Julia for data science zacharias voulgaris pdf

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Julia for Data Science by Zacharias Voulgaris (, Paperback) for sale online | eBay

Julia is a relatively new programming language that was developed for Data Science by a group of researchers in MIT. Unlike other programming languages that excel at a niche use case, Julia is a multipurpose language geared towards a variety of applications and a larger audience. Lately, its usefulness in data science became more evident as more packages made it into the Julia ecosystem. Julia is a good general purpose programming language because of six main points. Firstly, it is light and efficient. In fact, there are use cases where very lightweight computers, such as a tiny computer, have been used with Julia to perform computationally intensive operations in real-time.
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Intro to Julia for data science

Julia for Data Science. by Zacharias Voulgaris PhD. Publisher: Technics Publications. Release Date: September ISBN: View table of.

Julia and Its Usefulness in Data Science

Get jupyter notebooks for the following youtube tutorials here or run them directly on JuliaBox. Julia is ready for the classroom. We encourage instructors to participate in the Julia community resources for questions about Julia or specific packages. This page puts together various resources that instructors may find useful. Tutorials and other learning materials are in the learning section of the website. Julia is now being used in several universities and online courses.

Specialized script packages are introduced and described. Hands-on problems representative of those commonly encountered throughout the data science pipeline are provided, and we guide you in the use of Julia in solving them using published datasets. Many of these scenarios make use of existing packages and built-in functions, as we cover:. An overview of the data science pipeline along with an example illustrating the key points, implemented in Julia. Engineering tasks, such as importing, cleaning, formatting and storing data, as well as performing data preprocessing. Data visualization and some simple yet powerful statistics for data exploration purposes.

But why look to a relatively new language when you already have other commonly-used languages at your disposal? Pitted against R for example, Julia is a lot faster and less quirky as a programming language. After covering the importance of Julia to the data science community and several essential data science principles, the book starts with the basics including how to install Julia and its libraries. Many examples are provided as the author illustrates how to leverage each Julia command, data set, and function. The book introduces and describes specialized script packages. Hands-on problems representative of those commonly encountered throughout the data science pipeline are provided, and the book guides you in the use of Julia in solving them using published data sets. Many of these scenarios make use of existing packages and built-in functions.

Author: Zacharias Voulgaris, PhD. ISBN, print. ISBN, Kindle ISBN, ePub. ISBN, PDF.
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Towards Data Science

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Zacharias Voulgaris was born in Athens, Greece. He also was a Program Manager at Microsoft, on a data analytics pipeline for Bing. Julia for Data Science. Zacharias Voulgaris. Master how to use the Julia language to solve business critical data science challenges.

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  1. Editorial Reviews. About the Author. Dr. Zacharias Voulgaris was born in Athens, Greece. He studied Production Engineering and Management at the Technical.

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