This book aims to show that Julia is an accessible, intuitive, and highly efficient base language with speed that exceeds R and Python. Authors Paul D. McNicholas and Peter Tait get readers up to speed on key features of the Julia language and illustrate its facilities for data science and machine learning work. Using well known data science methods, the book shows what makes Julia a formidable language for data science.
Author: Paul D. McNicholas and Peter Tait
Publisher: Chapman and Hall/CRC
Date: January 2019
Audience: Senior undergraduates or practicing data scientists
Category: Data Science
- Covers the core components of Julia as well as packages relevant to the input, manipulation and representation of data.
- Discusses several important topics in data science including supervised and unsupervised learning.
- Reviews data visualization using the Gadfly package, which was designed to emulate the very popular ggplot2 package in R. Readers will learn how to make many common plots and how to visualize model results.
- Presents how to optimize Julia code for performance.
- Will be an ideal source for people who already know R and want to learn how to use Julia (though no previous knowledge of R or any other programming language is required).
For recommendations of Data science books see Reading Your Way Into Big Data in our Programmer's Bookshelf section.
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