The problem companies face today is not a lack of data; on the contrary, it is the massive loads of data that data scientists find difficult to manage. Big data has revolutionized the data science industry as we knew it, including the topics that data scientists engage with. While statistics have not been popular with data scientists in the past, they play a very important underlying role in better data analysis, prediction, and inference. It helps to analyze the data and present the findings in a simple way, thereby identifying patterns and hidden aspects of the data, which plays a crucial role in data-driven decisions.
But data scientists often tend to lack a deep understanding of statistics that could enhance their insight generation. furthermore, given the broad nature of statistics, not everything is relevant to data science. With this barrier in mind, the Indian analytics magazine has identified the top statistics books targeting data science.
You are reading: Best statistics books for data science
signal and noise: why most predictions fail but some don’t
by nate silver
Labeled “one of the most seminal books of the decade” by the new york times book review, signal and noise is a comprehensive guide to making better predictions using statistical models. The book has been seen as preparing data scientists to communicate their findings clearly and precisely. Nate Silver is a popular blogger known for his baseball performance prediction system and his 2008 election prediction, among other works. This book builds on their learnings and guides data scientists to distinguish “true signals” from noisy data, forecast errors to avoid, the forecast paradox, and more through excerpts from some of the most successful forecasters in different fields and their real life experiences.
Find the book here.
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thinking statistics
by allen b. downy
think stats introduces probability and statistics to python programmers and mainly covers concepts directly related to data science. With Python code examples, Think Stats is aimed at experienced programmers, teaching them statistical concepts through hands-on data analysis examples and encouraging them to work on real data sets. it is based on Bayesian methods and covers topics such as statistical thinking, correlation, hypothesis testing regression, time series analysis, survival analysis, distributions, and analytical methods. downey’s other book, think bayes, explores solving statistical problems with python code.
Find the book here.
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naked statistics: taking the dread out of data
by charles wheelan
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It has been commented that an advanced stats book, bare stats, makes “stats come to life”. The book begins with basic concepts such as the normal distribution and continues with complex topics. Packed with examples and case studies, the book strays a bit from the technical details and focuses on the underlying concepts of statistical analysis. covers topics such as inference, correlation, regression, and practical examples.
Find the book here.
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statistics in plain language
by timothy c. urdan
Statistics in Plain Language covers general statistical techniques and concepts in an easy-to-understand manner. Different chapters of the book explain and illustrate, with an example, a statistical technique, including central tendency, and describe distributions, t-tests, regression, repeated measures, ANOVA, and factor analysis. Although the book is not aimed at data scientists, it is an ideal book for data science beginners and covers the topics of regression, distribution, factor analysis, and probability.
Find the book here.
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statistical inference of the age of the computer
by bradley efron and trevor hastie
Statistical Inference for the Computer Age explores data analysis and the data science revolution through classical Bayesian, Frequentist, and Fisherian inferential theories. talks about the theories behind machine learning algorithms with detailed explanations and use case examples on topics like spam data. Topics covered in the book include machine learning, deep learning, hypothesis testing, random forests, survival analysis, logistic regression, empirical Bayes, jackknife and bootstrap, Markov chain Monte Carlo, and inference after model selection. in the end, the book speculates on the future direction of data science and statistics.
find the book here.
Practical statistics for data scientists
by peter bruce and andrew bruce
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Practical Statistics for Data Scientists is a guide to applying statistical methods to data science through practical code examples and explanations of statistical terms. Aimed at data scientists familiar with the R programming language, this book is a quick reference for understanding how to incorporate statistical methods and avoid misusing them. The book covers data structures, data sets, random sampling, regression, descriptive statistics, probability, statistical experiments, and machine learning. the code is available in both python and r.
Find the book here.
pattern classification
by richard or doubt
A popular book explaining mathematical formulas and algorithms, Pattern Classification, was first published in 1973 and updated a few years ago. The book studies neural networks, machine learning, and statistical learning with classic and new methods. includes examples, case studies, and algorithms to explain specific techniques and historical commentary. Topics covered include Bayesian decision theory, stochastic methods, unsupervised learning and clustering, linear discriminant functions, nonparametric techniques, algorithm-independent machine learning, multilayer neural networks, and nonmetric methods.
Find the book here.
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advanced engineering mathematics
by erwin kreyszig
Originally published in 1962 and updated in 2015, Advanced Engineering Mathematics is a popular theoretical choice for engineers, computer scientists, and data scientists to learn about statistics and practical applications. the book includes differential equations, fourier analysis, vector analysis, complex analysis, and algebra. the latest version of the book explores the use of technology for conceptual problems and projects from the point of view of statistics and advanced mathematics.
Find the book here.
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