Data Science Using Python and R (Wiley Series on Methods and Applications in Data Mining)
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Data Science Using Python and R (Wiley Series on Methods and Applications in Data Mining)
Learn data science by doing data science! Data Science Using Python and R will get you plugged into the world’s two most widespread open-source platforms for data science: Python and R.
Data Science Using Python and R (Wiley Series on Methods and Applications in Data Mining)
Numéro d'article: 16954754

Data Science Using Python and R (Wiley Series on Methods and Applications in Data Mining)

87% des répondants recommanderaient ceci à un ami

Numéro d'article: 16954754

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Ce qui se démarque

Comprehensive Coverage
Combines Python and R for data science, providing a well-rounded approach to techniques, methodologies, and applications, addressing the needs of both beginners and experienced practitioners.
Practical Examples
Includes real-world case studies and practical applications, enabling readers to apply theoretical knowledge to solve actual problems, enhancing their learning experience and skill development.
Expert Insights
Authored by industry professionals, offering authoritative guidance and best practices, which ensures readers are learning from trusted sources, increasing the credibility and value of the information provided.

Détails du produit

Shop Data Science Using Python and R (Wiley Series on Methods and Applications in Data Mining) online at a best price in Belgium. 1119526817
  • Learn data science by doing data science! Data Science Using Python and R will get you plugged into the world’s two most widespread open-source platforms for data science: Python and R.Data science is hot. Bloomberg called data scientist “the hottest job in America.” Python and R are the top two open-source data science tools in the world. In Data Science Using Python and R, you will learn step-by-step how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Data Science Using Python and R is written for the general reader with no previous analytics or programming experience. An entire chapter is dedicated to learning the basics of Python and R. Then, each chapter presents step-by-step instructions and walkthroughs for solving data science problems using Python and R.Those with analytics experience will appreciate having a one-stop shop for learning how to do data science using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naïve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining.Further, exciting new topics such as random forests and general linear models are also included. The book emphasizes data-driven error costs to enhance profitability, which avoids the common pitfalls that may cost a company millions of dollars.Data Science Using Python and R provides exercises at the end of every chapter, totaling over 500 exercises in the book. Readers will therefore have plenty of opportunity to test their newfound data science skills and expertise. In the Hands-on Analysis exercises, readers are challenged to solve interesting business problems using real-world data sets.
Publisher Wiley
Publication date April 9, 2019
Edition 1st
Language English
Print length 256 pages
ISBN-10 1119526817
ISBN-13 978-1119526810
Item Weight 15.7 ounces (445.1 grams)
Dimensions 6.24 x 0.77 x 9.24 inches (15.8 x 2 x 23.5 cm)
Part of series Wiley Series on Methods and Applications in Data Mining

À qui est-ce destiné ?

Suitable For
  • Aspiring Data Scientists

    Ideal for beginners aiming to understand the foundational concepts of data science using Python and R.

  • Students and Academics

    Perfect for university students or researchers needing a comprehensive resource for data mining methodologies.

  • Professionals Transitioning

    Suitable for professionals seeking to switch careers to data science, providing practical tools and techniques.

Not Suitable For
  • Advanced Practitioners

    Not ideal for experienced data scientists looking for cutting-edge techniques and advanced applications.

DESCRIPTION DU PRODUIT

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Questions et réponses des clients

  • question: What topics are covered in 'Data Science Using Python and R'?

    répondre: The book 'Data Science Using Python and R' provides comprehensive coverage of key concepts in data science, including statistical methods, data visualization, machine learning, and data mining techniques. It integrates Python and R, two of the most popular programming languages in data science, enabling readers to apply practical skills in real-world scenarios. This text is essential for anyone looking to enhance their understanding of data-driven decision-making in various industries.
  • question: Who is the target audience for this book?

    répondre: The target audience for 'Data Science Using Python and R' includes students, professionals, and researchers in the fields of data science and analytics. It is suitable for individuals who have a foundational knowledge of programming and statistics and wish to advance their skills in data manipulation and analysis. As a resource, it serves both those new to the field and experienced practitioners looking to deepen their expertise.
  • question: How does this book compare Python and R in data analysis?

    répondre: This book effectively compares Python and R, outlining the strengths and weaknesses of each in context to data analysis. It presents case studies and examples demonstrating how each language can be utilized for different data science tasks. By juxtaposing the two languages, readers can appreciate their unique features and decide which tool best fits their project needs or personal preference, enhancing their analytical versatility.
  • question: Can beginners benefit from this book?

    répondre: Yes, beginners can benefit significantly from 'Data Science Using Python and R'. The book starts with foundational concepts and gradually introduces more complex topics, making it an ideal starting point for those new to data science. Practical examples accompany theoretical sections, allowing beginners to apply what they learn and build their confidence as they progress in their data science journey.
  • question: What practical applications does this book provide?

    répondre: This book includes a multitude of practical applications across various domains such as finance, healthcare, and marketing. Readers can find examples of how to handle real-world data, perform exploratory data analysis, and implement machine learning models. These examples not only reinforce theoretical knowledge but also equip readers with the skills needed to tackle real problems, making it a crucial resource for aspiring data scientists.
  • question: Are there any accompanying resources with this book?

    répondre: Yes, 'Data Science Using Python and R' often comes with supplementary resources. These may include access to online code repositories, datasets for practice, and instructional videos. Such resources enhance the learning experience, providing readers with additional tools to implement data analysis techniques and further their understanding of the content presented in the book.
  • question: What programming experience do I need to read this book?

    répondre: While prior programming experience in Python or R is beneficial, it is not strictly necessary to begin reading 'Data Science Using Python and R'. The book is structured to cater to various skill levels, introducing programming basics alongside data science concepts. Readers are encouraged to engage with the programming languages through guided exercises, making it accessible for novices and a useful refresher for those with some experience.
  • question: How can this book aid in professional development?

    répondre: Reading 'Data Science Using Python and R' can significantly aid in professional development by equipping readers with in-demand skills in data analytics and machine learning. As organizations increasingly rely on data-driven insights, knowledge of these programming languages and data science methodologies positions individuals favorably in the job market. Practicing the techniques provided in the book can help professionals adapt to evolving industry standards.
  • question: Where can I buy 'Data Science Using Python and R Wiley Series on Methods and Applications in Data Mining 1st Edition'?

    répondre: You can buy 'Data Science Using Python and R Wiley Series on Methods and Applications in Data Mining 1st Edition' from Ubuy. They offer a wide selection of books, including this one, and provide a reliable platform for purchasing high-quality academic materials in Belgium. Ubuy’s user-friendly interface makes it simple to find and purchase the book, ensuring you get your hands on this essential resource.
  • question: What is the importance of data mining in data science?

    répondre: Data mining is a critical component of data science, as it involves extracting valuable insights and patterns from large datasets. In 'Data Science Using Python and R', readers learn various data mining techniques that can be applied to analyze trends and make predictions. This knowledge is invaluable across multiple sectors, enabling professionals to inform strategic decisions and enhance operational efficiency through data-driven practices.

Python Editorial Review

Data Science Using Python and R is a book that provides an example-oriented approach and is well detailed, covering various data science topics. However, the book is only a couple of hundred pages and is printed on cheap paper. Difficult steps are glossed over, such as installing necessary packages to generate output. Additionally, the website to download the datasets mentioned in the book no longer exists.

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Avantages

  • Example-oriented approach
  • Well-detailed
  • Code snippets provided

Les inconvénients

  • Printed on cheap paper

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Caractéristiques et avantages

  • Learn data science using Python and R.
  • The book provides step-by-step instructions and walk-throughs for solving data science problems.
  • It covers several topics including data preparation, exploratory data analysis, decision trees, regression modeling, and more.
  • It emphasizes data-driven error costs to enhance profitability.
  • It includes over 500 exercises and Hands-on Analysis exercises to challenge readers.
  • Recommended for the general reader with no previous analytics or programming experience.

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