MyLab Statistics with Pearson eText Instant Access for Data Science for All, Global Edition

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.

Table of Contents:
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

About the Author :
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.