes possible to use outside data in your research. This chapter discusses strategies for finding datasets and lays out how to cite data once you have found it. This book is designed for active scientific researchers but it is useful for anyone who wants to get more from their data: academics, educators, professionals or anyone who teaches data management, sharing and preservation. "An excellent practical treatise on the art and practice of data management, this book is essential to any researcher, regardless of subject or discipline." —Robert Buntrock, Chemical Information Bulletin
Exploratory Data Mining and Data Cleaning by Tamraparni Dasu,Theodore Johnson Pdf
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.
Data cleaning is a waste of time. If the data had been collected properly in the first place there wouldn’t be any cleaning to do, and you wouldn’t now be faced with the prospect of weeks of cleaning to get your dataset analysis-ready. Worse still, your boss won’t understand why your analysis report isn’t on his desk yet, a mere 48 hours after he’s asked for it. Bless him, he doesn’t understand – he thinks that cleaning data is just about clicking a few buttons in Excel and – ta da! – it’s all done. Even a monkey can do that, right? And – for good reason – you won’t get any help from statistics books either. Data is messy and cleaning it can be difficult, time-consuming and costly. Not to mention it’s the least sexy thing you can do with a dataset. Yet you’ve still got to do it, because, well, someone has to… But it doesn’t have to be so difficult. If you're organised and follow a few simple rules your data cleaning processes can be simple, fast and effective. Not to mention fun! Well, not fun exactly, just not quite as coma-inducing. Practical Data Cleaning (now in its 5th Edition!) explains the 19 most important tips about data cleaning with a focus on understanding your data, how to work with it, choose the right ways to analyse it, select the correct tools and how to interpret the results to get your data clean in double quick time. Best of all, there is no technical jargon – it is written in plain English and is perfect for beginners! Discover how to clean your data quickly and effectively. Get this book, TODAY!
United States. Congress. House. Committee on Oversight and Government Reform. Subcommittee on Government Organization, Efficiency, and Financial Management
Author : United States. Congress. House. Committee on Oversight and Government Reform. Subcommittee on Government Organization, Efficiency, and Financial Management Publisher : Unknown Page : 36 pages File Size : 44,7 Mb Release : 2011 Category : Political Science ISBN : UCSD:31822038352688
Department of Homeland Security Financial Management by United States. Congress. House. Committee on Oversight and Government Reform. Subcommittee on Government Organization, Efficiency, and Financial Management Pdf
Information Technology Control and Audit, Third Edition by Sandra Senft,Frederick Gallegos Pdf
The headline-grabbing financial scandals of recent years have led to a great urgency regarding organizational governance and security. Information technology is the engine that runs modern organizations, and as such, it must be well-managed and controlled. Organizations and individuals are dependent on network environment technologies, increasing the importance of security and privacy. The field has answered this sense of urgency with advances that have improved the ability to both control the technology and audit the information that is the lifeblood of modern business. Reflects the Latest Technological Advances Updated and revised, this third edition of Information Technology Control and Audit continues to present a comprehensive overview for IT professionals and auditors. Aligned to the CobiT control objectives, it provides a fundamental understanding of IT governance, controls, auditing applications, systems development, and operations. Demonstrating why controls and audits are critical, and defining advances in technology designed to support them, this volume meets the increasing need for audit and control professionals to understand information technology and the controls required to manage this key resource. A Powerful Primer for the CISA and CGEIT Exams Supporting and analyzing the CobiT model, this text prepares IT professionals for the CISA and CGEIT exams. With summary sections, exercises, review questions, and references for further readings, it promotes the mastery of the concepts and practical implementation of controls needed to effectively manage information technology resources. New in the Third Edition: Reorganized and expanded to align to the CobiT objectives Supports study for both the CISA and CGEIT exams Includes chapters on IT financial and sourcing management Adds a section on Delivery and Support control objectives Includes additional content on audit and control of outsourcing, change management, risk management, and compliance
Best Practices in Data Cleaning by Jason W. Osborne Pdf
Many researchers jump straight from data collection to data analysis without realizing how analyses and hypothesis tests can go profoundly wrong without clean data. This book provides a clear, step-by-step process of examining and cleaning data in order to decrease error rates and increase both the power and replicability of results. Jason W. Osborne, author of Best Practices in Quantitative Methods (SAGE, 2008) provides easily-implemented suggestions that are research-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. If your goal is to do the best research you can do, draw conclusions that are most likely to be accurate representations of the population(s) you wish to speak about, and report results that are most likely to be replicated by other researchers, then this basic guidebook will be indispensible.
Trends in Enterprise Architecture Research by Erik Proper,Marc Lankhorst,Marten Schönherr,Joseph Barjis,Sietse Overbeek Pdf
This volume constitutes the proceedings of the 5th International Workshop on Trends in Enterprise Architecture Research (TEAR), held in Delft, The Netherlands, on November 12, 2010. The main objective of the workshop is to identify major trends and challenges in enterprise architecture research by providing a discussion forum where researchers and practitioners can exchange experiences, problems, and ideas. The 7 papers presented were extensively reviewed and selected from 15 submissions. They report on core concepts and the effectiveness of enterprise architecture, on architecture description languages, and on exemplary case studies.
Radioactive Waste Management and Contaminated Site Clean-Up by William E Lee,Michael I. Ojovan,Carol M Jantzen Pdf
Radioactive waste management and contaminated site clean-up reviews radioactive waste management processes, technologies, and international experiences. Part one explores the fundamentals of radioactive waste including sources, characterisation, and processing strategies. International safety standards, risk assessment of radioactive wastes and remediation of contaminated sites and irradiated nuclear fuel management are also reviewed. Part two highlights the current international situation across Africa, Asia, Europe, and North America. The experience in Japan, with a specific chapter on Fukushima, is also covered. Finally, part three explores the clean-up of sites contaminated by weapons programmes including the USA and former USSR. Radioactive waste management and contaminated site clean-up is a comprehensive resource for professionals, researchers, scientists and academics in radioactive waste management, governmental and other regulatory bodies and the nuclear power industry. Explores the fundamentals of radioactive waste including sources, characterisation, and processing strategies Reviews international safety standards, risk assessment of radioactive wastes and remediation of contaminated sites and irradiated nuclear fuel management Highlights the current international situation across Africa, Asia, Europe, and North America specifically including a chapter on the experience in Fukushima, Japan
United States. Congress. House. Committee on Appropriations. Subcommittee on Department of the Interior and Related Agencies
Author : United States. Congress. House. Committee on Appropriations. Subcommittee on Department of the Interior and Related Agencies Publisher : Unknown Page : 764 pages File Size : 41,9 Mb Release : 1999 Category : United States ISBN : SRLF:AA0008820060
Department of the Interior and Related Agencies Appropriations for 2000: Secretary of the Interior by United States. Congress. House. Committee on Appropriations. Subcommittee on Department of the Interior and Related Agencies Pdf
