Data Science with Julia

Name: Data Science with Julia
Brand: Taylor & Francis
SKU: 9781138499980
Price: 134.0 NZD
Availability: InStock

By Paul D. McNicholas and Peter Tait

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Summary

Data Science with Julia introduces readers to Julia, an open-source programming language designed to combine the ease of use of R and Python with the speed of C and Fortran. The book covers core Julia components and relevant packages for data input, manipulation, and visualisation, illustrating key data science methods including supervised and unsupervised learning. It also explores data visualisation using the Gadfly package and optimisation techniques to enhance code performance. Suitable for those new to programming or experienced R users seeking to learn Julia, this guide equips readers to become efficient data scientists.
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Format: Paperback / softback

$134⁰⁰

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Data Science with Julia

$134⁰⁰

Book Hero Magic created this recommendation. While it's new and still learning, it may not be perfect - your feedback is welcome! IS THIS YOUR NEXT READ?

Ideal for senior undergraduates, beginning graduate students, and practising data scientists interested in adopting Julia for their data science workflow. No prior programming experience is required, making it accessible to a broad educational audience.

There is a dearth of resources for data scientists, statisticians, etc., wishing to learn about Julia. Using well known data science methods, this book will both motivate the reader and assuage any unease. The book will get readers up to speed on key features of the Julia language and illustrate some of its advantages for data science work.

Description

Data Science with Julia is described as "a great way to both start learning data science through the promising Julia language and to become an efficient data scientist," according to Professor Charles Bouveyron, INRIA Chair in Data Science, Université Côte d’Azur, Nice, France.

Julia is an open-source programming language created to be as easy to use as languages like R and Python, while also achieving speeds comparable to C and Fortran. With its accessible, intuitive, and highly efficient base, Julia becomes a formidable language for data science. Using well-known data science methods that motivate the reader, Data Science with Julia helps readers get up to speed on key features of the Julia language, illustrating 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 visualisation using the Gadfly package, designed to emulate the popular ggplot2 package in R. Readers will learn how to create many common plots and visualise model results.
Presents techniques to optimise Julia code for performance.
Serves as an ideal resource for individuals knowledgeable in R wanting to learn Julia, although no previous knowledge of R or any programming language is required.

The advantages of Julia for data science cannot be understated. Beyond its speed and ease of use, there are 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. This book is suitable for senior undergraduates, beginner graduate students, or practising data scientists seeking 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
Université Côte d’Azur, Nice, France

Book Details

INFORMATION

ISBN: 9781138499980

Publisher: Taylor & Francis Ltd

Format: Paperback / softback

Date Published: 11 January 2019

Country: United Kingdom

Imprint: CRC Press

Audience: Professional and scholarly

DIMENSIONS

Width: 138.0mm

Height: 216.0mm

Weight: 336g

Pages: 240

Collections

Computing & Technology Education & Reference Non-Fiction Paul D. McNicholas Peter Tait Science & Nature

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.