WORLD DATA CLUSTERING ADEWALE .O . MAKO DATA MINING INTRODUCTION: Data mining is the analysis step of knowledge discovery in databases or a field at the intersection of computer science and statistics. It is also the analysis of large observational datasets to find unsuspected relationships. This definition refers to observational data as opposed to experimental data. Data mining typically deals with data that has already been collected for some purpose or the other than the data mining
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Mohammed Al Bittar - 1006091 1. What is meant by big data? A term used for complex sets of data which becomes very difficult to process‚ manage‚ or capture by commonly-used software. 2. What is meaning of data-driven culture? A culture where decisions made upon analyzing real statistical information. Like how Wal-Mart checks on the weather in order to provide more products to the customers; because their statistical information shows that whenever there is a storm‚ customers by
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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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Be Data Literate – Know What to Know by Peter F. Drucker Executives have become computer literate. The younger ones‚ especially‚ know more about the way the computer works than they know about the mechanics of the automobile or the telephone. But not many executives are information-literate. They know how to get data. But most still have to learn how to use data. Few executives yet know how to ask: What information do I need to do my job? When do I need it? In what
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Data Collection QNT/351 July 10‚ 2014 There are many times when companies have to collect data to come to a conclusion about an issue. The data may be collected from their employers‚ their competition or their consumers. BIMS saw that there had been an average turnover that was larger then what the company had seen in the past. Human Resources decided that they would conduct a survey to see what had changed in the company from the employee’s point of view. They attached
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Turnage‚ Bonebright‚ Buhman‚ Flowers (1996) showed that untrained participants can listen to shapes. That is‚ they used data sonification – musical representation of two dimensional space‚ with pitch as the vertical dimension and time as the horizontal dimension – to present participants the visual and auditory representation of waveforms. In two conditions‚ they showed the participants could match one visual presentation to one of two auditory representations‚ or match one auditory presentation
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of variables Qualitative Quantitative • Reliability and Validity • Hypothesis Testing • Type I and Type II Errors • Significance Level • SPSS • Data Analysis Data Analysis Using SPSS Dr. Nelson Michael J. 2 Variable • A characteristic of an individual or object that can be measured • Types: Qualitative and Quantitative Data Analysis Using SPSS Dr. Nelson Michael J. 3 Types of Variables • Qualitative variables: Variables which differ in kind rather than degree • Measured
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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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Data Collection: Data collection is the heart of any research. No study is complete without the data collection. This research also includes data collection and was done differently for different type of data. TYPES OF DATA Primary Data: For the purpose of collecting maximum primary data‚ a structured questionnaire was used wherein questions pertaining to the satisfaction level of the customer about pantaloons product(apparel)‚ the quality‚ color‚ variety of products‚ the availability of different
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measures widely used to measure complexity in manufacturing systems. With reference to this second framework‚ two indexes were selected (static and dynamic complexity index) and a Business Dynamic model was developed. This model was used with empirical data collected in a job shop manufacturing system in order to test the usefulness and validity of the dynamic complex index. The Business Dynamic model analyzed the trend of the index in function of different inputs in a selected work center. The results
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