In this chapter, you will learn about performing parallel computing and using it to handle big data. So, some concepts such as data movements, sharded arrays, and the Map-Reduce framework are important to know in order to handle large amounts of data by computing on it using parallelized CPUs. So, all the concepts discussed in this chapter will help you build good parallel computing and multiprocessing basics, including efficient data handling and code optimization.
Julia Cookbook
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Julia Cookbook
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Overview of this book
Want to handle everything that Julia can throw at you and get the most of it every day? This practical guide to programming with Julia for performing numerical computation will make you more productive and able work with data more efficiently. The book starts with the main features of Julia to help you quickly refresh your knowledge of functions, modules, and arrays. We’ll also show you how to utilize the Julia language to identify, retrieve, and transform data sets so you can perform data analysis and data manipulation.
Later on, you’ll see how to optimize data science programs with parallel computing and memory allocation. You’ll get familiar with the concepts of package development and networking to solve numerical problems using the Julia platform.
This book includes recipes on identifying and classifying data science problems, data modelling, data analysis, data manipulation, meta-programming, multidimensional arrays, and parallel computing. By the end of the book, you will acquire the skills to work more effectively with your data.
Table of Contents (12 chapters)
Julia Cookbook
Credits
About the Author
About the Reviewer
www.PacktPub.com
Preface
Free Chapter
Extracting and Handling Data
Metaprogramming
Statistics with Julia
Building Data Science Models
Working with Visualizations
Customer Reviews