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220922 ||| eng |
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|a 9781000514117
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|a 1003165273
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|a 9781003165279
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|a 9781000514100
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|a 1000514110
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4 |
|a T58.6
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| 100 |
1 |
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|a Jugulum, Rajesh
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| 245 |
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|a Common Data Sense for Professionals
|b a Process-Oriented Approach for Data-Science Projects
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| 250 |
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|a First edition
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| 260 |
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|a [Place of publication not identified]
|b Productivity Press
|c 2021
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| 300 |
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|a xviii, 100 pages
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| 653 |
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|a Big data / fast
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| 653 |
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|a Gestion / Informatique
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| 653 |
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|a Big data / http://id.loc.gov/authorities/subjects/sh2012003227
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| 653 |
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|a Données volumineuses
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| 653 |
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|a Decision making / Data processing
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| 653 |
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|a Business / Data processing / http://id.loc.gov/authorities/subjects/sh85018264
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| 653 |
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|a Business / Data processing / fast
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| 653 |
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|a Prise de décision / Informatique
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| 653 |
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|a Decision making / Data processing / fast
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| 653 |
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|a Business intelligence / http://id.loc.gov/authorities/subjects/sh85018300
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| 653 |
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|a Business intelligence / fast
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 500 |
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|a Foreword 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
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| 776 |
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|z 9780367760502
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| 776 |
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|z 9780367760489
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| 776 |
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|z 9781003165279
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| 776 |
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|z 1003165273
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| 856 |
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|u https://learning.oreilly.com/library/view/~/9781000514117/?ar
|x Verlag
|3 Volltext
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|a 153.83
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|a 658.4038
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|a 330
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| 520 |
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|a 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 --
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| 520 |
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|a 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
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| 520 |
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|a Data 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.
|