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: “Dewey Defeats Truman” 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: The Washington Post Uses a Visualization to Report on U.S. Flooding
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: ESPN Updates Its Visualizations in Real Time
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: FiveThirtyEight
6.3: Sampling Methods
Case Study: Sabermetrics and Next-Gen Stats
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: Advertising from Target
Putting It Together
Ethics in Practice: Subjectivity in Unsupervised Learning
Chapter Review Questions

A: Guide to Data Science Software
B: Answers

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