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Готові списки джерел за темами / Cleaning of data / Книги

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Книги з теми "Cleaning of data"

Автор: Grafiati

Опубліковано: 30 травня 2022

Оновлено: 31 травня 2022

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1

Exploratory data mining and data cleaning. Hoboken, NJ: John Wiley & Sons, 2004.

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2

Best practices in data cleaning. Thousand Oaks: SAGE, 2013.

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3

Institute, SAS, ed. Cody's data cleaning techniques using SAS. 2nd ed. Cary, NC: SAS Institute Inc., 2008.

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4

Institute, SAS, ed. Cody's data cleaning techniques using SAS software. Cary, NC: SAS Institute Inc., 1999.

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5

de Jonge, Edwin, and Mark van der Loo. Statistical Data Cleaning with Applications in R. Chichester, UK: John Wiley & Sons, Ltd, 2018. http://dx.doi.org/10.1002/9781118897126.

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6

Buttrey, Samuel. A Data Scientist's Guide to Acquiring, Cleaning and Managing Data in R. Chichester, UK: John Wiley & Sons Ltd, 2017. http://dx.doi.org/10.1002/9781119080053.

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7

Gibbs, Roger. A review of the data available on cleaning services. [London?: Department of Trade and Industry?], 1987.

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8

Kimball, Ralph. The data warehouse ETL toolkit: Practical techniques for extracting, cleaning, conforming, and delivering data. Indianapolis, IN: Wiley, 2004.

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9

Waschbusch, Robert J. Data and methods of a 1999-2000 street sweeping study on an urban freeway in Milwaukee County, Wisconsin. Middleton, Wis: U.S. Dept. of the Interior, U.S. Geological Survey, 2003.

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10

H, Long Stephen, Marquis M. Susan, Robert Wood Johnson Foundation, and Rand Corporation, eds. Data cleaning procedures for the 1993 Robert Wood Johnson Foundation family health insurance survey. Santa Monica, CA: Rand, 1997.

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11

H, Long Stephen, Marquis M. Susan, Robert Wood Johnson Foundation, and Rand Corporation, eds. Data cleaning procedures for the 1993 Robert Wood Johnson Foundation employer health insurance survey. Santa Monica, CA: Rand, 1997.

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12

Cleaning up a computer mess: A guide to diagnosing and correcting computer problems. New York: Van Nostrand Reinhold, 1986.

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13

Pellegrin, Nathan, and Aeron Zentner. Cleansing Data. 2455 Teller Road, Thousand Oaks California 91320: SAGE Publications, Inc., 2021. http://dx.doi.org/10.4135/9781071868164.

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14

Müller, Heiko. Problems, methods, and challenges in comprehensive data cleansing. Berlin: Professoren des Inst. Für Informatik, 2005.

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15

Campesato, Oswald. Data Cleaning. Mercury Learning & Information, 2018.

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16

Dasu, Tamraparni, and Theodore Johnson. Exploratory Data Mining and Data Cleaning. Wiley & Sons, Incorporated, John, 2007.

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17

Johnson, Theodore, and Tamraparni Dasu. Exploratory Data Mining and Data Cleaning. Wiley-Interscience, 2003.

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18

Dasu, Tamraparni, and Theodore Johnson. Exploratory Data Mining and Data Cleaning. Wiley & Sons, Incorporated, John, 2003.

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19

Ganti, Venkatesh, and Anish Das Sarma. Data Cleaning: A Practical Perspective. Morgan & Claypool Publishers, 2013.

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20

Jonge, Edwin de, and Mark van der Loo. Statistical Data Cleaning with Applications in R. Wiley, 2018.

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21

Jonge, Edwin de, and Mark van der Loo. Statistical Data Cleaning with Applications in R. Wiley & Sons, Incorporated, John, 2018.

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22

Whitaker, Lyn R., and Samuel E. Buttrey. Data Scientist's Guide to Acquiring, Cleaning, and Managing Data in R. Wiley & Sons, Incorporated, John, 2017.

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23

Buttrey, Samuel. A data scientist's guide to acquiring, cleaning, and managing data in R. 2018.

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24

United States. Environmental Protection Agency. Office of Water Regulations and Standards, ed. Preliminary data summary for the transportation equipment cleaning industry. Washington, D.C: Office of Water Regulations and Standards, Office of Water, U.S. Environmental Protection Agency, 1993.

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25

McCallum, Q. Ethan. Bad Data Handbook: Cleaning up the Data So You Can Get Back to Work. O'Reilly Media, Incorporated, 2012.

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26

Kimball, Ralph. Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data. Wiley & Sons, Incorporated, John, 2011.

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27

Kimball, Ralph. Data Warehouse�ETL Toolkit: Practical Techniques for Extracting, Cleaning, Conforming, and Delivering Data. Wiley & Sons, Incorporated, John, 2007.

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28

European social dialogue: Statutory and agreement-based provisions on certain aspects of the organization of working time in the cleaning industry : Data ... (Document series / European Commission). Unipub [distributor], 1995.

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29

Walker, Michael. Python Data Cleaning Cookbook: Modern techniques and Python tools to detect and remove dirty data and extract key insights. Packt Publishing, 2020.

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30

publisher, fathers. Cleaning Lady Client Data Log Book: Appointment Book with a to Z Alphabetic Tabs, Notebook Journal to Record Customer Information, Cleaning Lady Gift. Independently Published, 2020.

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31

Euller, Ronald. Data Cleaning Procedures for the 1993 Robert Wood Johnson Foundation Employer Health Insurance Survey, Volume 1. RAND Corporation, 1997.

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32

Osborne, Jason W., ed. Sweating the Small Stuff: Does data cleaning and testing of assumptions really matter in the 21st century? Frontiers Media SA, 2013. http://dx.doi.org/10.3389/978-2-88919-155-0.

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33

Peacock, Janet L., and Sally M. Kerry. Introduction to the research process. Oxford University Press, 2013. http://dx.doi.org/10.1093/med/9780198599661.003.0002.

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Анотація:

Chapter 2 discusses the research process, including defining the question, writing protocols, data collection, coding and data entry, data checking and cleaning, using computers, record keeping, presenting results, and when to seek help from a statistician.

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34

Aye, Goodness C., Laurence Harris, and Junior T. Chiweza. Monetary policy and wealth inequality in South Africa: Evidence from tax administrative data. UNU-WIDER, 2020. http://dx.doi.org/10.35188/unu-wider/2020/931-0.

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This paper examines the relationship between monetary policy and wealth inequality in South Africa. We employed a unique database of tax administrative data which allowed us to account for individual heterogeneity. These tax data span from 2011 to 2017 and include over 3 million individual taxpayers in South Africa after data cleaning. Results based on fixed- and random-effects panel model estimates show that monetary policy generally increases wealth Gini inequality while it decreases the wealth 90–10 percentile differential. Increasing asset prices and gross domestic product per capita generally increases wealth inequality, while inflation reduces wealth inequality. The effect of age on wealth distribution varies depending on whether a fixed- or random-effects panel model is considered. Based on the estimates and observed data, being male tends to increase wealth inequality.

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35

Fisher, Lucy T., and Miliann Kang. Reinventing Dirty Work. University of Illinois Press, 2017. http://dx.doi.org/10.5406/illinois/9780252037573.003.0010.

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This chapter examines how immigrant women accommodate themselves to the various demands of low-wage, low-status service jobs by engaging in “boundary making,” processes that circumscribe and redefine the performance of “dirty work.” Boundary making refers to material and symbolic processes in which providers of low-wage work impose limitations on its performance while redefining the work as skillful and important. Dirty work is defined as physical labor that involves cleaning and caring for the human body, its products, and its environs. The chapter first provides an overview of certified nursing assistants (CNAs) who provide elder care in the United States before exploring how immigrants working as CNAs make meaning of work that is often construed as dirty work. Using data from fieldwork in three California nursing homes, the chapter shows how CNAs try to bring some measure of dignity to a low-wage, low-status job, and shape their identity formation as workers and immigrants within constraining institutional contexts.

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36

Kimball, Ralph, and Joe Caserta. The Data Warehouse ETL Toolkit: Practical Techniques for Extracting, Cleanin. Wiley, 2004.

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37

Office, General Accounting. Hazardous waste: EPA's generation and management data need further improvement : report to the chairman, Subcommittee on Environment, Energy, and Natural Resources, Committee on Government Operations, House of Representatives. Washington, D.C: The Office, 1990.

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38

Office, General Accounting. Hazardous waste: Data management problems delay EPA's assessment of minimization efforts : report to the chairman, Subcommittee on Transportation and Hazardous Materials, Committee on Energy and Commerce, House of Representatives. Washington, D.C: GAO, 1991.

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39

Damani, Nizam. Manual of Infection Prevention and Control. Oxford University Press, 2019. http://dx.doi.org/10.1093/med/9780198815938.001.0001.

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Анотація:

The Manual of Infection Prevention and Control provides practical guidance on all aspects of healthcare-associated infections (HAIs). It outlines the basic concepts of infection prevention and control (IPC), modes of transmission, surveillance, control of outbreaks, epidemiology, and biostatistics. The book provides up-to-date advice on the triage and isolation of patients and on new and emerging infectious diseases, and with the use of illustrations, it provides a step-by-step approach on how to perform hand hygiene and how to don and take off personal protective equipment correctly. In addition, this section also outlines how to minimize cross-infection by healthcare building design and prevent the transmission of various infectious diseases from infected patients after death. The disinfection and sterilization section reviews how to risk assess, disinfect and/or sterilize medical items and equipment, antimicrobial activities, and the use of various chemical disinfectants and antiseptics, and how to decontaminate endoscopes. The section on the prevention of HAIs reviews and updates IPC guidance on the prevention of the most common HAIs, i.e. surgical site infections, infections associated with intravascular and urinary catheters, and hospital- and ventilator-acquired pneumonias. In view of the global emergence of antimicrobial resistance to the various pathogens, the book examines and provides practical advice on how to implement an antibiotic stewardship programme and prevent cross-infection against various multi-drug resistant pathogens. Amongst other pathogens, the book also reviews IPC precautions against various haemorrhagic and bloodborne viral infections. The section on support services discusses the protection of healthcare workers, kitchen, environmental cleaning, catering, laundry services, and clinical waste disposal services.

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