"This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist."- Professor Charles Bouveyron, INRIA Chair in Data Science, Universit� C�te d’Azur, Nice, France

Julia, an open-source programming language, was created to be as easy to use as languages such as R and Python while also as fast as C and Fortran. An accessible, intuitive, and highly efficient base language with speed that exceeds R and Python, makes Julia a formidable language for data science. Using well known data science methods that will motivate the reader, Data Science with Julia will get readers up to speed on key features of the Julia language and illustrate its facilities for data science and machine learning work.

Features:

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).

The advantages of Julia for data science cannot be understated. Besides speed and ease of use, there are already over 1,900 packages available and Julia can interface (either directly or through packages) with libraries written in R, Python, Matlab, C, C++ or Fortran. The book is for senior undergraduates, beginning graduate students, or practicing data scientists who want to learn how to use Julia for data science.

"This book is a great way to both start learning data science through the promising Julia language and to become an efficient data scientist."

Professor Charles Bouveyron
INRIA Chair in Data Science
Universit� C�te d’Azur, Nice, France

About the Author

Paul D. McNicholas is the Canada Research Chair in Computational Statistics at McMaster University, where he is a Professor in the Department of Mathematics and Statistics.

Peter Tait is a Ph.D. student at the Department of Mathematics and Statistics at McMaster University. Prior to returning to academia, he worked as a data scientist in the software industry, where he gained extensive practical experience.

"About this title" may belong to another edition of this title.

Store Description

The Saint Bookstore has a range of over 1 million titles available.

Visit Seller's Storefront

Seller's business information

SB ONLINE LTD
50 Devonshire Road, Southport, PR9 7BZ, United Kingdom

Sale & Shipping Terms

Terms of Sale

Please order through the Abebooks checkout. We only take orders through Abebooks - We don't take direct orders by email or phone.

Refunds or Returns: A full refund of the purchase price will be given if returned within 30 days in undamaged condition.

As a seller on abebooks we adhere to the terms explained at http://www.abebooks.co.uk/docs/HelpCentral/buyerIndex.shtml - if you require further assistance please email us at orders@thesaintbookstore.co.uk

Right of cancellation

Shipping Terms

Most orders usually ship within 1-3 business days, but some can take up to 7 days.

Shipping rates from United Kingdom to U.S.A.

Shipping rates from United Kingdom to U.S.A.
Order quantity	7 to 28 business days	7 to 28 business days
First item	� 12.43	� 14.43

Delivery times are set by sellers and vary by carrier and location. Orders passing through Customs may face delays and buyers are responsible for any associated duties or fees. Sellers may contact you regarding additional charges to cover any increased costs to ship your items.