# My Data Science Reading List

Everyone has hobbies. My hobby is in a big red bookcase. Currently my big red bookcase contains around ~200 dead-tree books on statistics, data science, research, and mathematics. I rarely travel anywhere without at least one book from this bookcase and on a lazy Sunday afternoon you will likely see to working my way through one of it's members.

## Mathematics

### Basics

- Basic Math for Social Scientists: Concepts (Quantitative Applications in the Social Sciences)
- Mathematics for the Nonmathematician (Dover Books on Mathematics)
- A Mathematics Course for Political and Social Research
- Introduction to Mathematical Thinking
- Basic Math for Social Scientists: Problems and Solutions (Quantitative Applications in the Social Sciences)
- The Language of Mathematics: Making the Invisible Visible
- The Joy of x: A Guided Tour of Math, from One to Infinity
- Here's Looking at Euclid: A Surprising Excursion Through the Astonishing World of Math
- Good Math: A Geek's Guide to the Beauty of Numbers, Logic, and Computation (Pragmatic Programmers)
- Mathematics 1001: Absolutely Everything That Matters in Mathematics.
- Essential Mathematics for Political and Social Research (Analytical Methods for Social Research)
- Doing Math with Python: Use Programming to Explore Algebra, Statistics, Calculus, and More!
- Mathematics and Python Programming

### Algebra

- Linear Algebra (Dover Books on Mathematics)
- Basic Algebra I: Second Edition (Dover Books on Mathematics)
- Basic Algebra II: Second Edition (Dover Books on Mathematics)
- Introduction to Linear Algebra and Differential Equations (Dover Books on Mathematics)
- Coding the Matrix: Linear Algebra through Applications to Computer Science

### Calculus

- Quick Calculus: A Self-Teaching Guide, 2nd Edition
- Calculus: An Intuitive and Physical Approach (Second Edition) (Dover Books on Mathematics)
- Essential Calculus with Applications (Dover Books on Mathematics)
- Introduction to Partial Differential Equations with Applications (Dover Books on Mathematics)
- Partial Differential Equations for Scientists and Engineers (Dover Books on Mathematics)
- Ordinary Differential Equations (Dover Books on Mathematics)

### Probability

- Introduction to Probability (Chapman & Hall/CRC Texts in Statistical Science)
- Probability Theory: A Concise Course (Dover Books on Mathematics)

### Other

- Geometry: A Comprehensive Course (Dover Books on Mathematics)
- Introduction to Stochastic Processes (Dover Books on Mathematics)

## Traditional Statistics

### Basic Statistics

- Statistics
- Discovering Statistics Using R
- Statistics: The Art and Science of Learning from Data (3rd Edition)
- Principles of Statistics (Dover Books on Mathematics)
- All of Statistics: A Concise Course in Statistical Inference (Springer Texts in Statistics)
- Mathematical Methods in Statistics a Workbook
- Fundamental Statistics for the Behavioral Sciences
- Statistics Made Simple
- Using Basic Statistics in the Behavioral Sciences
- Statistical Rules of Thumb
- Analysis of Longitudinal Data (Oxford Statistical Science)
- Practical Longitudinal Data Analysis (Chapman & Hall/CRC Texts in Statistical Science)
- The SAGE Handbook of Regression Analysis and Causal Inference
- Statistical Modeling and Inference for Social Science (Analytical Methods for Social Research)
- Statistical Models and Causal Inference: A Dialogue with the Social Sciences
- Statistics Done Wrong: The Woefully Complete Guide
- Even You Can Learn Statistics: A Guide for Everyone Who Has Ever Been Afraid of Statistics
- Practical Statistics Simply Explained (Dover Books on Mathematics)

### Exploratory Data Analysis

- Exploratory Data Analysis (Quantitative Applications in the Social Sciences)
- Fundamentals of Exploratory Analysis of Variance
- Practical Longitudinal Data Analysis (Chapman & Hall/CRC Texts in Statistical Science)
- Exploratory Data Analysis
- Fundamentals of Exploratory Analysis of Variance
- Understanding Robust and Exploratory Data Analysis

### Regression

- Multiple Regression in Practice (Quantitative Applications in the Social Sciences)
- Understanding Regression Assumptions (Quantitative Applications in the Social Sciences)
- Understanding Regression Analysis: An Introductory Guide (Quantitative Applications in the Social Sciences)
- Applied Regression: An Introduction (Quantitative Applications in the Social Sciences)
- Multiple Regression in Practice (Quantitative Applications in the Social Sciences)
- Tests of Significance (Quantitative Applications in the Social Sciences)
- Introduction to Multivariate Analysis (Chapman & Hall/CRC Texts in Statistical Science)
- Multivariate Statistical Analysis: A Conceptual Introduction, 2nd Edition
- What is a p-value anyway? 34 Stories to Help You Actually Understand Statistics
- Understanding Regression Assumptions (Quantitative Applications in the Social Sciences)
- Regression Analysis by Example
- Applied Logistic Regression (Wiley Series in Probability and Statistics)
- Statistics for the Social Sciences
- Introduction to Linear Regression Analysis
- Basic statistics: Tales of distributions
- Analysis of Covariance (Quantitative Applications in the Social Sciences)
- Interpreting and Using Regression (Quantitative Applications in the Social Sciences, No. 29)
- Data Analysis and Regression: A Second Course in Statistics
- Understanding Significance Testing (Quantitative Applications in the Social Sciences)
- Loglinear Models with Latent Variables (Quantitative Applications in the Social Sciences)
- Statistical Models: Theory and Practice
- Permutation, Parametric, and Bootstrap Tests of Hypotheses (Springer Series in Statistics)

### Advanced Regression

- Interpreting Probability Models: Logit, Probit, and Other Generalized Linear Models (Quantitative Applications in the Social Sciences)
- Regression Models for Categorical and Limited Dependent Variables (Advanced Quantitative Techniques in the Social Sciences)
- Analysis of Ordinal Data (Quantitative Applications in the Social Sciences)
- Analysis of Nominal Data (Quantitative Applications in the Social Sciences)
- Ordinal Log-Linear Models (Quantitative Applications in the Social Sciences)
- Analysis of Ordinal Categorical Data (Wiley Series in Probability and Statistics)
- An Introduction to Categorical Data Analysis (Wiley Series in Probability and Statistics)
- Regression with Dummy Variables (Quantitative Applications in the Social Sciences)
- An Introduction to Generalized Linear Models, Third Edition (Chapman & Hall/CRC Texts in Statistical Science)
- Modeling Count Data
- A Primer on Linear Models (Chapman & Hall/CRC Texts in Statistical Science)
- Data Analysis Using Regression and Multilevel/Hierarchical Models
- Statistical Methods for Categorical Data Analysis, 2nd Edition
- Data Analysis Using Regression and Multilevel/Hierarchical Models

### Non-Parametric

### Time Series

- The Analysis of Time Series: An Introduction, Sixth Edition (Chapman & Hall/CRC Texts in Statistical Science)
- Time Series Analysis
- Interrupted Time Series Analysis (Quantitative Applications in the Social Sciences)
- Time Series Analysis for the Social Sciences (Analytical Methods for Social Research)

### Event History

### Survival Analysis

### Other

- The Life and Times of the Central Limit Theorem (History of Mathematics)
- Introduction to Factor Analysis: What It Is and How To Do It (Quantitative Applications in the Social Sciences)
- Statistics on the Table: The History of Statistical Concepts and Methods
- Understanding The New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis (Multivariate Applications Series)
- Central Tendency and Variability (Quantitative Applications in the Social Sciences)
- Probability, Statistics and Truth (Dover Books on Mathematics)
- Applied Missing Data Analysis (Methodology in the Social Sciences)
- Thinking Statistically
- Heteroskedasticity in Regression: Detection and Correction (Quantitative Applications in the Social Sciences)
- Statistical Analysis with Missing Data
- The Cult of Statistical Significance: How the Standard Error Costs Us Jobs, Justice, and Lives (Economics, Cognition, and Society)
- Statistics As Principled Argument
- The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
- Probability, Statistics and Truth (Dover Books on Mathematics)
- Statistics, Data Mining, and Machine Learning in Astronomy: A Practical Python Guide for the Analysis of Survey Data
- Statistical Distributions
- Mathematical Methods of Statistics

## Bayesian Statistics

- Bayes' Rule: A Tutorial Introduction to Bayesian Analysis
- Data Analysis: A Bayesian Tutorial
- Introduction to Bayesian Statistics, 2nd Edition
- Bayesian Data Analysis, Second Edition (Chapman & Hall/CRC Texts in Statistical Science)
- Bayesian Statistics for the Social Sciences (Methodology in the Social Sciences)
- Bayesian Programming (Chapman & Hall/CRC Machine Learning & Pattern Recognition)
- Bayesian Reasoning and Machine Learning
- Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Methods (Addison-Wesley Data & Analytics Series)
- Bayesian Methods in Health Economics (Chapman & Hall/CRC Biostatistics Series)
- Bayesian and Frequentist Regression Methods (Springer Series in Statistics)

## Machine Learning

- Machine Learning in Action
- Learning From Data
- Machine Learning: An Algorithmic Perspective, Second Edition (Chapman & Hall/Crc Machine Learning & Pattern Recognition)
- Pattern Recognition and Machine Learning 1st Edition
- Python Machine Learning (Raschka)
- Python Machine Learning (Bowles)
- An Introduction to Machine Learning
- Boosting: Foundations and Algorithms (Adaptive Computation and Machine Learning series)
- Machine Learning in Python: Essential Techniques for Predictive Analysis
- Machine Learning: A Probabilistic Perspective (Adaptive Computation and Machine Learning series)
- Introduction to Machine Learning (Adaptive Computation and Machine Learning series)

## Algorithims

- Introduction to Algorithms, 3rd Edition
- Data Structures and Algorithms with Python (Undergraduate Topics in Computer Science)
- Python Algorithms: Mastering Basic Algorithms in the Python Language
- Annotated Algorithms in Python: with Applications in Physics, Biology, and Finance
- Data Structure and Algorithmic Thinking with Python: Data Structure and Algorithmic Puzzles

## Other

- Data Analysis for Politics and Policy
- Missing Data (Quantitative Applications in the Social Sciences)
- A Handbook of Small Data Sets (Chapman & Hall Statistics Texts)
- The Statistical Analysis of Experimental Data (Dover Books on Mathematics)
- The Chicago Guide to Writing about Multivariate Analysis (Chicago Guides to Writing, Editing, and Publishing)
- Visual Explanations: Images and Quantities, Evidence and Narrative
- Visual Storytelling with D3: An Introduction to Data Visualization in JavaScript (Addison-Wesley Data & Analytics Series)
- Cluster Analysis (Quantitative Applications in the Social Sciences)
- Best Practices in Data Cleaning: A Complete Guide to Everything You Need to Do Before and After Collecting Your Data
- The Visual Display of Quantitative Information
- Matched Sampling for Causal Effects
- Beautiful Data: The Stories Behind Elegant Data Solutions
- Sampling Techniques, 3rd Edition
- Counterfactuals and Causal Inference: Methods and Principles for Social Research (Analytical Methods for Social Research)
- Introduction to Analysis (Dover Books on Mathematics)
- An Introduction to Information Theory: Symbols, Signals and Noise (Dover Books on Mathematics)
- Monte Carlo Simulation and Resampling Methods for Social Science
- Neural Networks for Pattern Recognition (Advanced Texts in Econometrics)
- Causality: Models, Reasoning and Inference
- Natural Experiments in the Social Sciences: A Design-Based Approach (Strategies for Social Inquiry)
- Game Theory: Concepts and Applications (Quantitative Applications in the Social Sciences)
- Web Scraping with Python: A Comprehensive Guide to Data Collection Solutions
- Practical Text Mining and Statistical Analysis for Non-structured Text Data Applications
- Running Randomized Evaluations: A Practical Guide
- Python for Data Science For Dummies
- Counterfactuals and Causal Inference: Methods and Principles for Social Research (Analytical Methods for Social Research)
- A Practical Introduction to Index Numbers
- Sampling
- Data Mining for the Social Sciences: An Introduction
- Beautiful Data: A History of Vision and Reason since 1945 (Experimental Futures)
- Designing Social Inquiry: Scientific Inference in Qualitative Research
- Using Propensity Scores in Quasi-Experimental Designs
- Propensity Score Analysis: Statistical Methods and Applications (Advanced Quantitative Techniques in the Social Sciences)