Julia 1.0 Programming Cookbook

More Information
  • Boost your code’s performance using Julia’s unique features
  • Organize data in to fundamental types of collections: arrays and dictionaries
  • Organize data science processes within Julia and solve related problems
  • Scale Julia computations with cloud computing
  • Write data to IO streams with Julia and handle web transfer
  • Define your own immutable and mutable types
  • Speed up the development process using metaprogramming

Julia, with its dynamic nature and high-performance, provides comparatively minimal time for the development of computational models with easy-to-maintain computational code. This book will be your solution-based guide as it will take you through different programming aspects with Julia.

Starting with the new features of Julia 1.0, each recipe addresses a specific problem, providing a solution and explaining how it works. You will work with the powerful Julia tools and data structures along with the most popular Julia packages. You will learn to create vectors, handle variables, and work with functions. You will be introduced to various recipes for numerical computing, distributed computing, and achieving high performance. You will see how to optimize data science programs with parallel computing and memory allocation. We will look into more advanced concepts such as metaprogramming and functional programming. Finally, you will learn how to tackle issues while working with databases and data processing, and will learn about on data science problems, data modeling, data analysis, data manipulation, parallel processing, and cloud computing with Julia.

By the end of the book, you will have acquired the skills to work more effectively with your data

  • Address the core problems of programming in Julia with the most popular packages for common tasks
  • Tackle issues while working with Databases and Parallel data processing with Julia
  • Explore advanced features such as metaprogramming, functional programming, and user defined types
Page Count 460
Course Length 13 hours 48 minutes
ISBN 9781788998369
Date Of Publication 28 Nov 2018


Bogumił Kamiński

Bogumił Kamiński (GitHub username: bkamins) is an associate professor and head of the Decision Support and Analysis Unit at the SGH Warsaw School of Economics, as well as adjunct professor at the data science laboratory, Ryerson University, Toronto. He is coeditor of the Central European Journal of Economic Modeling and Econometrics, and of the Multiple Criteria Decision Making journal. His scientific interests center on operational research and computational social science. He has authored over 50 research articles on simulation, optimization, and prediction methods. He also has 15+ years' experience in the deployment of large-scale advanced analytics solutions for industry and public administration.

Przemysław Szufel

Przemysław Szufel (GitHub username: pszufe) is an assistant professor in the Decision Support and Analysis Unit at the SGH Warsaw School of Economics. His current research focuses on distributed systems and methods for the execution of large-scale simulations for numerical experiments and optimization. He is working on asynchronous algorithms for the parallel execution of large-scale computations in the cloud and distributed computational environments. He has authored, and co-authored, several open source tools for high-performance and numerical simulation.