Top 10 Best Practical Statistics For Data Scientists

of November 2024
1
Best ChoiceBest Choice
Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and
O'Reilly Media
O'Reilly Media
10
Exceptional
View on Amazon
2
Best ValueBest Value
Practical Statistics for Data Scientists: 50 Essential Concepts
O'Reilly Media
O'Reilly Media
9.9
Exceptional
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3
Data Science from Scratch: First Principles with Python
9.8
Exceptional
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4
Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG,
9.7
Exceptional
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5
Naked Statistics: Stripping the Dread from the Data
W W Norton Company
W W Norton Company
9.6
Exceptional
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6
Becoming a Data Head: How to Think, Speak and Understand Data Science,
9.5
Excellent
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7
Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing
Cambridge University Press
Cambridge University Press
9.4
Excellent
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8
Python Data Science Handbook: Essential Tools for Working with Data
O'Reilly Media
O'Reilly Media
9.3
Excellent
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9
Storytelling with Data: A Data Visualization Guide for Business Professionals
Wiley
Wiley
9.2
Excellent
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10
An Introduction to Statistical Learning: with Applications in R (Springer Texts
Springer
Springer
9.1
Excellent
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About Practical Statistics For Data Scientists

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Practical Statistics for Data Scientists: 50+ Essential Concepts Using R and Python

Practical Statistics for Data Scientists: 50 Essential Concepts

Data Science from Scratch: First Principles with Python

Ace the Data Science Interview: 201 Real Interview Questions Asked By FAANG, Tech Startups, & Wall Street

Naked Statistics: Stripping the Dread from the Data

W W Norton Company.

Becoming a Data Head: How to Think, Speak and Understand Data Science, Statistics and Machine Learning

Trustworthy Online Controlled Experiments: A Practical Guide to A/B Testing

Python Data Science Handbook: Essential Tools for Working with Data

O'Reilly Media.

Storytelling with Data: A Data Visualization Guide for Business Professionals

Wiley. Language english. Book - storytelling with data a data visualization guide for business professionals.

An Introduction to Statistical Learning: with Applications in R (Springer Texts in Statistics)

This book presents some of the most important modeling and prediction techniques, along with relevant applications. Topics include linear regression, classification, re-sampling 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.
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