李刚民，Dr.Gangmin Li is a Senior Researcher in the Research Institute of Big Data Analytics (RIBDA) and an Associated Professor in the Department of Computer Science and Software Engineer at Xi'an Jiaotong-Liverpool University.
He has over 35 years of research and teaching experience in 4 British Universities and 2 Chinese Universities. His research interests include Knowledge Engineering (KE), Distributed Artificial Intelligence (DAI)， Distributed Systems, Grid and Clouds Computing and Big Data Analytics.He has over 100 publications and wide industrial connections and research collaborations.

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

李刚民著的《大数据分析基础(概念技术方法和商务)(英文版)》涵盖了大数据分析的四个基本方面：概念和基础，平台和工具，方法和算法，以及社会问题和最佳实践。