Data Science with Julia
McNicholas, Paul D.; Tait, Peter
Sold by Biblios, Frankfurt am main, HESSE, Germany
AbeBooks Seller since September 10, 2024
New - Hardcover
Condition: New
Quantity: 3 available
Add to basketSold by Biblios, Frankfurt am main, HESSE, Germany
AbeBooks Seller since September 10, 2024
Condition: New
Quantity: 3 available
Add to basket"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
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:
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
Université Côte d’Azur, Nice, France
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.
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