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.
Sprache
Verlagsort
Zielgruppe
Für höhere Schule und Studium
Dateigröße
ISBN-13
978-1-292-48518-8 (9781292485188)
Copyright in bibliographic data and cover images is held by Nielsen Book Services Limited or by the publishers or by their respective licensors: all rights reserved.
Schweitzer Klassifikation
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.
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
