The Decision Maker's Handbook to Data Science: AI and Data Science for Non-Technical Executives, Managers, and Founders

Contents
About the Author
Chapter 1: Demystifying Data Science and All the Other Buzzwords
	What Is Data Science?
		Data Science Is Multidisciplinary
		Core Fields of Data Science
			Artificial Intelligence: A Little History
				The AI Dream
				Automated Planning
				The AI Winters
				What We Learned from AI Research
			The Next Step: Enter Machine Learning
				The Problem with Machine Learning
				Deep Learning
			Statistics
				What Makes Statistics Unique?
				The Battle: Statistics vs. Machine Learning
		Subfields of Data Science
	The New Frontier in AI: Large Language Models and Transformer Networks
		Data Science and AI: The Skills Needed
			What Does a Data Scientist Need to Know?
Chapter 2: Data Management
	Understanding Where Data Comes From
	Data Collection Methods
		Data Acquisition Considerations
			Appropriateness of the Data
			Nature of the Data
			Time Requirement
			Cost of Acquisition
		Quantitative vs. Qualitative Research
			Quantitative Research
			Qualitative Research
Chapter 3: Data Collection Problems
	Data Collection Examples
		B2C Apps
		Sales
		Retail
		Finance
		Sports
		Social Media
	Data Management Practices
		Good Practices for Data Collection and Organization
			Establish a Goal First
			Awareness of How Data Collection Affects the Rest of Your Business
			Establish a Data Standard
		Bad Practices for Data Collection and Organization
			No Clear Objective
			Ignoring the Connection Between Data Collection and the Rest of Your Business
			No Documentation or Data Standard
	Examples of Bad Practices or What You Shouldn’t Do!
		Lack of Clear Objective
		But I Have an Objective!
	Examples of Good Practice or What You Should Do!
	Don’t Buy the Hype!
	Setting Goals in Advance
	Examples of the Impact of Data Science and Collection on Your Business
		Data Science and Dating
		Data Science and Entertainment
Chapter 4: How to Keep Data Tidy
	Solutions
Chapter 5: Thinking like a Data Scientist (Without Being One)
	The Data Science Process
	Defining the Data Science Process
		Step 1: Defining the Problem
		Step 2: Choosing the Right Data
		Step 3: Solving the Problem
		Step 4: Creating Value Through Actionable Insights
	Solving a Problem Using the Data Science Process
		Step 1: Defining the Problem
		Step 2: Choosing the Right Data
			Part A: Think About the Data
			Part B: Collect the Data
		Step 3: Solving the Problem
		Step 4: Creating Value Through Actionable Insights
Chapter 6: A Short Introduction to Statistics
	Descriptive Statistics
	Inferential Statistics
	How to Use Statistics
		Examples of Inferential Statistics
			Hypothesis Testing
			Statistical Modeling
	Misleading with Statistics
		Lying with Charts
		Misleading Using Descriptive Statistics
		Biases in Sampling
			Selection Bias
			Area Bias
			Self-Selection Bias
			Leading Question Bias
			Social Desirability Bias
			Sampling Bias in the Real World
		Lying with Inferential Statistics
Chapter 7: A Short Introduction to Machine Learning
	The Main Advantage of Machine Learning
	Types of Machine Learning
		Supervised Learning
		Unsupervised Learning
		A Closer Look at Supervised Learning
		A Better Understanding of Unsupervised Learning
			Examples of Clustering
			Examples of Anomaly Detection
			Examples of Dimensionality Reduction
Chapter 8: An introduction to AI
	The Evolution of AI
		The Early Years: Foundations and the Turing Test
		The AI Winters: Periods of Disillusionment
		The Resurgence: Internet, Computational Power, and Big Data
	Foundational Concepts in AI
		Machine Learning: The Core of Modern AI
		Deep Learning: Neural Networks at Play
		Natural Language Processing (NLP): Understanding Human Language
	The Advent of Transformers in AI
	The Rise of Generative AI
		Stable Diffusion and DALL-E: Evolution of Image Generation
	Applications of AI
Chapter 9: Problem Solving
	Understanding Whether a Problem Can Be Solved
	Quick Heuristics
		Statistical Modeling Problem
		Hypothesis Testing
		Supervised Learning
		Unsupervised Learning
	A Few More Heuristics
	When Heuristics Fail
		A Vague Project Plan
		Developing Skynet to Kill a Fly
		Lack of the Right Data
	Other Considerations
	What Problem Do You Really Need to Solve?
Chapter 10: Pitfalls
	What Not to Do
		Example: Bad Collaboration
		The Real Problem
	What’s the Solution?
Chapter 11: Hiring and Managing Data Scientists
	Into the Mind of a Data Scientist
		Code Hacking Skills
		Mathematical and Statistics Knowledge
		Domain Knowledge
		Two Is Not Enough
	What Motivates a Data Scientist?
	What Will Disengage a Data Scientist?
	When a Data Scientist Is Looking for a Job
		What Does a Data Scientist Want?
			The Team
			The Problem
			The Technology Stack
			Relationship to Academia
		Avoiding Traditional Limitations
		Data Science Is a General Toolbox
		Discovering Young Talent
		A Few Typical Data Scientist Dilemmas
		Freeze Your Data Scientist Recruitment Drive Now
	Data Science Tribes
		The Major Tribes
			Computer Scientists
			Statisticians
			Other Quantitative Specialists
			Convergence Point
		The Smaller Tribes
Chapter 12: Building a Data Science Culture
	An Overview of the Data Science Culture
		Understanding What a Data Science Culture Is About
		The Three Levels of a Data Science Culture
			The Management Level
			The Employee Level
			The Organizational Level
			Being Data Informed and Data Driven
		Creating a Friendly Environment for Data Scientists
		Being Data-Centric on an Organizational Level
			Example 1: When Data Is Not at the Core
			Example 2: Things Don’t Get Implemented
			Example 3: Poor Communication
	Steps to Build a Data Science Culture
		Why Do You Need Data Science in the First Place?
		Resistance to Change
			Cultural Resistance to Change
			Personal Resistance to Change
			Intellectual Resistance to Change
		The Journey to Change
		How to Start
		Where to Start
			Understanding and Using Dark Data
				What Can Dark Data Do for You?
		Other Steps to Take to Become More Data Driven
		Rewarding Good Behavior
		Create an Embedded Culture
Chapter 13: AI Ethics
	Bias in AI
		Fairness in AI
		Transparency in AI
	Real-World Scenarios and Case Studies
	AI Ethics Regulations
Chapter 14: Navigating the Future of Artificial Intelligence
	AI and Automation
	AI’s Personalized and Creative Revolution in Education
	AI and creativity
	AI Transforming Healthcare: Diagnosis, Treatment, and Personalized Care
		Enhancing Diagnosis and Treatment Accuracy
		Revolutionizing Surgical Procedures
		Personalized Care and Patient Monitoring
	Advancing Manufacturing Through AI: A Comprehensive Insight
	The Promise and Potential of AGI
	Philosophical Questions and Societal Implications
	Addressing the Risks and Ensuring Ethical AI Development
	References
Chapter 15: Epilogue: Data Science and AI Rule the World
Appendix: Tools for Data Science
	The Data Science Project Assessment Questionnaire
	Interview Questions for Data Scientists
	The New Solution Adoption Questionnaire
Index