Data Science for Decision Makers & Data Professionals

Foreword
Acknowledgments
Introduction

1. The importance and benefits of data science
1.1 Introduction
1.2 Fundamental developments
1.3 The Data-Decision gap
1.4 Twenty-five advantages of effective data science

2. Data science processes and the intelligent organization
2.1 Introduction
2.2 The four maturity levels of data science
2.3 The three stages of the intelligent organization
2.4 The contours of the intelligent organization
2.5 Four key concepts

3. The senses of the intelligent organization
3.1 Introduction
3.2 Developing sensors: four approaches
3.3 Key Performance Indicators: KPIs
3.4 Machine learning models & big data
3.5 Valuable insights
3.6 The context of sensors and indicators
3.7 Criteria of information and indicators
3.8 Setting norms and targets for indicators

4. The brain of the intelligent organization
4.1 Introduction
4.2 Processing: turning data into knowledge & action
4.3 AI, algorithms, and machine learning
4.4 The processors: stakeholders
4.5 Requirements of the processing cycle
4.6 Business Intelligence frameworks

5. The limbs of the intelligent organization
5.1 Introduction
5.2 Research the necessity of an agile organization
5.3 Expertise
5.4 The structure of the agile organization
5.5 Social infrastructure

6. The architecture of the intelligent organization
6.1 Introduction
6.2 Principles dictate the architecture
6.3 The data warehouse and ETL
6.4 The data lake
6.5 Portals, mobile data science, and reflex BI
6.6 Metadata: the glue of the intelligent organization

7. Data science instruments and applications
7.1 Introduction
7.2 Data science instruments
7.3 Data science applications

8. Data science projects
8.1 Introduction
8.2 How to approach data science projects
8.3 The data science project cycle: approach & steps
8.4 Top ten data science risks and obstacles
8.5 Data science roles and competencies