• Title: An Introduction to R and Python for Data Analysis: A Side-By-Side Approach
• Author(s) Taylor R. Brown
• Publisher: Chapman and Hall/CRC; 1st edition (June 28, 2023); eBook (Creative Commons Licensed)
• License(s): Creative Commons License (CC)
• Paperback: 246 pages
• eBook: HTML
• Language: English
• ISBN-10: 1032203250
• ISBN-13: 978-1032203256
• Share This:  

Book Description

This book helps teach students to code in both R and Python simultaneously. The book is written in an engaging, collaborative style that makes it enjoyable to read. It maintains its formality without creating a barrier between the reader and the content.

• Taylor R. Brown is an assistant professor of statistics at the University of Virginia.

• Amazon

• Data Analysis and Data Mining
• R Programming
• Python Programming

• An Introduction to R and Python for Data Analysis: A Side-By-Side Approach (Taylor R. Brown)
• The Mirror Site (1) - PDF
• The Mirror Site (2) - PDF

• Python for Data Analysis: Pandas, NumPy, and Jupyter

  The focus is specifically on Python programming, libraries, and tools as opposed to data analysis methodology. This is the Python programming you need for data analysis. You'll learn the latest versions of pandas, NumPy, and Jupyter in the process.

• Introduction to Statistical Data Analysis with R (Matthias Kohl)

  The book offers an introduction to statistical data analysis applying the free statistical software R, probably the most powerful statistical software today. The analyses are performed and discussed using real data.

• R Cookbook: Recipes for Data Analysis, Statistics, and Graphics

  This book is full of how-to recipes, each of which solves a specific problem. Each recipe includes a quick introduction to the solution followed by a discussion that aims to unpack the solution and give you some insight into how it works.

• Python Programming for Economics and Finance

  Looking to enhance your skills in Economics and Finance? Dive into Python programming! With libraries like Pandas, NumPy, and Matplotlib, you can analyze data, build models, and visualize trends like never before.

• O'Reilly® Python Data Science Handbook: Essential Tools

  Several resources exist for individual pieces of this data science stack, but only with the Python Data Science Handbook do you get them all - IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and other related tools.

• Python for Everybody: Exploring Data in Python 3

  This book is designed to introduce students to programming and software development through the lens of exploring data. You can think of the Python programming language as your tool to solve data problems that are beyond the capability of a spreadsheet.

• Think Stats, 2nd Edition: Exploratory Data Analysis in Python

  This concise introduction shows you how to perform statistical analysis computationally, rather than mathematically, with programs written in Python.

• Elements of Data Science using Python (Allen B. Downey)

  This book is an introduction to data science for people with no programming experience. The goal is to present a small, powerful subset of Python that allows you to do real work in data science as quickly as possible.

• Mining Social Media using Python: Finding Stories in Data

  This book shows you how to use Python and key data analysis tools to find the stories buried in social media. Perform advanced data analysis using Python, Jupyter Notebooks, and the pandas library.

• R for Data Science: Visualize, Model, Transform, Tidy, Import

  This book teaches you how to do data science with R: You'll learn how to get your data into R, get it into the most useful structure, transform it, visualize it and model it, how data science can help you work with the uncertainty and capture the opportunities.