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210123 ||| eng |
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|a 9780124016811
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|a 0124016812
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4 |
|a T57.6
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|a Sheikh, Nauman Mansoor
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|a Implementing analytics
|b a blueprint for design, development, and adoption
|c Nauman Sheikh
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| 260 |
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|a Burlington
|b Elsevier Science
|c 2013
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| 300 |
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|a xxi, 211 pages
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| 505 |
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|a Includes bibliographical references and index
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| 505 |
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|a Defining analytics -- Information continuum -- Using analytics -- Performance variables and model development -- Automated decisions and business innovation -- Governance: monitoring and tuning of analytics -- Analytics adoption roadmap -- Requirements gathering for analytics projects -- Analytics implementation methodology -- Analytics organization and architecture -- Big data, Hadoop and cloud computing
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| 653 |
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|a System analysis / fast
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| 653 |
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|a Analyse de systèmes
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| 653 |
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|a System analysis
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| 653 |
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|a systems analysis / aat
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| 653 |
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|a SCIENCE / System Theory / bisacsh
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| 653 |
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|a TECHNOLOGY & ENGINEERING / Operations Research / bisacsh
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| 041 |
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|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 490 |
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|a The Morgan Kaufmann Series on Business Intelligence
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| 776 |
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|z 0124016960
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| 776 |
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|z 9780124016965
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| 776 |
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|z 0124016812
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| 776 |
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|z 9780124016811
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| 856 |
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|u https://learning.oreilly.com/library/view/~/9780124016965/?ar
|x Verlag
|3 Volltext
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|a 500
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|a 620
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|a 003
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|a Implementing Analytics demystifies the concept, technology and application of analytics and breaks its implementation down to repeatable and manageable steps, making it possible for widespread adoption across all functions of an organization. Implementing Analytics simplifies and helps democratize a very specialized discipline to foster business efficiency and innovation without investing in multi-million dollar technology and manpower. A technology agnostic methodology that breaks down complex tasks like model design and tuning and emphasizes business decisions rather than the technology behind analytics. Simplifies the understanding of analytics from a technical and functional perspective and shows a wide array of problems that can be tackled using existing technology Provides a detailed step by step approach to identify opportunities, extract requirements, design variables and build and test models. It further explains the business decision strategies to use analytics models and provides an overview for governance and tuning Helps formalize analytics projects from staffing, technology and implementation perspectives Emphasizes machine learning and data mining over statistics and shows how the role of a Data Scientist can be broken down and still deliver the value by building a robust development process
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