Computational and Inferential Thinking

The Foundations of Data Science

Ani Adhikari

John DeNero

Computational and Inferential Thinking is an introductory text for data science that explores foundational concepts in data processing and statistics using modern programming tools. Ideas are illustrated by real-world data sets and examples. While rigorous in presentation, this text does not expect prior experience in computing, calculus, or linear algebra.

1 · Data Science and Computing

• 1.1 Introduction
• 1.2 Why Data Science?
• 1.3 Causality and Experiments
• 1.4 Expressions
• 1.5 Tables

2 · The Power and Peril of Pictures

• 2.1 Visualizations
• 2.2 Bar Charts and Histograms
• 2.3 Functions
• 2.4 Sampling
• 2.5 Explorations: Privacy
• 2.6 Appendix: Statements

3 · Is There a Difference?

• 3.1 Iteration
• 3.2 Distributions
• 3.3 How close are two distributions?
• 3.4 Permutation Tests
• 3.5 Confidence Intervals
• 3.6 The Bootstrap

4 · Making Predictions

• 4.1 Correlation
• 4.2 Regression
• 4.3 Bootstrap for Regression
• 4.4 Classification
• 4.5 Multiple Regression
• 4.6 Chance Models