Data science necessitates a thorough grasp of statistics. Here are five free books that will teach you all you need to know about statistics as a data analyst.
To learn data science, you need a solid mathematical foundation. Statistics is one of the necessary math skills for data science.
However, learning statistics can be intimidating, especially if you do not come from a math or computer science background. To help you get started, we’ve compiled a list of free books explaining statistics for data science.
The bulk of these books take a hands-on approach to statistical concepts, which is essential to properly use statistics as a data scientist. So, let’s look at these statistics books.
1. Introduction to Statistics
The Introductory Statistics book provides a simple introduction to statistics that covers what a semester-long introductory statistics course in college usually does
This book, which is available for free on OpenStax and was developed by a team of contributing professional authors, takes an application-first strategy for statistics rather than a theory-first approach, and it contains problems for each subject matter.
The content of this book will teach you the following.
- Samples and data
- Descriptive Statistics
- Topics in probability and random variables.
- Normal distribution.
- The Central Limit Theorem
- Confidence intervals
- Hypothesis testing.
- The Chi-squared distribution
- Linear regression and correlation.
- F distribution, one-way ANOVA
Link to Introductory Statistics 2e
2. Introduction to Modern Statistics.
Mine Çetinkaya-Rundel and Johanna Hardin wrote Introduction to Modern Statistics, a free online textbook available through the OpenIntro initiative
If you wish to learn the statistical foundations for efficient data analysis, this book is for you. The book’s contents are as follows:
- Introduction to Data
- Exploratory data analysis.
- Regression modeling
- Foundations of Inference
- Statistical Inference
- Inferential models
Link: Introduction to Modern Statistics.
3. Probabilistic Programming and Bayesian Methods for Hackers
Probabilistic Programming and Bayesian Methods for Hackers, sometimes known as Bayesian Methods for Hackers, is a famous book on Bayesian statistics.
“Bayesian Methods for Hackers” is an introduction to Bayesian methods and probabilistic programming that prioritizes computational and comprehension above mathematics. All in pure Python ;).
– Source.
You’ll learn probability theory and Bayesian inference while using the PyMC software. The book’s contents are as follows:
- Introduction to Bayesian Methods
- The Python MC library
- Monte Carlo using a Markov chain
- The Law of Large Numbers: Loss Functions
- Priors
Link: Probabilistic Programming and Bayesian Methods for Hackers
4. Inferential and Computational Thinking
You can understand the statistical underpinnings of data science by reading Computational and Inferential Thinking: The Foundations of Data Science by Ani Adhikari, John DeNero, and David Wagner
This book was created as an addition to UC Berkeley’s Data 8: Foundations of Data Science course. This book covers the following subjects:
- Overview of data science
- Python Programming: Tables, Sequences, and Data Types
- Tables and Visualisation Functions
- Unpredictability
- Empirical distribution and sampling
- Testing of hypotheses
- Calculation
- Descriptive Regression Analysis
Link Computational and Deductive Reasoning: The Basis of Information Science
5. think statistics
You can use Python to learn and practice statistical ideas with the help of Allen B. Downey’s book Think Stats.
so that you may use your knowledge of Python to acquire ideas related to probability and statistics for efficient data handling. As you progress through the book, you will have the opportunity to develop brief Python programs and gain experience with real datasets to strengthen your grasp of statistical principles.
The following subjects were covered:
- Analysing exploratory data
- Distribution
- Mass functions for probability
- Distribution functions that accumulate over time
- Distributional modelling
- Density functions for probabilities
- associations between different factors
- Calculation
- Testing of hypotheses
- Squares of linear least squares
- Regression
- Analysis of survival
- analytical techniques
