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Data Science For All, 1st edition

Published by Pearson (January 1, 2025) © 2026

  • Brennan Davis California Polytechnic State University
  • Hunter Glanz California Polytechnic State University

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For introductory data science courses.

Help students navigate a data-rich world

Data Science for All, 1st Edition is a comprehensive, reader-friendly journey into the subject 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, equipping students to apply them immediately to their everyday lives.

This fresh approach offers meticulously designed content with unparalleled quality and clarity that does not sacrifice depth. The authors demystify data science, covering its entire lifecycle from preparation and analysis to storytelling. Learning by doing is emphasized through the authors’ unique STAR framework and various tools that encourage a more engaging and practical experience. A flexible presentation enables instructors to incorporate specific topics or projects aligned to their unique courses.

Hallmark features of this title

Unique approach

  • The authors’ STAR framework guides learners through a structured process of thinking about and communicating with data:
    • Scope the Data’s Context details the data’s background, including the observational units, variables, types, and data source.
    • Track the Question Behind the Exploration identifies crucial questions and underlying tensions the data aim to resolve.
    • Articulate the Results focuses on clearly presenting the findings derived from the data.
    • Respond with Next Steps suggests further actions or investigations guided by the results.
  • Chapter-opening stories present young people encountering data science investigations in their school, work, or personal lives; subsequent lessons apply related concepts to real life.
  • Case studies integrated throughout explore concepts in business, technology, environmental sciences, health, sports, and more.

Teaching and learning aids

  • Quick Tips add guidance on challenging material, warnings about common mistakes, and more.
  • Big Picture Breaks explain “why does this all matter?” with additional context.
  • Try It Yourself (TiY) exercises help students practice what they have just learned. Where appropriate they are accompanied by Tool Time, which offers technology-specific solutions and tips.
  • Applying the Concepts (AtC) exercises are provided at chapter end and are paired with TiYs, offering students the opportunity to review material on their own.
  • Explaining the Concepts questions are available at chapter end to invite additional conversation.
  • Putting It Together at the end of each chapter breaks down and reinforces the material.
  • R, Python, Microsoft® Excel®, and StatCrunch support is provided (plus SQL as topics allow); screenshots from different tools are used throughout the text.
  • Ample instructor support includes solutions, presentation slides and test materials.

Features of MyLab Statistics for the 1st Edition

  • Lecture videos introduce each section, and Try it Yourself videos walk students through accompanying TiY exercises in each chapter.
  • Video support is offered for software functions in R, Python, Excel, and StatCrunch.
  • R and Python code for every Try It Yourself feature is provided where applicable.
  • Excel® and StatCrunch click paths are provided for every Try It Yourself feature, where available and applicable.
  • Video guides for downloading and installing R and Python are provided.
  • All datasets are available both in MyLab Statistics and the Pearson Math and Statistics Resource site. The authors also provide code, data, and other digital assets at GitHub.
  • Integrated Review provides additional support as needed with statistical and mathematical fundamentals, with auto-graded exercises, worksheets, and videos.

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

About our authors

Brennan Davis is the Richard and Julie Hood Professor and director of graduate analytics programs at the Orfalea College of Business at California Polytechnic State University (Cal Poly, San Luis Obispo). He received a BS in mathematics from the University of California, Los Angeles, an MBA from the Wharton School of Business at the University of Pennsylvania, and a PhD from the University of California, Irvine. Brennan currently teaches undergraduate and graduate analytics courses. In 2019, Brennan received the Emeritus Faculty Award for significant and meritorious achievement in contributing to student welfare.

Hunter Glanz is a Professor of Statistics and Data Science at California Polytechnic State University (Cal Poly, San Luis Obispo). He received a BS in mathematics and a BS in statistics from Cal Poly, followed by an MA and PhD in statistics from Boston University. He maintains a passion for data science, machine learning, and statistical computing and enjoys teaching courses in those areas. Hunter serves on numerous committees and organizations dedicated to delivering cutting-edge statistical and data science content to students and professionals alike, including being a founding board member of the California Alliance for Data Science Education. In 2019, Hunter received the Terrance Harris Excellence in Mentorship Award, and in 2020 he received the Outstanding Faculty Award in the Master’s in Business Analytics program at Cal Poly.

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