Название: Learning R and Python for Business School Students
Автор: Yuxing Yan
Издательство: Cambridge Scholars Publishing
Год: 2023
Страниц: 703
Язык: английский
Формат: pdf (true)
Размер: 13.3 MB

This book provides a guide for business school students, individual investors, and business professionals to learn R and Python, two open-source programming languages. It is unique since it allows the reader to learn programming in an “R-assisted learning environment”. The book provides 15 weeks’ worth of teaching material for the reader.

It is crazy to teach business students both R and Python at the same time! It was my initial thought when asked to teach such a course. However, after struggling heavily together with my students (over three semesters), I found that such a course is not only possible, but also desirable. In a recent paper, I summarized my related teaching experiences into several areas: 1) write my own lecture notes; 2) use concepts and formulae suitable for business students; 3) tons of in-class exercises; 4)use the same formulae, data sets or even the same exercises several times; 5) at least one video for each chapter; 6) over 1,000 small programs; 7) many utility functions, and 8) spend more time on R, then compare Python with R constantly.

One of the main features of this book is that it is not just a book or a textbook. There are many accompanying materials, such as over 1,000 programs written in R and Python, 200 data sets in CSV, RData, sas7bdat, and pickle (Python data sets) formats. On the other hand, R is used to help readers and students learn, along with many utility functions. Among a dozen utility functions, a function called .searchWebs() could be used to search various web pages and data sets. This function makes addressing some data sets efficient with, just a few lines of R or Python code. We will elaborate those properties in the next sections.

From this book, readers learn basic concepts and programming skills related to R, and Python, then apply them to finance, accounting, marketing, and other business areas. This is a beginner’s book. In other words, we do not assume that readers have any prior knowledge or skills for R or Python.

A reader or student could learn both R and Python within one semester if the book is used as a textbook with a good instructor. On the other hand, if a learner learns both languages by himself/herself, it is a good idea to take two semesters, i.e., at a slow pace. There are three parts of the book: R, Python, and applications of R and Python. If this book is adopted as a textbook, the first two parts are more than enough for one semester. The first 13 chapters are centered on R. Starting from Chapter 14: Python Basics, we will focus on Python. One important feature of the second half of the book is that we constantly compare Python with R. Such a comparison serves two purposes. First, learners/students could refresh their mind about R knowledge and related functions. Second, it is much easier to use the same concept with a different language.