# 5 Free Books to Learn Statistics

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Statistics is the backbone of modern technology that plays a vital role in various domains, ranging from business and finance to healthcare. It provides the necessary tools and techniques to collect, analyze, and interpret data, enabling us to make informed decisions. However, learning statistics can be difficult, especially for those without a strong mathematical background. Fortunately, there are several excellent free resources available online that can help you learn statistics in no time.

In this blog post, we will introduce you to five outstanding books that cover a wide range of statistical concepts and techniques, catering to different levels of expertise and educational backgrounds. These books offer various approaches to learning statistics, catering to diverse educational and professional backgrounds.

## 1. Think Stats 2e

Think Stats 2e is an excellent book for Python programmers who want to learn probability and statistics. The second edition includes updated examples, exercises, and a clearer narrative that appeals to those without a deep mathematical background.
The Think Stats book covers importing and cleaning data, then moving on to single variable explorations, pairwise explorations, multivariate analysis, finally estimation, hypothesis testing, and visualization. This structure of the book allows readers to build their understanding of data analysis step-by-step.

## 2. Introductory to Statistics

Introductory to Statistics is a textbook for the students majoring in fields other than mathematics or engineering. This book is perfect for students who want a straightforward and brief overview of statistics. It aligns with the scope and guidelines for a one-semester introductory course.

The  Introductory to Statistics covers fundamental concepts in statistics, including descriptive statistics, probability, random variables, hypothesis testing, linear regression and more. The content is well-structured, making it easy to follow, and it is extensively peer-reviewed, ensuring reliability and accuracy.

## 3. Probabilistic Programming & Bayesian Methods for Hackers

Bayesian Methods for Hackers interactive online book is perfect for those interested in Bayesian statistics. It uses Python libraries like PyMC to teach Bayesian methods through practical examples and real-world scenarios. This book is particularly suitable for those who prefer learning by doing and have a background in programming.

The book introduces Bayesian methods with the PyMC library and covers Markov Chain Monte Carlo, the law of large numbers, loss functions, and more.

## 4. Computational and Inferential Thinking

Computational and Inferential Thinking focuses on building the foundations of Data Science, providing readers with tools to analyze real datasets and understand the analysis results. The book uses the Python programming language and introduces concepts in a manner that is accessible to beginners.

The data analysis involves three core aspects: exploration, prediction, and inference. This book provides a consistent approach to all three, introducing statistical concepts and fundamental computer science ideas.

## 5. Introduction to Statistical Learning

Introduction to Statistical Learning is an excellent textbook designed for undergraduate and graduate students seeking a complete guide to statistical learning methods. The book is also an invaluable resource for practicing data scientists, offering a balance of theoretical concepts and practical applications.

The book comes with R labs that provide detailed, step-by-step explanations on how to implement various statistical learning methods in real-life settings. “R” is a statistical programming language similar to Python.

The book covers statistical learning, regression, classification, resampling methods, model selection and regularization, tree-based methods, support vector machines, unsupervised machine learning, and more.

## Final Thoughts

Whether you’re an undergraduate student who wants to understand the fundamentals or a professional who wants to enhance skills in statistical modeling or Bayesian methods, these books provide valuable knowledge at no cost.

It’s important to remember that most AI models are statistical models and there is no magic behind them. It’s all based on math, probability, and statistics. So, learning statistics is the first step toward becoming an AI engineer or data scientist.

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