فهرست مطالب

• Cover
• Title Page
• Copyright Page
• About the Authors
• About the Technical Editors
• Acknowledgments
• Contents
• Introduction
• Part I: Thinking Like a Data Head
• Chapter 1: What Is the Problem?
• Questions a Data Head Should Ask
• Understanding Why Data Projects Fail
• Working on Problems That Matter
• Chapter Summary
• Chapter 2: What Is Data?
• Data vs. Information
• Data Types
• How Data Is Collected and Structured
• Basic Summary Statistics
• Chapter Summary
• Chapter 3: Prepare to Think Statistically
• Ask Questions
• There Is Variation in All Things
• Probabilities and Statistics
• Chapter Summary
• Part II: Speaking Like a Data Head
• Chapter 4: Argue with the Data
• What Would You Do?
• Tell Me the Data Origin Story
• Is the Data Representative?
• What Data Am I Not Seeing?
• Argue with Data of All Sizes
• Chapter Summary
• Chapter 5: Explore the Data
• Exploratory Data Analysis and You
• Embracing the Exploratory Mindset
• Can the Data Answer the Question?
• Did You Discover Any Relationships?
• Did You Find New Opportunities in the Data?
• Chapter Summary
• Chapter 6: Examine the Probabilities
• Take a Guess
• The Rules of the Game
• Probability Thought Exercise
• Be Careful Assuming Independence
• All Probabilities Are Conditional
• Ensure the Probabilities Have Meaning
• Chapter Summary
• Chapter 7: Challenge the Statistics
• Quick Lessons on Inference
• The Process of Statistical Inference
• The Questions You Should Ask to Challenge the Statistics
• Chapter Summary
• Part III: Understanding the Data Scientist’s Toolbox
• Chapter 8: Search for Hidden Groups
• Unsupervised Learning
• Dimensionality Reduction
• Principal Component Analysis
• Clustering
• k-Means Clustering
• Chapter Summary
• Chapter 9: Understand the Regression Model
• Supervised Learning
• Linear Regression: What It Does
• Linear Regression: What It Gives You
• Linear Regression: What Confusion It Causes
• Other Regression Models
• Chapter Summary
• Chapter 10: Understand the Classification Model
• Introduction to Classification
• Logistic Regression
• Decision Trees
• Ensemble Methods
• Watch Out for Pitfalls
• Misunderstanding Accuracy
• Chapter Summary
• Chapter 11: Understand Text Analytics
• Expectations of Text Analytics
• How Text Becomes Numbers
• Topic Modeling
• Text Classification
• Practical Considerations When Working with Text
• Chapter Summary
• Chapter 12: Conceptualize Deep Learning
• Neural Networks
• Applications of Deep Learning
• Deep Learning in Practice
• Artificial Intelligence and You
• Chapter Summary
• Part IV: Ensuring Success
• Chapter 13: Watch Out for Pitfalls
• Biases and Weird Phenomena in Data
• The Big List of Pitfalls
• Chapter Summary
• Chapter 14: Know the People and Personalities
• Seven Scenes of Communication Breakdowns
• Data Personalities
• Chapter Summary
• Chapter 15: What’s Next?
• Index
• EULA