Whenever a person starts to learn a new thing, it is of utmost importance that he/she have access to the right resources, whether it be books, online papers or videos. If you do not have the right start then it may take you more time to understand the things. Unlimited resources are available out there, but choosing what is the best is of more importance than anything else.
It is often said that, Books are our best friend and it has proven itself true from time to time.
So here is a list of top 5 Statistics books which you can use to enhance your knowledge in the field of data science.
- Statistics in Plain English
Author: Timothy C. Urdan
It is a very good book for you to get started in the field of data science. This book presents statistical concepts and techniques in simple, everyday language to help readers gain a better understanding of how they work and how to interpret them correctly. Each self-contained chapter features a description of the statistic including how it is used and the information it provides, how to calculate the formula, the strengths and, weaknesses of each technique, the conditions needed for its use, and an example that uses and interprets the statistic. A glossary of terms and symbols is also included along with an Interactive CD with PowerPoint presentations and problems and solutions for each chapter.
2. Think Stats: Probability and Statistics for Programmers
Author: Allen B. Downey
If you know how to program, you have the skills to turn data into knowledge using the tools of probability and statistics. This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.
You’ll work with a case study throughout the book to help you learn the entire data analysis process—from collecting data and generating statistics to identifying patterns and testing hypotheses. Along the way, you’ll become familiar with distributions, the rules of probability, visualization, and many other tools and concepts.
Develop your understanding of probability and statistics by writing and testing code
Run experiments to test statistical behavior, such as generating samples from several distributions
Use simulations to understand concepts that are hard to grasp mathematically
Learn topics not usually covered in an introductory course, such as Bayesian estimation
Import data from almost any source using Python, rather than be limited to data that has been cleaned and formatted for statistics tools
Use statistical inference to answer questions about real-world data
3.Introduction to Statistical Learning
Authors: Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential tool set for making sense of the vast and complex data sets that have emerged in fields ranging from biology to finance to marketing to astrophysics in the past twenty years. This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, resampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, and more. Color graphics and real-world examples are used to illustrate the methods presented. Since the goal of this textbook is to facilitate the use of these statistical learning techniques by practitioners in science, industry, and other fields, each chapter contains a tutorial on implementing the analyses and methods presented in R, an extremely popular open source statistical software platform.
Two of the authors co-wrote The Elements of Statistical Learning (Hastie, Tibshirani and Friedman, 2nd edition 2009), a popular reference book for statistics and machine learning researchers. An Introduction to Statistical Learning covers many of the same topics, but at a level accessible to a much broader audience. This book is targeted at statisticians and non-statisticians alike who wish to use cutting-edge statistical learning techniques to analyze their data. The text assumes only a previous course in linear regression and no knowledge of matrix algebra.