大數(shù)據(jù)導(dǎo)論（英文版）簡(jiǎn)介，目錄書(shū)摘
2020-02-07 21:29
來(lái)源：京東
作者：京東
大數(shù)據(jù)導(dǎo)論（英文版）
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內(nèi)容簡(jiǎn)介:本書(shū)是面向商業(yè)和技術(shù)專業(yè)人員的大數(shù)據(jù)指南，清楚地介紹了大數(shù)據(jù)相關(guān)的概念、理論、術(shù)語(yǔ)與基礎(chǔ)技術(shù)，并使用真實(shí)連貫的商業(yè)案例以及簡(jiǎn)單的圖表，幫助讀者更清晰地理解大數(shù)據(jù)技術(shù)。本書(shū)可作為高等院校相關(guān)專業(yè)“大數(shù)據(jù)基礎(chǔ)”“大數(shù)據(jù)導(dǎo)論”等課程的教材，也可供有一定實(shí)踐經(jīng)驗(yàn)的軟件開(kāi)發(fā)人員、管理人員和所有對(duì)大數(shù)據(jù)感興趣的人士閱讀。
目錄:Contents
PART I: THE FUNDAMENTALS OF BIG DATA
CHAPTER 1: Understanding Big Data 3
Concepts and Terminology 5
Datasets 5
Data Analysis 6
Data Analytics 6
Descriptive Analytics 8
Diagnostic Analytics 9
Predictive Analytics 10
Prescriptive Analytics 11
Business Intelligence (BI) 12
Key Performance Indicators (KPI) 12
Big Data Characteristics 13
Volume 14
Velocity 14
Variety 15
Veracity 16
Value 16
Different Types of Data 17
Structured Data 18
Unstructured Data 19
Semi-structured Data 19
Metadata 20
Case Study Background 20
History 20
Technical Infrastructure and Automation Environment 21
Business Goals and Obstacles 22
Case Study Example 24
Identifying Data Characteristics 26
Volume 26
Velocity 26
Variety 26
Veracity 26
Value 27
Identifying Types of Data 27
CHAPTER 2: Business Motivations and Drivers for Big Data Adoption 29
Marketplace Dynamics 30
Business Architecture 33
Business Process Management 36
Information and Communications Technology 37
Data Analytics and Data Science 37
Digitization 38
Affordable Technology and Commodity Hardware 38
Social Media 39
Hyper-Connected Communities and Devices 40
Cloud Computing 40
Internet of Everything (IoE) 42
Case Study Example 43
CHAPTER 3: Big Data Adoption and Planning Considerations 47
Organization Prerequisites 49
Data Procurement 49
Privacy 49
Security 50
Provenance 51
Limited Realtime Support 52
Distinct Performance Challenges 53
Distinct Governance Requirements 53
Distinct Methodology 53
Clouds 54
Big Data Analytics Lifecycle 55
Business Case Evaluation 56
Data Identification 57
Data Acquisition and Filtering 58
Data Extraction 60
Data Validation and Cleansing 62
Data Aggregation and Representation 64
Data Analysis 66
Data Visualization 68
Utilization of Analysis Results 69
Cas
PART I: THE FUNDAMENTALS OF BIG DATA
CHAPTER 1: Understanding Big Data 3
Concepts and Terminology 5
Datasets 5
Data Analysis 6
Data Analytics 6
Descriptive Analytics 8
Diagnostic Analytics 9
Predictive Analytics 10
Prescriptive Analytics 11
Business Intelligence (BI) 12
Key Performance Indicators (KPI) 12
Big Data Characteristics 13
Volume 14
Velocity 14
Variety 15
Veracity 16
Value 16
Different Types of Data 17
Structured Data 18
Unstructured Data 19
Semi-structured Data 19
Metadata 20
Case Study Background 20
History 20
Technical Infrastructure and Automation Environment 21
Business Goals and Obstacles 22
Case Study Example 24
Identifying Data Characteristics 26
Volume 26
Velocity 26
Variety 26
Veracity 26
Value 27
Identifying Types of Data 27
CHAPTER 2: Business Motivations and Drivers for Big Data Adoption 29
Marketplace Dynamics 30
Business Architecture 33
Business Process Management 36
Information and Communications Technology 37
Data Analytics and Data Science 37
Digitization 38
Affordable Technology and Commodity Hardware 38
Social Media 39
Hyper-Connected Communities and Devices 40
Cloud Computing 40
Internet of Everything (IoE) 42
Case Study Example 43
CHAPTER 3: Big Data Adoption and Planning Considerations 47
Organization Prerequisites 49
Data Procurement 49
Privacy 49
Security 50
Provenance 51
Limited Realtime Support 52
Distinct Performance Challenges 53
Distinct Governance Requirements 53
Distinct Methodology 53
Clouds 54
Big Data Analytics Lifecycle 55
Business Case Evaluation 56
Data Identification 57
Data Acquisition and Filtering 58
Data Extraction 60
Data Validation and Cleansing 62
Data Aggregation and Representation 64
Data Analysis 66
Data Visualization 68
Utilization of Analysis Results 69
Cas
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