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  <titleInfo>
    <title>Data analytics</title>
    <subTitle>handbook of formulas and techniques</subTitle>
  </titleInfo>
  <name type="personal">
    <namePart>Badiru, Adedeji Bodunde</namePart>
    <namePart type="date">1952-</namePart>
    <role>
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    <dateIssued encoding="marc">2020</dateIssued>
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  <physicalDescription>
    <extent>1 online resource : illustrations (black and white).</extent>
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  <abstract>Good data analytics is the basis for effective decisions. Whoever has the data, has the ability to extract information promptly and effectively to make pertinent decisions. The premise of this handbook is to empower users and tool developers with the appropriate collection of formulas and techniques for data analytics and to serve as a quick reference to keep pertinent formulas within fingertip reach of readers. This handbook includes formulas that will appeal to mathematically inclined readers. It discusses how to use data analytics to improve decision-making and is ideal for those new to using data analytics to show how to expand their usage horizon. It provides quantitative techniques for modeling pandemics, such as COVID-19. It also adds to the suite of mathematical tools for emerging technical areas. This handbook is a handy reference for researchers, practitioners, educators, and students in areas such as industrial engineering, production engineering, project management, civil engineering, mechanical engineering, technology management, and business management worldwide.</abstract>
  <note type="statement of responsibility">Adedeji Badiru.</note>
  <subject authority="lcsh">
    <topic>Quantitative research</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Information visualization</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Engineering mathematics</topic>
  </subject>
  <subject authority="lcsh">
    <topic>Business mathematics</topic>
  </subject>
  <subject authority="bisacsh">
    <topic>COMPUTERS / Database Management / Data Mining</topic>
  </subject>
  <subject authority="bisacsh">
    <topic>TECHNOLOGY / Operations Research</topic>
  </subject>
  <subject authority="bisacsh">
    <topic>MATHEMATICS / Mathematical Analysis</topic>
  </subject>
  <classification authority="lcc">QA76.9.Q36</classification>
  <classification authority="ddc" edition="23">001.42</classification>
  <identifier type="isbn">9781000297355</identifier>
  <identifier type="isbn">1000297357</identifier>
  <identifier type="isbn">1000297330</identifier>
  <identifier type="isbn">9781000297348</identifier>
  <identifier type="isbn">1000297349</identifier>
  <identifier type="isbn">9781003083146</identifier>
  <identifier type="isbn">1003083145</identifier>
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  <identifier type="uri">http://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf</identifier>
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