into the Workers’ Compensation (WC) actuarial model workbook. Payroll data for the WC model should contain “only the actual hours worked” for specific Rate Schedule Codes (RSC) groups‚ including executives. The WC payroll data should exclude all paid leave types. A comparison of work hours from the NPHRS mainframe report to the summary in EDW reveals very small differences. We hope to align the NPHRS and EDW work hour data. Also‚ we (Technical Analysis‚ Accounting and Finance) need to understand
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Big Data In It terminology‚ Big Data is looked as a group of data sets‚ which are so sophisticated and large that the data can not be easily taken‚ stored‚ searched‚ shared‚ analyzed or visualized making use of offered tools. In global market segments‚ such “Big Data” generally looks throughout attempts to identify business tendencies from accessible files sets. Other areas‚ exactly where Big Data continually appears include various job areas of research for example the human being genome and also
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HATCO Data Set Description The HATCO data set gives data from a survey of customers of the HATCO company. The data set consists of 100 observations on 14 separate variables. Three types of information were collected. The first type of information is the perception of HATCO on seven attributes identified in past studies as the most influential in the choice of suppliers. The respondents‚ purchasing managers of firms buying from HATCO‚ rated HATCO on each attribute. Each of these 7 variables
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Big data describes innovative methods and technologies to capture‚ distribute‚ manage and analyze larger-sized data sets with high rate and diverse structures that conventional data management methods are unable to handle. Digital data is now everywhere—in every sector public or private‚ economy‚ organization and customer of digital technology. There are many ways that big data can be used to create value across sectors of the global economy. It has demonstrated the capacity to improve predictions
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4V of Big Data? Imagine all the information you alone generate each time you swipe your credit card‚ post to social media‚ drive your car‚ leave a voicemail‚ or visit a doctor. Now try to imagine your data combined with the data of all humans‚ corporations‚ and organizations in the world! From healthcare to social media‚ from business to the auto industry‚ humans are now creating more data than ever before. volume‚ velocity‚ variety‚ and veracity. Volume: Scale of Data Big data is big. It’s
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Data Structures and Algorithms DSA Annotated Reference with Examples Granville Barne Luca Del Tongo Data Structures and Algorithms: Annotated Reference with Examples First Edition Copyright c Granville Barnett‚ and Luca Del Tongo 2008. This book is made exclusively available from DotNetSlackers (http://dotnetslackers.com/) the place for .NET articles‚ and news from some of the leading minds in the software industry. Contents 1 Introduction 1.1 What this book is‚ and what
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Introduction: Data breach has always been a sensitive topic‚ let alone when the data breach is related to banking. In the mean time‚ there’s a breach was found happened to the online banking system of the competitive bank of First Union Bank‚ and the hacker had stolen quantities of customers’ personal information and data. It has been an alarm for all the banks‚ it reminds the whole society to be alert of the damage caused by the data breach. The Chief Information Officer of the First Union Bank
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BCSCCS 303 R03 DATA STRUCTURES (Common for CSE‚ IT and ICT) L T P CREDITS 3 1 0 4 UNIT - I (15 Periods) Pseudo code & Recursion: Introduction – Pseudo code – ADT – ADT model‚ implementations; Recursion – Designing recursive algorithms – Examples – GCD‚ factorial‚ fibonnaci‚ Prefix to Postfix conversion‚ Tower of Hanoi; General linear lists – operations‚ implementation‚ algorithms UNIT - II (15 Periods)
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Collecting Data Shauntia Dismukes BSHS/405 June 1‚ 2015 Tim Duncan Collecting Data Data collection is the process of gathering and measuring information on variables of interest‚ in an established systematic fashion that enables one to answer stated research questions‚ test hypotheses‚ and evaluate outcomes. In this paper I will define the importance of data collecting in the helping field. While working in the helping field‚ there are many important things that must happen
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There are many key differences that are important to understand between data oriented and process oriented approaches to designing a new system. The system focus of the data views and process views are entirely different. The process view focuses on what the systems supposed to do and when‚ while the data view has a focus on what the system needs to operate. Another noteworthy difference that distinguishes the two views is the design stability. The design stability of a process view is a more limited
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