Data Science for All, Global Edition, 1st edition

1: What Is Data Science?

• 1.1: Introduction to Data Science
• Case Study: Netflix Uses Data Science for a Better Customer Experience Section
• Case Study: NASA Uses Cloud Services to Stream Real-Time Mars Footage Section
• 1.2: Data in Tables
• 1.3: Data Preparation
• 1.4: Data Analysis and Storytelling
• 1.5: Data Science in Society and Industry
• Case Study: Amazon Uses Data for Customers, Ads, and Fraud Prevention
• Putting It Together
• Ethics in Practice: Some Risks in Data Science
• Chapter Review Questions

2: Data Wrangling: Preprocessing

• 2.1: What Is Data Wrangling?
• 2.2: Cleaning Missing Data
• Case Study: Data Wrangling in Criminal Justice Research
• 2.3: Cleaning Anomalous Values
• Case Study: Conservative Party Defeats Labour Party in the UK and the Role of Data Wrangling
• 2.4: Transforming Quantitative Variables
• Case Study: GlobalGiving Teaches Nonprots About Transforming Variables
• 2.5: Transforming Categorical Variables
• 2.6: Reshaping a Dataset
• 2.7: Combining Datasets
• Putting It Together
• Ethics in Practice: Othering
• Chapter Review Questions

3: Making Sense of Data Through Visualization

• Case Study: Visualization of Natural Hazards
• 3.1: The Grammar of Graphics
• 3.2: Visualizations with One Quantitative Variable
• 3.3: Visualizations with One Categorical Variable
• 3.4: Visualizations with Two Variables
• 3.5: Visualizations with Three or More Variables
• 3.6: The Dangers of Visual Misrepresentation
• 3.7: Data Visualization Guidelines
• Case Study: European Space Agency Offers Interactive Star Mapper
• Case Study: Real-Time Visualization and Disease Outbreaks
• Putting It Together
• Ethics in Practice: The Perils of Using Color
• Chapter Review Questions

4: Exploratory Data Analysis

• Case Study: Shopify Helps Small Businesses with Descriptive Analytics Section
• 4.1: Central Tendency
• 4.2: Variability
• Case Study: On- and Off-Field Exploratory Data Analysis in Sports Section
• 4.3: Shape
• 4.4: Resistant Central Tendency and Variability
• 4.5: Data Associations
• Case Study: Exploratory Data Analysis of Electronic Medical Records Section
• 4.6: Identifying Outliers
• Putting It Together
• Ethics in Practice: Simpson’s Paradox
• Chapter Review Questions

5: Data Management

• 5.1: Asking Questions of Data
• 5.2: Selecting Variables
• Case Study: Starbucks Queries Its Customer Data
• 5.3: Filtering and Ordering Observations
• Case Study: Zara Filters to Move Its Product Faster
• 5.4: Summarizing and Structuring Data
• 5.5: Merging Tables
• Case Study: Merging Data to Combat the Spread of Disease
• Putting It Together
• Ethics in Practice: Data Privacy Regulation
• Chapter Review Questions

6: Understanding Uncertainty, Probability, and Variability

• 6.1: Variability and Uncertainty
• 6.2: Probability
• Case Study: The Economist’s French Presidential Election Model
• 6.3: Sampling Methods
• Case Study: Data Analytics in Cricket and Table Tennis
• 6.4: Simulation
• 6.5: Working with Probabilities and Common Fallacies
• Case Study: The Base Rate Fallacy of COVID-19 Misinformation in Iceland
• Putting It Together
• Ethics in Practice: Power in Sampling
• Chapter Review Questions

7: Drawing Conclusions from Data

• 7.1: Introduction to Statistical Inference
• 7.2: Data Collection and Study Design
• Case Study: Firearm Regulations and Causation Versus Correlation Section
• 7.3: The Language of Statistical Inference
• 7.4: Exploratory Data Analysis to Begin Inference
• 7.5: Drawing Conclusions in an Observational Study
• 7.6: A/B Testing as a Case of Experiments
• Case Study: A/B Testing Rating Systems at Netflix
• Putting It Together
• Ethics in Practice: P-Hacking and the Reproducibility Crisis
• Chapter Review Questions

8: Machine Learning

• 8.1: Artificial Intelligence
• 8.2: Three Steps in the Machine Learning Process
• Case Study: How Tesla Uses Machine Learning
• 8.3: Characteristics of Machine Learning Methods
• 8.4: Machine Learning Method Evaluation Section
• 8.5: Deep Learning
• Case Study: ChatGPT
• Case Study: Improving Safety in the Construction Industry Through Deep Learning
• 8.6: Use High-Quality Data in Machine Learning
• Putting It Together
• Ethics in Practice: Social Justice in Data Science
• Chapter Review Questions

9: Supervised Learning

• 9.1: Linear Regression with a No Explanatory Variables
• 9.2: Linear Regression with a Categorical Explanatory Variable
• 9.3: Linear Regression with a Quantitative Explanatory Variable
• 9.4: Multiple Linear Regression
• Case Study: Anesthesia and Regression
• 9.5: Nonparametric Regression Models
• Case Study: Improving Student Success and Satisfaction in Higher Education
• 9.6: Classification Models
• Putting It Together
• Ethics in Practice: Extrapolation
• Chapter Review Questions

10: Unsupervised Learning

• 10.1: What Is Unsupervised Learning?
• Case Study: Anomaly Detection at Accenture
• 10.2: Getting to Know Cluster Analysis
• 10.3: Introduction to K-Means Clustering
• Case Study: Spotify Uses Unsupervised Machine Learning for Personalization
• 10.4: Introduction to Hierarchical Clustering
• 10.5: Assessing the Quality of Clusters
• Case Study: Clustering and Targeting Advertising
• Putting It Together
• Ethics in Practice: Subjectivity in Unsupervised Learning
• Chapter Review Questions

Appendices

• A: Guide to Data Science Software
• B: Answers