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    Big Data

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    Trang Vuong Big Data and Its Potentials Data exists everywhere nowadays. It flows to every area of the economy and plays an important role in the decision-making process. Indeed‚ “businesses‚ industries‚ governments‚ universities‚ scientists‚ consumers‚ and nonprofits are generating data at unprecedented levels and at an incredible pace” to ensure the accuracy and reliability of their data-driven decisions (Gordon-Murnane 30). Especially when technology and economy are growing at an unbelievable

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    Data Comm

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    DATA COMMUNICATION (Basics of data communication‚ OSI layers.) K.K.DHUPAR SDE (NP-II) ALTTC ALTTC/NP/KKD/Data Communication 1 Data Communications History • 1838: Samuel Morse & Alfred Veil Invent Morse Code Telegraph System • 1876: Alexander Graham Bell invented Telephone • 1910:Howard Krum developed Start/Stop Synchronisation ALTTC/NP/KKD/Data Communication 2 History of Computing • 1930: Development of ASCII Transmission Code • 1945: Allied Governments develop the First Large Computer

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    Big Data

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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 Center

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    The Difference Between Data Centers and Computer Rooms By Peter Sacco Experts for Your Always Available Data Center White Paper #1 EXECUTIVE SUMMARY The differences between a data center and a computer room are often misunderstood. Furthermore‚ the terms used to describe the location where companies provide a secure‚ power protected‚ and environmentally controlled space are often used inappropriately. This paper provides a basis for understanding the differences between these locations

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    Statistics and Data

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    Simply use statistics as a tool. You will be given a data. (Next year you will not be given data‚ you will gather data yoruself). 1. Data: one of the variables is dependent and other dependent. Can be multiple. Then do regression analysis. ANOVA for overall significance and Regression equation. And write based on ANOVA there is a significance or not. 2. Some comments on correlation: volume vs. horse power etc. 3. Hypothesis test of one population. I assume that the mean is etc etc. Small paragraph

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    Big Data

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    Networks Volvo utilized data mining in an effort to discover the unknown valuable relationships in the data collected and to assist in making early predictive information. It created a network of sensors and CPUs that were embedded throughout the cars and from which data was captured. Data was also captured from customer relationship systems (CRM)‚ dealership systems‚ product development and design systems and from the production floors in their factories. The terabytes of data collected was streamed

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    Comparing Data

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    Using the Standard Deviation You made a number of observations about the data sets for the school activities. You used mean and median to measure the center of the data‚ and you used the interquartile range (IQR) to measure the spread. When outliers are present‚ the median and IQR are used to measure center and spread because they are unaffected by extreme values. When the data appears to be symmetric and there are no known outliers‚ the mean and standard deviation (another measure of spread)

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    Data Center

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    Increase Your Data Center Energy Efficiency • Increase Your Data Center Energy Efficiency • Increase Your Data Center Energy Efficiency • Increase Your Data Center Energy Efficiency • Increase Key Best Practices Optimize the Central Plant Quick Start Guide to Increase Data Center Energy How To Start A Problem That You Can Fix Data Center energy efficiency is derived from addressing BOTH your hardware equipment AND your infrastructure. Commit to Improved Design and Operations

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    Hr Data

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    team‚ collect different types of data. One of them is meeting legal requirements. In order to satisfy legal obligations we collect such information as contractual arrangements‚ employees’ duties‚ privileges‚ salaries‚ working hours‚ vacation accruals‚ bonuses‚ as well as documents relating to health and safety. The Russian Labor Inspection can check any data regarding individual employees and it is important for the organization to timely provide accurate and valid data in order to avoid fees or other

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    Data Warehousing

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    and Kimball’s definition of Data Warehousing. Bill Inmon advocates a top-down development approach that adapts traditional relational database tools to the development needs of an enterprise wide data warehouse. From this enterprise wide data store‚ individual departmental databases are developed to serve most decision support needs. Ralph Kimball‚ on the other hand‚ suggests a bottom-up approach that uses dimensional modeling‚ a data modeling approach unique to data warehousing. Rather than building

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