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Practical Statistics for Data Scientists, 2e: 50+ Essential Concepts Using R and Python
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Practical Statistics for Data Scientists, 2e: 50+ Essential Concepts Using R and Python
Practical Statistics for Data Scientists is a must-read for anyone who wants to implement statistical methods in practical data science projects. Comprehensive examples in R and Python are included, and the book's focus on the practical aspects of statistics is refreshing.
Practical Statistics for Data Scientists, 2e: 50+ Essential Concepts Using R and Python
Artigo n.º: 90371535

Practical Statistics for Data Scientists, 2e: 50+ Essential Concepts Using R and Python

87% of respondents would recommend this to a friend

Artigo n.º: 90371535

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Practical Statistics for Data Scientists is a must-read for anyone who wants to implement statistical methods in practical data science projects. Comprehensive examples in R and Python are included, and the book's focus on the practical aspects of statistics is refreshing.
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O que se Destaca

Comprehensive Approach
Covers over 50 essential statistical concepts, making it an invaluable resource for both beginners and seasoned data scientists looking to enhance their statistical skills using R and Python.
Dual Language Support
Utilizes both R and Python for practical examples, allowing readers to easily switch between programming languages and apply statistical methods in various contexts relevant to their needs.
Hands-On Learning
Includes practical examples and exercises that facilitate hands-on experience, ensuring readers not only understand theoretical concepts but can also apply them effectively in real-world data scenarios.

Detalhes do produto

Shop Practical Statistics for Data Scientists, 2e: 50+ Essential Concepts Using R and Python online at a best price in São Tomé and Príncipe. 149207294X
  • Practical Statistics for Data Scientists: 50 Essential Concepts Using R and Python 2nd Edition
  • Suitable for data scientists familiar with R and/or Python and have some prior knowledge of statistics
  • Authors provide an appreciation of statistics' contribution to data science
  • Includes code examples in both R and Python
  • Output and plots shown using R code, complete code and data sets available for download at GitHub
  • Goals: lay out key statistics concepts relevant to data science and explain their importance and usefulness
Publisher Ou2032Reilly
Publication date 29 Jun. 2020
Edition 2nd
Language English
Print length 360 pages
ISBN-10 149207294X
ISBN-13 978-1492072942
Item weight 612 g
Dimensions 17.78 x 2.29 x 23.11 cm

Quem Deverá Comprar?

Suitable For
  • Aspiring Data Scientists

    Ideal for beginners wanting to learn essential statistical concepts with practical applications in R and Python.

  • Working Professionals

    Great for professionals looking to enhance their statistical knowledge and apply it in data science projects.

  • Students of Statistics

    Beneficial for students needing a practical guide to statistics for academic courses and real-world application.

Not Suitable For
  • Advanced Statisticians

    Not suitable for experts who require complex statistical methodologies beyond the essentials covered in the book.

DESCRIÇÃO DO PRODUTO

Practical Statistics for Data Scientists, 2e: 50+ Essential Concepts Using R and Python

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Perguntas e respostas do cliente

  • Pergunta: Does this book include examples in Python?

    Resposta: Yes, the second edition of the book offers comprehensive examples in both R and Python programming languages.
  • Pergunta: Who would benefit from reading this book?

    Resposta: Anyone who wants to implement statistical methods in practical data science projects would benefit from reading this book.
  • Pergunta: What is the focus of this book?

    Resposta: This book focuses on the practical aspects of statistics in the context of data science.

Data Mining Editorial Review

Practical Statistics For Data Scientists, 2e 50+ Essential Concepts Using R And Python is a comprehensive guide that effectively introduces fundamental data science concepts from traditional statistics to machine learning models. The book is well-organized into digestible sections, with practical examples in both R and Python, which enhance understanding. Readers have noted the high-quality writing and the clarity of explanations, making it a valuable resource for anyone looking to fill gaps in their statistical knowledge. However, some commenters expressed a desire for more real-life examples and practical applications, particularly for complex concepts. The coverage of basic statistics as well as classification and regression tasks adds depth to the learning experience, but may feel repetitive for advanced users familiar with other materials.

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Prós

  • Well-written and easy to understand for beginners.
  • Covers fundamental concepts relevant to data science.
  • Explains topics with practical examples using R and Python.
  • Organized structure makes it easy to follow.
  • Good resource for filling statistical knowledge gaps.

Contras

  • Could benefit from more practical examples for complex concepts.

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Recursos e benefícios

  • Comprehensive examples in Python
  • Practical guidance on applying statistical methods
  • Accessible, readable format
  • Key preliminary step in data science
  • Classification techniques for predicting
  • Unsupervised learning methods

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