Analyzing Data Using Pivot Tables – An Example Remember assignment 5 where you were asked to compare Invoice amounts to Sales Order amounts? You had to create a query to join together 2 tables from an Access database. If the results of that query had been downloaded into an Excel file (a simple thing to do)‚ you could have used the Excel file and a Pivot table to help in the analysis. Before you try to follow this example‚ you should learn as much as you can about Pivot Tables from Microsoft’s
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DATA COMPRESSION The word data is in general used to mean the information in digital form on which computer programs operate‚ and compression means a process of removing redundancy in the data. By ’compressing data’‚ we actually mean deriving techniques or‚ more specifically‚ designing efficient algorithms to: * represent data in a less redundant fashion * remove the redundancy in data * Implement compression algorithms‚ including both compression and decompression. Data Compression
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Systems Coursework Part 1: Big Data Student ID: 080010830 March 16‚ 2012 Word Count: 3887 Abstract Big data is one of the most vibrant topics among multiple industries‚ thus in this paper we have covered examples as well as current research that is being conducted in the field. This was done based on real applications that have to deal with big data on a daily basis together with a clear focus on their achievements and challenges. The results are very convincing that big data is a critical subject that
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Turning data into information © Copyright IBM Corporation 2007 Course materials may not be reproduced in whole or in part without the prior written permission of IBM. 4.0.3 Unit objectives After completing this unit‚ you should be able to: Explain how Business and Data is correlated Discuss the concept of turning data into information Describe the relationships between DW‚ BI‚ and Data Insight Identify the components of a DW architecture Summarize the Insight requirements and goals of
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Data Mining Information Systems for Decision Making 10 December 2013 Abstract Data mining the next big thing in technology‚ if used properly it can give businesses the advance knowledge of when they are going to lose customers or make them happy. There are many benefits of data mining and it can be accomplished in different ways. The problem with data mining is that it is only as reliable as the data going in and the way it is handled. There are also privacy concerns with data mining
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Unit 1 Problem Set 1: Using Statistical Thinking and Summarizing Data 1. (Page 11 #26) – a. Yes b. Yes‚ because there is about a 1 chance in 1000 of getting the types of success rates generated through the study. c. Yes‚ because a 92% success rate is a much better result than a 72% success rate. d. Yes‚ after the study has been conducted numerous times to have validity. 2. (Page 17 #30) – a. The sample are the 1012 randomly surveyed adults. b. The population is that of all adults.
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DATA ORGANIZATION‚ PRESENTATION AND ANALYSIS Research Methods 1 Data Organization and Presentation To make interpretation and analysis of gathered data easier‚ data should be organized and presented properly. The usual methods used by researchers are textual‚ tables‚ graphs and charts. 1.1 Textual Data can be presented in the form of texts‚ phrases or paragraphs. It involves enumerating important characteristics‚ emphasizing significant figures and identifying important features of
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Cisco Systems‚ Inc. is an American multinational corporation headquartered in San Jose‚ California‚ that designs‚ manufactures‚ and sells networking equipment. The stock was added to the Dow Jones Industrial Average on June 8‚ 2009‚ and is also included in the S&P 500 Index‚ the Russell 1000 Index‚ NASDAQ 100 Index and the Russell 1000 Growth Stock Index. The phenomenal growth of the Internet in mid-to-late 1990s quickly changed the telecom landscape. As the Internet Protocol (IP) became widely
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Master Thesis Electrical Engineering November 2011 Security Techniques for Protecting Data in Cloud Computing Venkata Sravan Kumar Maddineni Shivashanker Ragi School of Computing Blekinge Institute of Technology SE - 371 79 Karlskrona Sweden i This thesis is submitted to the School of Computing at Blekinge Institute of Technology in partial fulfillment of the requirements for the degree of Master of Science in Software Engineering. The thesis is equivalent to 40 weeks of full time studies.
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Data Warehouse Concepts and Design Contents Data Warehouse Concepts and Design 1 Abstract 2 Abbreviations 2 Keywords 3 Introduction 3 Jarir Bookstore – Applying the Kimball Method 3 Summary from the available literature and Follow a Proven Methodology: Lifecycle Steps and Tracks 4 Issues and Process involved in Implementation of DW/BI system 5 Data Model Design 6 Star Schema Model 7 Fact Table 10 Dimension Table: 11 Design Feature: 12 Identifying the fields from facts/dimensions: MS: 12 Advanced
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