Julia Cookbook

More Information
Learn
  • Extract and handle your data with Julia
  • Uncover the concepts of metaprogramming in Julia
  • Conduct statistical analysis with StatsBase.jl and Distributions.jl
  • Build your data science models
  • Find out how to visualize your data with Gadfly
  • Explore big data concepts in Julia
About

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.

Features
  • Follow a practical approach to learn Julia programming the easy way
  • Get an extensive coverage of Julia’s packages for statistical analysis
  • This recipe-based approach will help you get familiar with the key concepts in Julia
Page Count 172
Course Length 5 hours 9 minutes
ISBN 9781785882012
Date Of Publication 29 Sep 2016

Authors

Jalem Raj Rohit

Jalem Raj Rohit is an IIT Jodhpur graduate with a keen interest in recommender systems, machine learning, and serverless and distributed systems. Raj currently works as a senior consultant—data science—and NLP at Episource, before which he worked at Zomato and Kayako. He contributes to open source projects in Python, Go, and Julia. He also speaks at tech conferences about serverless engineering and machine learning.

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