データ分析のためのＲとPython：両面からの入門 An Introduction to R and Python for Data Analysis : A Side-By-Side Approach

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データ分析のためのＲとPython：両面からの入門<br>An Introduction to R and Python for Data Analysis : A Side-By-Side Approach

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¥22,921

電子書籍

データ分析のためのＲとPython：両面からの入門
An Introduction to R and Python for Data Analysis : A Side-By-Side Approach

著者名：Brown, Taylor R.
価格 ¥17,499 （本体¥15,909）
Chapman and Hall/CRC（2023/06/28発売）
ポイント 159pt　（実際に付与されるポイントはご注文内容確認画面でご確認下さい）

言語：ENG
ISBN：9781032203256
eISBN：9781000896008

ファイル： /

Description

An Introduction to R and Python for Data Analysis helps teach students to code in both R and Python simultaneously. As both R and Python can be used in similar manners, it is useful and efficient to learn both at the same time, helping lecturers and students to teach and learn more, save time, whilst reinforcing the shared concepts and differences of the systems. This tandem learning is highly useful for students, helping them to become literate in both languages, and develop skills which will be handy after their studies. This book presumes no prior experience with computing, and is intended to be used by students from a variety of backgrounds. The side-by-side formatting of this book helps introductory graduate students quickly grasp the basics of R and Python, with the exercises providing helping them to teach themselves the skills they will need upon the completion of their course, as employers now ask for competency in both R and Python. Teachers and lecturers will also find this book useful in their teaching, providing a singular work to help ensure their students are well trained in both computer languages. All data for exercises can be found here: https://github.com/tbrown122387/r_and_python_book/tree/master/data. Instructors can access the solutions manual via the book's website.

Key features:　

- Teaches R and Python in a "side-by-side" way.　

- Examples are tailored to aspiring data scientists and statisticians, not software engineers.　

- Designed for introductory graduate students.

- Does not assume any mathematical background.

1. Introduction 2. Basic Types 3. R vectors versus Numpy arrays and Pandas’ Series 4. Numpy ndarrays Versus R’s matrix and array Types 5. R’s lists Versus Python’s lists and dicts 6. Functions 7. Categorical Data 8. Data Frames Part 1. Introducing the Basics 10. Using Third-Party Code 11. Control Flow 12. Reshaping and Combining Data Sets 13. Visualization Part 2. Common Tasks and Patterns 14. An Introduction to Object-Oriented Programming 15. An Introduction to Functional Programming