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EBOOKCENTRAL_on1228037740 |
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OCoLC |
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20240329122006.0 |
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m o d |
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201226s2020 xx o ||| 0 eng d |
| 040 |
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|a EBLCP
|b eng
|c EBLCP
|d EBLCP
|d REDDC
|d OCLCF
|d OCLCQ
|d OCLCO
|d OCLCQ
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| 020 |
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|a 9781119544166
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| 020 |
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|a 1119544165
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| 035 |
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|a (OCoLC)1228037740
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|a QA76.9.D343
|b .C339 2021
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0 |
4 |
|a 006.312
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| 049 |
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|a UAMI
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| 100 |
1 |
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|a Cady, Field.
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| 245 |
1 |
0 |
|a Data Science
|h [electronic resource] :
|b The Executive Summary - a Technical Book for Non-Technical Professionals.
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| 260 |
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|a Newark :
|b John Wiley & Sons, Incorporated,
|c 2020.
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| 300 |
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|a 1 online resource (211 p.)
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| 500 |
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|a Description based upon print version of record.
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| 505 |
0 |
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|a Cover -- Title Page -- Copyright -- Contents -- Chapter 1 Introduction -- 1.1 Why Managers Need to Know About Data Science -- 1.2 The New Age of Data Literacy -- 1.3 Data-Driven Development -- 1.4 How to Use this Book -- Chapter 2 The Business Side of Data Science -- 2.1 What Is Data Science? -- 2.1.1 What Data Scientists Do -- 2.1.2 History of Data Science -- 2.1.3 Data Science Roadmap -- 2.1.4 Demystifying the Terms: Data Science, Machine Learning, Statistics, and Business Intelligence -- 2.1.4.1 Machine Learning -- 2.1.4.2 Statistics -- 2.1.4.3 Business Intelligence
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| 505 |
8 |
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|a 2.1.5 What Data Scientists Don't (Necessarily) Do -- 2.1.5.1 Working Without Data -- 2.1.5.2 Working with Data that Can't Be Interpreted -- 2.1.5.3 Replacing Subject Matter Experts -- 2.1.5.4 Designing Mathematical Algorithms -- 2.2 Data Science in an Organization -- 2.2.1 Types of Value Added -- 2.2.1.1 Business Insights -- 2.2.1.2 Intelligent Products -- 2.2.1.3 Building Analytics Frameworks -- 2.2.1.4 Offline Batch Analytics -- 2.2.2 One-Person Shops and Data Science Teams -- 2.2.3 Related Job Roles -- 2.2.3.1 Data Engineer -- 2.2.3.2 Data Analyst -- 2.2.3.3 Software Engineer
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| 505 |
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|a 2.3 Hiring Data Scientists -- 2.3.1 Do I Even Need Data Science? -- 2.3.2 The Simplest Option: Citizen Data Scientists -- 2.3.3 The Harder Option: Dedicated Data Scientists -- 2.3.4 Programming, Algorithmic Thinking, and Code Quality -- 2.3.5 Hiring Checklist -- 2.3.6 Data Science Salaries -- 2.3.7 Bad Hires and Red Flags -- 2.3.8 Advice with Data Science Consultants -- 2.4 Management Failure Cases -- 2.4.1 Using Them as Devs -- 2.4.2 Inadequate Data -- 2.4.3 Using Them as Graph Monkeys -- 2.4.4 Nebulous Questions -- 2.4.5 Laundry Lists of Questions Without Prioritization
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| 505 |
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|a Chapter 3 Working with Modern Data -- 3.1 Unstructured Data and Passive Collection -- 3.2 Data Types and Sources -- 3.3 Data Formats -- 3.3.1 CSV Files -- 3.3.2 JSON Files -- 3.3.3 XML and HTML -- 3.4 Databases -- 3.4.1 Relational Databases and Document Stores -- 3.4.2 Database Operations -- 3.5 Data Analytics Software Architectures -- 3.5.1 Shared Storage -- 3.5.2 Shared Relational Database -- 3.5.3 Document Store + Analytics RDB -- 3.5.4 Storage + Parallel Processing -- Chapter 4 Telling the Story, Summarizing Data -- 4.1 Choosing What to Measure
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| 505 |
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|a 4.2 Outliers, Visualizations, and the Limits of Summary Statistics: A Picture Is Worth a Thousand Numbers -- 4.3 Experiments, Correlation, and Causality -- 4.4 Summarizing One Number -- 4.5 Key Properties to Assess: Central Tendency, Spread, and Heavy Tails -- 4.5.1 Measuring Central Tendency -- 4.5.1.1 Mean -- 4.5.1.2 Median -- 4.5.1.3 Mode -- 4.5.2 Measuring Spread -- 4.5.2.1 Standard Deviation -- 4.5.2.2 Percentiles -- 4.5.3 Advanced Material: Managing Heavy Tails -- 4.6 Summarizing Two Numbers: Correlations and Scatterplots -- 4.6.1 Correlations -- 4.6.1.1 Pearson Correlation
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| 590 |
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|a ProQuest Ebook Central
|b Ebook Central Academic Complete
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| 650 |
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0 |
|a Data mining.
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| 650 |
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6 |
|a Exploration de données (Informatique)
|
| 650 |
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7 |
|a Data mining
|2 fast
|
| 776 |
0 |
8 |
|i Print version:
|a Cady, Field
|t Data Science
|d Newark : John Wiley & Sons, Incorporated,c2020
|z 9781119544081
|
| 856 |
4 |
0 |
|u https://ebookcentral.uam.elogim.com/lib/uam-ebooks/detail.action?docID=6413916
|z Texto completo
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| 938 |
|
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|a ProQuest Ebook Central
|b EBLB
|n EBL6413916
|
| 994 |
|
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|a 92
|b IZTAP
|
