Data Clean Up And Management
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Data Clean Up And Management
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Author : Margaret Hogarth
language : en
Publisher: Elsevier
Release Date : 2012-10-22
Data Clean Up And Management written by Margaret Hogarth and has been published by Elsevier this book supported file pdf, txt, epub, kindle and other format this book has been release on 2012-10-22 with Business & Economics categories.
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
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Author : Venkatesh Ganti
language : en
Publisher: Morgan & Claypool Publishers
Release Date : 2013-09-01
Data Cleaning written by Venkatesh Ganti and has been published by Morgan & Claypool Publishers this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-09-01 with Computers categories.
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.
Data Cleaning
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Author : Ihab F. Ilyas
language : en
Publisher: Morgan & Claypool
Release Date : 2019-06-18
Data Cleaning written by Ihab F. Ilyas and has been published by Morgan & Claypool this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06-18 with Computers categories.
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.
Practical Data Cleaning
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Author : Lee Baker
language : en
Publisher: Lee Baker
Release Date : 2019-01-30
Practical Data Cleaning written by Lee Baker and has been published by Lee Baker this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-30 with Education categories.
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!
Development Research In Practice
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Author : Kristoffer Bjärkefur
language : en
Publisher: World Bank Publications
Release Date : 2021-07-16
Development Research In Practice written by Kristoffer Bjärkefur and has been published by World Bank Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-07-16 with Business & Economics categories.
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 Cleaning
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Author : Venkatesh Ganti
language : en
Publisher: Springer
Release Date : 2013-10-01
Data Cleaning written by Venkatesh Ganti and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2013-10-01 with Computers categories.
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.
Databases For Small Business
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Author : Anna Manning
language : en
Publisher: Apress
Release Date : 2015-11-21
Databases For Small Business written by Anna Manning and has been published by Apress this book supported file pdf, txt, epub, kindle and other format this book has been release on 2015-11-21 with Computers categories.
This book covers the practical aspects of database design, data cleansing, data analysis, and data protection, among others. The focus is on what you really need to know to create the right database for your small business and to leverage it most effectively to spur growth and revenue. Databases for Small Business is a practical handbook for entrepreneurs, managers, staff, and professionals in small organizations who are not IT specialists but who recognize the need to ramp up their small organizations’ use of data and to round out their own business expertise and office skills with basic database proficiency. Anna Manning—a data scientist who has worked on database design and data analysis in a computer science university research lab, her own small business, and a nonprofit—walks you through the progression of steps that will enable you to extract actionable intelligence and maximum value from your business data in terms of marketing, sales, customer relations, decision making, and business strategy. Dr. Manning illustrates the steps in the book with four running case studies of a small online business, an engineering startup, a small legal firm, and a nonprofit organization. Databases for Small Business teaches non-techie entrepreneurs and professionals how to: Design a small business database from scratch Extract the maximum profit from your data Follow guidance on data protection law Effectively use data collection and data cleansing techniques Train staff to leverage your data
Cody S Data Cleaning Techniques Using Sas Third Edition
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Author : Ron Cody
language : en
Publisher: SAS Institute
Release Date : 2017-03-15
Cody S Data Cleaning Techniques Using Sas Third Edition written by Ron Cody and has been published by SAS Institute this book supported file pdf, txt, epub, kindle and other format this book has been release on 2017-03-15 with Computers categories.
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. --
Data Cleaning The Ultimate Practical Guide
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Author : Lee Baker
language : en
Publisher: Lee Baker
Release Date : 2022-11-07
Data Cleaning The Ultimate Practical Guide written by Lee Baker and has been published by Lee Baker this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-07 with Business & Economics categories.
Data visualisation is sexy. So are Bayesian Belief Nets and Artificial Neural Networks. You can’t get to do any of these things, though, if your data are dirty. Your analysis package will just stare back at you, saying ‘computer says no’. But just how do you get the clean data that these packages need? What is ‘clean data’? And, for that matter, what is ‘dirty data’? Data Cleaning: The Ultimate Practical Guide is a guide to understanding what dirty data is, and how it gets into your dataset. More than that, it is a guide to helping you prevent most types of dirty data getting into your dataset in the first place, and cleaning out quickly and efficiently the remaining errors, so you can have clean, fit-for-purpose and analysis-ready data. So that your data are ready to change the world! Data Cleaning: The Ultimate Practical Guide is a snappy little non-threatening book about everything you ever wanted to know (but were afraid to ask) about the craft of cleaning and preparing your data for the sexier parts of your analysis. First, I’ll explain about the 4 phases of data cleaning. Then I’ll show you the 6 different types of dirty data that tend to find a way into your dataset. You’ll learn about the 5 data collection methods typically used in research, and you’ll get a 5 step method of cleaning data. Finally, you’ll learn about the 4 data pre-processing steps using summary statistics that will help you get your data fit-for-purpose and analysis-ready. Best of all, there is no technical jargon – it is written in plain English and is perfect for beginners! By the time you’ve read this short book, you’ll know more about data collection and cleaning than most people around you! Discover how to clean your data quickly and effectively. Get this book, TODAY!
Exploratory Data Mining And Data Cleaning
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Author : Tamraparni Dasu
language : en
Publisher: John Wiley & Sons
Release Date : 2003-08-01
Exploratory Data Mining And Data Cleaning written by Tamraparni Dasu and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2003-08-01 with Mathematics categories.
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.
