Best Books to Learn Statistics for Data Science, Do you want to learn statistics for data science? If so, read these books. If so, your quest is over here.
The eight finest books for learning statistics for data science are listed in this post. So, read the entire article to choose which book is ideal for you.
If you want to be a successful data scientist or analyst, you should have a strong understanding of statistics. Knowing statistics will help you select the most effective algorithm for a certain assignment.
The knowledge of statistics comprises maximum likelihood estimators, statistical tests, and distributions. Each is crucial to data science.
Knowing statistics is essential for you as your job as a data scientist or analyst requires you to extract meaningful insights from the data.
But when it comes to learning statistics, books are crucial since they provide readers a firm grasp of the ideas. Let’s locate the Best Books to Learn Statistics for Data Science for you without further ado.
Best Books to Learn Statistics for Data Science
If you are familiar with the R or Python programming languages and have some past statistical background, this book is an excellent fit for you.
This book teaches you how to use regression to estimate outcomes and find anomalies, how random sampling can reduce bias and produce a higher-quality dataset even with enormous data, etc.
However, just one book is insufficient to comprehend the more complex statistical ideas. To delve deeper, you’ll require other statistics sources. The availability of both R and Python source code is this book’s best feature.
One of the best and most fascinating books to learn statistics is this one. Unlike other books, this one is straightforward to read.
The book will keep you interested in comedy and real-world examples of how statistics apply to the modern world.
You will discover the fundamental statistical ideas in this book, including inference, correlation, regression analysis, and others.
The book’s author, Wheelan, uses numerous instances from ordinary life to illustrate how statistics can be both illuminating and irritating.
Regardless of your level of experience, this book is appropriate for you. Your statistical notions will be clarified by this book.
Another intriguing book written in the style of a story is this one. You will learn about probability distributions like Binomial, Poisson, and Normal, descriptive statistics like mean, mode, median, and standard deviation, as well as correlation, regression, null hypothesis testing, and other inferential statistics in this book.
The author’s ability to thoroughly describe each topic is this book’s best quality. And unlike other books, this one won’t make you bored.
With the aid of actual examples of how to apply statistics in the real world, you will comprehend statistics.
If you are a beginner in statistics and are seeking a book that will make learning statistics easier, this one is a wonderful choice.
Concepts in statistics will be explained practically in this book. This book is intended for those who lack programming and statistical expertise.
This book can be used to refresh your memory even if you are an expert in the field.
Some of the most significant modeling and prediction strategies are covered in this book along with pertinent applications.
Linear regression, classification, resampling techniques, shrinkage strategies, tree-based techniques, support vector machines, clustering, and other subjects are covered.
This book offers a tonne of code examples and exclusively discusses ideas directly connected to data science.
And those who already have at least a fundamental knowledge of Python will benefit from this book. If you don’t already know Python, I wouldn’t advise you to read this book.
You will learn about distributions, probability laws, visualization, and many other tools and ideas in this book.
In this book, difficult-to-understand mathematical concepts are explained through simulations, and issues regarding data from the actual world are addressed using statistical inference.
This is a basic statistics book; it does not address more complex statistical ideas. but useful for reiterating your fundamentals. This book’s language is simple to understand.
Despite being only 144 pages long, the book is jam-packed with valuable information and common sense.
This book teaches you how to check data before accepting statistical findings at face value by teaching you the right questions to ask and the right criteria to look at.
The concepts in this book are still completely applicable even though the examples are outdated because it was published in the 1950s.
As implied by the title, “Statistics in Plain English,” this book is written in a fairly straightforward manner and explains a broad range of statistical ideas in a way that is easy to comprehend.
The statistical methods covered in this book range from the most fundamental, such as central tendency and characterizing distributions, to the most complex, including factor analysis, regression, repeated measures ANOVA, and t-test.
If you are entering data science without a math-based degree, this book will be helpful to you.
If you have any prior Python expertise, this book is a good choice. This book contains examples that you may run and modify on your own computer in addition to discussing theoretical aspects of Bayesian approaches.
This book, in my opinion, is the best guide to Bayesian inference and the easiest to understand for someone who is not a statistician.
The Github repository and code samples are also included in this book. The PyMC Python package for bayesian statistics is the main topic of the book.
You learned about the 8 Best Books to Learn Statistics for Data Science in this article. Have any of these books been purchased or read by you? If so, please share your experience in the comments.