Here is the list of the Best Statistics Books for Data Science for Beginners and Advanced in 2022 to learn. Read this list of best Statistics books for Data Science for beginners and if you found any Best Statistics Books for Data Science for Beginners to advanced is missing please comment on the Best Statistics books name so that we can add them and update the list.
7 Best Statistics Books for Data Science for Beginners and Advanced:
Best Statistics Books for data Science for Beginners to advanced are listed for you, enjoy this list by reading.
Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python
Statistical methods are a key part of data science, yet few data scientists have formal statistical training. Courses and books on basic statistics rarely cover the topic from a data science perspective. The second edition of this popular guide adds comprehensive examples in Python, provides practical guidance on applying statistical methods to data science, tells you how to avoid their misuse, and gives you advice on what’s important and what’s not.
Many data science resources incorporate statistical methods but lack a deeper statistical perspective. If you’re familiar with the R or Python programming languages and have some exposure to statistics, this quick reference bridges the gap in an accessible, readable format.
With this book, you’ll learn:
- Why exploratory data analysis is a key preliminary step in data science
- How random sampling can reduce bias and yield a higher-quality dataset, even with big data
- How the principles of experimental design yield definitive answers to questions
- How to use regression to estimate outcomes and detect anomalies
- Key classification techniques for predicting which categories a record belongs to
- Statistical machine learning methods that “learn” from data
- Unsupervised learning methods for extracting meaning from unlabeled data.
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Naked Statistics: Stripping the Dread from the Data
As best-selling author Charles Wheelan shows us in Naked Statistics, the right data and a few well-chosen statistical tools can help us answer these questions and more.
For those who slept through Stats 101, this book is a lifesaver. Wheelan strips away the arcane and technical details and focuses on the underlying intuition that drives statistical analysis.
He clarifies key concepts such as inference, correlation, and regression analysis, reveals how biased or careless parties can manipulate or misrepresent data, and shows us how brilliant and creative researchers are exploiting the valuable data from natural experiments to tackle thorny questions.
And in Wheelan’s trademark style, there’s not a dull page in sight. You’ll encounter clever Schlitz Beer marketers leveraging basic probability, an International Sausage Festival illuminating the tenets of the central limit theorem, and a head-scratching choice from the famous game show Let’s Make a Deal―and you’ll come away with insights each time.
With the wit, accessibility, and sheer fun that turned Naked Economics into a bestseller, Wheelan defies the odds yet again by bringing another essential, formerly unglamorous discipline to life.
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The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Second Edition (Springer Series in Statistics)
This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics.
Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry.
The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting—the first comprehensive treatment of this topic in any book.
This major new edition features many topics not covered in the original, including graphical models, random forests, ensemble methods, least angle regression & path algorithms for the lasso, non-negative matrix factorisation, and spectral clustering.
There is also a chapter on methods for “wide” data (p bigger than n), including multiple testing and false discovery rates.
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Statistics for Data Scientists: An Introduction to Probability, Statistics, and Data Analysis (Undergraduate Topics in Computer Science)
This book provides an undergraduate introduction to analysing data for data science, computer science, and quantitative social science students. It uniquely combines a hands-on approach to data analysis – supported by numerous real data examples and reusable [R] code – with a rigorous treatment of probability and statistical principles.
Where contemporary undergraduate textbooks in probability theory or statistics often miss applications and an introductory treatment of modern methods (bootstrapping, Bayes, etc.), and where applied data analysis books often miss a rigorous theoretical treatment, this book provides an accessible but thorough introduction into data analysis, using statistical methods combining the two viewpoints.
The book further focuses on methods for dealing with large data-sets and streaming-data and hence provides a single-course introduction of statistical methods for Best data science Books for Beginners.
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Head First Statistics: A Brain-Friendly Guide
Whether you’re a student, a professional, or just curious about statistical analysis, Head First’s brain-friendly formula helps you get a firm grasp of statistics so you can understand key points and actually use them. Learn to present data visually with charts and plots; discover the difference between taking the average with mean, median, and mode, and why it’s important; learn how to calculate probability and expectation; and much more.
Head First Statistics is ideal for high school and college students taking statistics and satisfies the requirements for passing the College Board’s Advanced Placement (AP) Statistics Exam. With this book, you’ll:
- Study the full range of topics covered in first-year statistics
- Tackle tough statistical concepts using Head First’s dynamic, visually rich format proven to stimulate learning and help you retain knowledge
- Explore real-world scenarios, ranging from casino gambling to prescription drug testing, to bring statistical principles to life
- Discover how to measure spread, calculate odds through probability, and understand the normal, binomial, geometric, and Poisson distributions
- Conduct sampling, use correlation and regression, do hypothesis testing, perform chi square analysis, and more
Before you know it, you’ll not only have mastered statistics, you’ll also see how they work in the real world. Head First Statistics will help you pass your statistics course, and give you a firm understanding of the subject so you can apply the knowledge throughout your life.
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An Introduction to Statistical Learning: with Applications in R
An Introduction to Statistical Learning provides an accessible overview of the field of statistical learning, an essential toolset 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, deep learning, survival analysis, multiple testing, 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.
This Second Edition features new chapters on deep learning, survival analysis, and multiple testing, as well as expanded treatments of naïve Bayes, generalized linear models, Bayesian additive regression trees, and matrix completion. R code has been updated throughout to ensure compatibility.
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Think Stats: Exploratory Data Analysis
By working with a single case study throughout this thoroughly revised book, you’ll learn the entire process of exploratory data analysis—from collecting data and generating statistics to identifying patterns and testing hypotheses. You’ll explore distributions, rules of probability, visualization, and many other tools and concepts.
New chapters on regression, time series analysis, survival analysis, and analytic methods will enrich your discoveries.
- Develop an 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
- Import data from most sources with Python, rather than rely on data that’s cleaned and formatted for statistics tools
- Use statistical inference to answer questions about real-world data
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Statistics in Plain English
This introductory textbook provides an inexpensive, brief overview of statistics to help readers gain a better understanding of how statistics work and how to interpret them correctly.
Each chapter describes a different statistical technique, ranging from basic concepts like central tendency and describing distributions to more advanced concepts such as t tests, regression, repeated measures ANOVA, and factor analysis. Each chapter begins with a short description of the statistic and when it should be used.
This is followed by a more in-depth explanation of how the statistic works. Finally, each chapter ends with an example of the statistic in use, and a sample of how the results of analyses using the statistic might be written up for publication.
A glossary of statistical terms and symbols is also included. Using the author’s own data and examples from published research and the popular media, the book is a straightforward and accessible guide to statistics.
New features in the fourth edition include:
- sets of work problems in each chapter with detailed solutions and additional problems online to help students test their understanding of the material,
- new “Worked Examples” to walk students through how to calculate and interpret the statistics featured in each chapter,
- new examples from the author’s own data and from published research and the popular media to help students see how statistics are applied and written about in professional publications,
- many more examples, tables, and charts to help students visualize key concepts, clarify concepts, and demonstrate how the statistics are used in the real world.
- a more logical flow, with correlation directly preceding regression, and a combined glossary appearing at the end of the book,
- a Quick Guide to Statistics, Formulas, and Degrees of Freedom at the start of the book, plainly outlining each statistic and when students should use them,
- greater emphasis on (and description of) effect size and confidence interval reporting, reflecting their growing importance in research across the social science disciplines
- an expanded website at www.routledge.com/cw/urdan with PowerPoint presentations, chapter summaries, a new test bank, interactive problems and detailed solutions to the text’s work problems, SPSS datasets for practice, links to useful tools and resources, and videos showing how to calculate statistics, how to calculate and interpret the appendices, and how to understand some of the more confusing tables of output produced by SPSS.
Statistics in Plain English, Fourth Edition is an ideal guide for statistics, research methods, and/or for courses that use statistics taught at the undergraduate or graduate level, or as a reference tool for anyone interested in refreshing their memory about key statistical concepts. The research examples are from psychology, education, and other social and behavioral sciences.
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Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference
Bayesian methods of inference are deeply natural and extremely powerful. However, most discussions of Bayesian inference rely on intensely complex mathematical analyses and artificial examples, making it inaccessible to anyone without a strong mathematical background.
Now, though, Cameron Davidson-Pilon introduces Bayesian inference from a computational perspective, bridging theory to practice–freeing you to get results using computing power.
Bayesian Methods for Hackers illuminates Bayesian inference through probabilistic programming with the powerful PyMC language and the closely related Python tools NumPy, SciPy, and Matplotlib. Using this approach, you can reach effective solutions in small increments, without extensive mathematical intervention.
Davidson-Pilon begins by introducing the concepts underlying Bayesian inference, comparing it with other techniques and guiding you through building and training your first Bayesian model. Next, he introduces PyMC through a series of detailed examples and intuitive explanations that have been refined after extensive user feedback.
You’ll learn how to use the Markov Chain Monte Carlo algorithm, choose appropriate sample sizes and priors, work with loss functions, and apply Bayesian inference in domains ranging from finance to marketing. Once you’ve mastered these techniques, you’ll constantly turn to this guide for the working PyMC code you need to jumpstart future projects.
Coverage includes
- Learning the Bayesian “state of mind” and its practical implications
- Understanding how computers perform Bayesian inference
- Using the PyMC Python library to program Bayesian analyses
- Building and debugging models with PyMC
- Testing your model’s “goodness of fit”
- Opening the “black box” of the Markov Chain Monte Carlo algorithm to see how and why it works
- Leveraging the power of the “Law of Large Numbers”
- Mastering key concepts, such as clustering, convergence, autocorrelation, and thinning
- Using loss functions to measure an estimate’s weaknesses based on your goals and desired outcomes
- Selecting appropriate priors and understanding how their influence changes with dataset size
- Overcoming the “exploration versus exploitation” dilemma: deciding when “pretty good” is good enough
- Using Bayesian inference to improve A/B testing
- Solving data science problems when only small amounts of data are available
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Conclusion:
Up to now, we have discussed the 9 Best Statistics Books for Data Science for Beginners, and also some best Statistics Books for Data Science for Beginners to Advanced Learners.
Still, if you find some of the Best Statistics books for Data Science for beginners to advanced are missing then please comment in the comment section. Thanks for reading the Best Statistics books for beginners.