大数据分析基础:概念.技术.方法和商务(英文版)

Part One Basics and Concepts Chapter 1 Introduction 1.1 What Is Big Data Analytics 1.1.1 Big Data Analytics Requires Data-Driven Business Culture 1.1.2 Big Data Analytics Requires High-Performance Analyses 1.2 Why Big Data Analytics 1.2.1 History and Evolution of Big Data Analytics 1.2.2 The Drivers of Big Data Analytics 1.2.3 Why Is Big Data Analytics Important 1.2.4 The Challenges of Big Data Analytics 1.2.5 How Big Data Analytics Is Used Today 1.3 Big Data Analytics Applications 1.3.1 Industries Where Big Data Analytics Are Successful 1.3.2 Four Powerful Big Data Analytics Application Examples 1.4 The Big Data Analytics Market 1.5 Big Data Analytics Future Trends 1.5.1 Predictive Analytics Will Dominate 1.5.2 Refocusing on the Human Decision-Making 1.5.3 Market Segmentation in Data Analysis Platforms 1.5.4 Open Source Software Tools 1.5.5 Plug-in AI Technologies 1.6 The Contents of Big Data Analytics 1.7 References 1.8 Review Questions and Exercises Chapter 2 Data and Big Data 2.1 Data as a Basic Entity in the DIKW Framework 2.1.1 DIKW Framework 2.1.2 Data Object, Data Attribute and Data Set 2.1.3 Data Attribute Types 2.2 Big Data 2.2.1 Big Data Definition 2.2.2 Big Data Types 2.3 Quality of Data and Big Data 2.3.1 Definition of Data Quality 2.3.2 Data Measurement and Data Collection 2.3.3 Errors in Measurement and Collection 2.3.4 Data Accuracy 2.4 Basic Measurement of Dataset 2.5 Summary 2.6 References 2.7 Review Questions Chapter 3 Big Data Analytics Process 3.1 The Process of Data Mining and Knowledge Discovery 3.1.1 CRISP-DM Framework 3.1.2 KDD Process 3.2 Process of Big Data Analytics 3.2.1 Acquisition 3.2.2 Understanding 3.2.3 Preprocess 3.2.4 Analysis 3.2.5 Reporting 3.2.6 Action 3.3 Data Preprocess 3.3.1 Data Cleaning 3.3.2 Data Integration 3.3.3 Data Reduction 3.3.4 Data Transformation 3.4 Big Data Analysis 3.4.1 Analysis 3.4.2 Types of Big Data Analysis 3.4.3 Descriptive Analysis 3.4.4 Explorative Analysis 3.4.5 Predictive Data Analysis 3.5 Summary 3.6 References 3.7 Questions and Exercises Part Two Technologies and Tools Chapter 4 Supporting Infrastructure 4.1 Cloud Computing 4.1.1 Essential Characteristics of Cloud Computing 4.1.2 Services Provided by Cloud Computing 4.2 Distributed Computing 4.2.1 Characteristics of Distributed Systems 4.2.2 Distributed Systems Composition 4.2.3 Distributed State …… Chapter 5 Hadoop and MapReduce Chapter 6 Apache Spark Chapter 7 NoSQL and MongoDB Part Three Methods and Algorithms Chapter 8 Data Preparation Chapter 9 Descriptive Data Analysis Chapter 10 Explorative Data Analysis Chapter 11 Predictive Data Analysis Part Four Social, Ethical and Organisational Issues Chapter 12 Ethics, Governance and Security of Big Data Chapter 13 Building Data-Driven Business Organisations