SKETCH OF SPECIALIZATION REPORT & EVALUATION FORMATS - 2014 S.No Content Page No. 1 Sketch of Report on Specialization Project 2-3 2 Comprehensive comparative table (Review of Literature) - Annexure -I 4 3 Sequence of the Report- Annexure-II 5 4 Evaluation weightage-Annexure -III 5 5 EVALUATION FORMAT- SP-2014 - At the end of 1st Week 6-7 6 EVALUATION FORMAT - SP-2014 - At the end of 2nd Week 7-8 7 EVALUATION FORMAT - SP-2014 - At the end of 3rd Week
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decision making, as organizations realize the true value of information and how it can affect the bottom line. The majority of successful organizations will employ business analysis staff to ensure that the data they get is properly interpreted to help support business-critical choices and strategies. By making use of the swathes of data available about any number of metrics within a company, clear and relevant insight into issues can be translated into more effective decisions and outcomes, based on verifiable
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Business leaders rely on the work of marketing researchers to make decisions that will impact revenue generation and the profitability of the company. The data analysis and interpretation phase of a marketing research is a critical process that is highly scrutinized for accuracy and validity. Marketing researchers have to take great care in analyzing data that will be interpreted and turned into information for the purpose of solving the research problem, or capitalizing on an opportunity that was under
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2014 Data Science Salary Survey Tools, Trends, What Pays (and What Doesn’t) for Data Professionals John King & Roger Magoulas Take the Data Science Salary and Tools Survey As data analysts and engineers—as professionals who like nothing better than petabytes of rich data—we find ourselves in a strange spot: We know very little about ourselves. But that’s changing. This salary and tools survey is the second in an annual series. To keep the insights flowing, we need one thing: People
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Study – August 2013 Big Data Analytics How Cisco IT Built Big Data Platform to Transform Data Management EXECUTIVE SUMMARY CHALLENGE ● Unlock the business value of large data sets, including structured and unstructured information ● Provide service-level agreements (SLAs) for internal customers using big data analytics services ● Support multiple internal users on same platform SOLUTION ● Implemented enterprise Hadoop platform on Cisco UCS CPA for Big Data - a complete infrastructure
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Initially, I used secondary data analysis to find out what factors have had an effect on ex-offenders. What lacked in previous studies are the long-term effects of these offenders’ family lives, values, socioeconomic status, prison programs/schooling, and their transitions. Therefore, while most research done on the impact consists of one factor alone, I incorporated all of these in to my study. By using existing data and disguised field observation, I was able to compare the data to real people who have
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Kengeri Campus, Bangalore. Review On Cluster Analysis Submitted for the partial fulfillment of process related to Continuous Internal Assessment 1 In Marketing Analytics [MBA - III Trimester (MBA 322)] Submitted By P.Akhilesh 1420419 Cluster analysis? The Cluster Analysis is an explorative analysis that tries to identify structures within the data. Cluster analysis is also called segmentation analysis .It is used to identify groups of cases if the alignment
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As a group, we chose Data Mining for our project and in specific, an organization very popular and well used, Facebook. Facebook was founded on February 4, 2004 and its mission or purpose was to provide a social media platform. There is not a subscription cost and the platform is freely available to anyone with an internet connection and email address. However, there are advertising tools which one can pay for to ensure your various posts reach more of an audience. For example, you can set up
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summarizing data. Furthermore, you were shown how crosstabulations and scatter diagrams can be used to summarize data for two variables simultaneously. The terms that you should have learned from this chapter include: A. Qualitative Data: Data that are measured by either nominal or ordinal scales of measurement. Each value serves as a name or label for identifying an item. B. Quantitative Data: Data that are measured by interval or ratio scales of measurement. Quantitative data are numerical
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The core drivers of the information age are: Data, Information, Business Intelligence and Knowledge. Data: Data are raw facts that describe the characteristics of an event or object. Before the information age, collecting and analyzing data is a time consuming and complicated task for managers as they used to do it manually. Managers often found difficulty in making business decisions about how many employees to hire, how much material to order. They used to do it based on intuition or gut
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