Название: An Introduction to R and Python for Data Analysis: A Side-By-Side ApproachАвтор: Taylor R. Brown
Издательство: CRC Press
Год: 2023
Страниц: 267
Язык: английский
Формат: pdf (true)
Размер: 10.2 MB
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 at the author’s GitHub site. 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.
This book is written to be used in a computing class that teaches both R and Python to graduate students in a statistics or Data Science department.This book is written for students that do not necessarily possess any previous familiarity with writing code.
• If you are using them for analyzing data, R and Python do a lot of the same things in pretty similar ways, so it does not always make sense to either a.) teach one language after the other, or b.) choose one over the other. The side-by-side approach helps to teach/learn more, save time, and reinforce shared concepts and key differences.
• This text does not describe statistical modeling techniques in detail, but many exercises will ask students to implement them. These exercises will not assume mathematical familiarity. The objective is to test programming ability, and an added benefit is that the work might be useful later in their careers.
• This book is written for aspiring data scientists, not necessarily aspiring software developers. Why do I draw the distinction? When discussing different types, for example, I do not discuss data structures in any depth. Rather, I discuss examples of applications where different types would be most useful.
• Generally speaking, chapters should be read in order, but occasionally skipping ahead can be beneficial. Later chapters are more may assume some familiarity with previous chapters. Also, within a chapter’s section, sometimes a discussion for a particular topic in Python, say, might follow a discussion about the same topic in R. In this case, the later section will often assume that the previous section has been read first.
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