Data Science For All, 1st edition

Published by Pearson (December 10, 2024) © 2026

  • Brennan Davis California Polytechnic State University
  • Hunter Glanz California Polytechnic State University
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We are all consumers of data, and you may become directly engaged with data work in your future career. Data Science for All, 1st Edition takes you on a thorough yet reader-friendly journey into the subject to help you navigate a data-rich world. The authors demystify data science, covering its entire lifecycle from preparation and analysis to storytelling.

Designed for students of all majors and backgrounds, it distills the most applicable ideas from the component fields of statistics, computer science, and domain application, helping you apply them immediately to your everyday life. Learning by doing is emphasized through the authors’ unique STAR framework and various tools that encourage a more engaging and practical experience.

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

Appendices

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

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