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An introduction to R and Python for ...

Brown, Taylor R.

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An introduction to R and Python for data analysis = a side-by-side approach /

Record Type:	Electronic resources : Monograph/item
Title/Author:	An introduction to R and Python for data analysis/ Taylor R. Brown.
Reminder of title:	a side-by-side approach /
Author:	Brown, Taylor R.
Published:	Boca Raton, FL :Chapman & Hall/CRC Press, : 2023.,
Description:	1 online resource :ill.
Subject:	R (Computer program language) -
Online resource:	https://www.taylorfrancis.com/books/9781003263241
ISBN:	9781003263241

An introduction to R and Python for data analysis = a side-by-side approach /
Brown, Taylor R.

An introduction to R and Python for data analysisa side-by-side approach /[electronic resource] :Taylor R. Brown. - 1st ed. - Boca Raton, FL :Chapman & Hall/CRC Press,2023. - 1 online resource :ill.

Includes bibliographical references and index.

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.

ISBN: 9781003263241Subjects--Topical Terms:

784593
R (Computer program language)

LC Class. No.: QA76.73.R3

Dewey Class. No.: 005.133

An introduction to R and Python for data analysis = a side-by-side approach /
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520 $a 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.
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