Section:04 Spring2010‚ Email ID:0630059 I have asked 20 people who live in Mirpur about their choice of soft drink they used to buy. Their answer s(my data set) are given below. Dataset 01: mojo mojo mojo mojo rc coke rc rc sprite coke mojo rc rc lemu lemu sprite lemu mojo sprite 7up ice Qualitative data analysis: from this data set we get to know the name and the number of the brands they choose and 2o people’s frequency of choosing among these brands. class Frequency
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Stock Exchange forecasting with Data Mining and Text Mining (Marketing and Sales Analysis) Full names : Fahed Yoseph TITLE : Senior software and Database Consultatnt (Founder of Info Technology System) E-mail: Yoseph@info-technology.net Date of submission: Sep 15th of 2013 CONTENTS PAGE Chapter 1 1. ABSTRACT 2 2. INTRODUCTION 3 2.1 The research problem. 4 2.2 The objectives of the proposal. 4 2.3 The Stock Market movement. 5 2.4 Research question(s). 6 2. Background 3. Problem
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Services E20-007 Data Science and Big Data Analytics Exam Exam Description Overview This exam focuses on the practice of data analytics‚ the role of the Data Scientist‚ the main phases of the Data Analytics Lifecycle‚ analyzing and exploring data with R‚ statistics for model building and evaluation‚ the theory and methods of advanced analytics and statistical modeling‚ the technology and tools that can be used for advanced analytics‚ operationalizing an analytics project‚ and data visualization techniques
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DATA INTEGRATION Data integration involves combining data residing in different sources and providing users with a unified view of these data. This process becomes significant in a variety of situations‚ which include both commercial (when two similar companies need to merge their databases and scientific (combining research results from different bioinformatics repositories‚ for example) domains. Data integration appears with increasing frequency as the volume and the need to share existing data explodes
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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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The importance of data for operations management and decision making In order to be able to make well guided decisions‚ one needs well based facts and therefore one is in continuous need of quality data. The same goes for operations management; data of substance is a must to run a company in its optimal levels of efficiency‚ effectiveness and capacity. The five levels of Data Quality Maturity according to Gartner are Aware‚ Reactive‚ Proactive‚ Managed and Optimized. Using these levels and applying
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Introduction Data communications (Datacom) is the engineering discipline concerned with communication between the computers. It is defined as a subset of telecommunication involving the transmission of data to and from computers and components of computer systems. More specifically data communication is transmitted via mediums such as wires‚ coaxial cables‚ fiber optics‚ or radiated electromagnetic waves such as broadcast radio‚ infrared light‚ microwaves‚ and satellites. Data Communications =
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Project 2) System Analysis i) Introduction ii) Existing System iii) Drawback of the Existing System iv) Proposed System v) Objective and Advantage of Proposed System vi) Software Requirement Specification vii) Data Flow Diagram viii) Gantt Chart 3) System Design i) Introduction ii) Design Strategy iii) Input Design iv) Output Design v) Table Design 4) Tools Chosen i) Proposed System Environment ii) About Front
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it for any other purpose. DATE: 06/10/2013 Introduction: In data mining it is said that “success or failure often depends not only on how well you are able to collect data but also on how well you are able to convert them into knowledge that will help you better manage your business (Wilson‚ 2001‚ p. 26).” Tourism and hospitality industry generates massive amount of data. In each and every transaction there is set of data generated. In tourism and hospitality‚ knowing your customer is very
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Components of DSS (Decision Support System) Data Store – The DSS Database Data Extraction and Filtering End-User Query Tool End User Presentation Tools Operational Stored in Normalized Relational Database Support transactions that represent daily operations (Not Query Friendly) Differences with DSS 3 Main Differences Time Span Granularity Dimensionality Operational DSS Time span Real time Historic Current transaction Short time frame Long time frame Specific Data facts Patterns Granularity Specific
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