Veridical Data Science - The Practice of Responsible Data Analysis and Decision Making

Using real-world data case studies, this innovative and accessible textbook introduces an actionable framework for conducting trustworthy data science. Most textbooks present data science as a linear analytic process involving a set of statistical and computational techniques without accounting for the challenges intrinsic to real-world applications. Bin Yu and Rebecca Barter employ the innovative Predictability, Computability, and Stability (PCS) framework to assess the trustworthiness and relevance of data-driven results relative to three sources of uncertainty that arise throughout the data science life cycle: the human decisions and judgment calls made during data collection, cleaning, and modeling. By providing real-world data case studies, intuitive explanations of common statistical and machine learning techniques, and supplementary R and Python code, ...

List of contents

Contents vii
Acknowledgments xv
Preface xvii
I PART 1: AN INTRODUCTION TO VERIDICAL DATA SCIENCE 1
1 An introduction to veridical data science 3
2 The Data Science Life Cycle 23
3 Setting up your data science project 43
II PART 2: PREPARING, EXPLORING, AND DESCRIBING DATA 65
4 Data Preparation 67
5 Exploratory Data Analysis 109
6 Principal component analysis 149
7 Clustering 197
III PART 3: PREDICTION 253
8 An introduction to prediction problems 255
9 Predicting continuous responses with Least Squares 275
10 Extending the Least Squares algorithm 311
11 Predicting binary responses and logistic regression 353
12 Decision trees and random forest 403
13 Producing the final prediction results 437
14 Conclusion 473
Answers to True or False exercises 481

Product details

• Authors: Rebecca Barter, Rebecca L Barter, Rebecca L. Barter, Rebecca L. Barter, Bin Yu
• Publisher: The MIT Press
• Languages: English
• Product format: Hardback
• Released: 15.10.2024
• EAN: 9780262049191
• ISBN: 978-0-262-04919-1
• No. of pages: 526
• Dimensions: 159 mm x 236 mm x 36 mm
• Series: Adaptive Computation and Machine Learning series
• Subjects: Natural sciences, medicine, IT, technology > IT, data processing > General, dictionaries; COMPUTERS / Computer Science, Information technology: general issues, COMPUTERS / Data Science / Machine Learning, COMPUTERS / Data Science / Data Analytics, Data capture and analysis