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001 9781003165279
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008 211007s2021 xx fo 000 0 eng d
040 _aOCoLC-P
_beng
_erda
_epn
_cOCoLC-P
020 _a9781003165279
_q(electronic bk.)
020 _a1003165273
_q(electronic bk.)
020 _a9781000514117
_q(electronic bk. : EPUB)
020 _a1000514110
_q(electronic bk. : EPUB)
020 _z9780367760502
020 _a9781000514100
_q(electronic bk. : PDF)
020 _a1000514102
_q(electronic bk. : PDF)
020 _z9780367760489
035 _a(OCoLC)1273728005
035 _a(OCoLC-P)1273728005
050 4 _aT58.6
072 7 _aBUS
_x043060
_2bisacsh
072 7 _aBUS
_x063000
_2bisacsh
072 7 _aBUS
_x041000
_2bisacsh
072 7 _aKJMV3
_2bicssc
082 0 4 _a658.4038
_223
100 1 _aJugulum, Rajesh.
245 1 0 _aCommon Data Sense for Professionals :
_bA Process-Oriented Approach for Data-Science Projects.
250 _aFirst edition.
264 1 _a[Place of publication not identified] :
_bProductivity Press,
_c2021.
300 _a1 online resource (xviii, 100 pages).
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
505 0 _aForeword Preface AcknowledgementsOverviewChapter 1: The Meeting of Manju and JimChapter 2: Understanding the ProblemChapter 3: Analyzing the Problem and Collecting DataChapter 4: Creating and Analyzing ModelsChapter 5: Project StructureChapter 6: Data Science Stories and Case Example AnalysisChapter 7: Concept ReviewChapter 8: Manju and Jim's Concluding MeetingReferences
520 _aData is an intrinsic part of our daily lives. Everything we do is a data point. Many of these data points are recorded with the intent to help us lead more efficient lives. We have apps that track our workouts, sleep, food intake, and personal finance. We use the data to make changes to our lives based on goals we have set for ourselves. Businesses use vast collections to determine strategy and marketing. Data scientists take data, analyze it and create models to help solve problems. You may have heard of companies having data management teams, or Chief Information Officers (CIO) or Chief Analytics Officers (CAO), etc. These are all people that work with data, but their function is more related to vetting data and preparing it for use by data scientists. The jump from personal data usage for self-betterment to mass data analysis for business process improvement often feels bigger to us than it is. In turn, we often think big data analysis requires tools held only by advanced degree holders. Though an advanced degrees are certainly valuable, this book illustrates how it is not a requirement to adequately run a data science project. Because we are all already data users, with some simple strategies and exposure to basic statistical analysis software programs, anyone who has the proper tools and determination can solve data science problems. The process presented in this book will help empower individuals to work on and solve data- related challenges. The goal for this book is to provide a step-by-step guide to the data science process so that you can feel confident in leading your own data science project. To aid with clarity and understanding, the author presents a fictional restaurant chain to use as a case study -- it illustrates how the various topics discussed can be applied. Essentially, this book helps traditional business people to solve data related problems on their own without any hesitation or fear. The powerful methods are presented in the form of conversations, examples, and case studies. The conversational style is engaging and provides clarity.
588 _aOCLC-licensed vendor bibliographic record.
650 0 _aBusiness
_xData processing.
650 0 _aDecision making
_xData processing.
650 0 _aBig data.
650 0 _aBusiness intelligence.
650 7 _aBUSINESS & ECONOMICS / Marketing / Research
_2bisacsh
650 7 _aBUSINESS & ECONOMICS / Strategic Planning
_2bisacsh
650 7 _aBUSINESS & ECONOMICS / Management
_2bisacsh
856 4 0 _3Taylor & Francis
_uhttps://www.taylorfrancis.com/books/9781003165279
856 4 2 _3OCLC metadata license agreement
_uhttp://www.oclc.org/content/dam/oclc/forms/terms/vbrl-201703.pdf
999 _c5939
_d5939