Book Image

Learning Julia

By : Anshul Joshi, Rahul Lakhanpal
Book Image

Learning Julia

By: Anshul Joshi, Rahul Lakhanpal

Overview of this book

Julia is a highly appropriate language for scientific computing, but it comes with all the required capabilities of a general-purpose language. It allows us to achieve C/Fortran-like performance while maintaining the concise syntax of a scripting language such as Python. It is perfect for building high-performance and concurrent applications. From the basics of its syntax to learning built-in object types, this book covers it all. This book shows you how to write effective functions, reduce code redundancies, and improve code reuse. It will be helpful for new programmers who are starting out with Julia to explore its wide and ever-growing package ecosystem and also for experienced developers/statisticians/data scientists who want to add Julia to their skill-set. The book presents the fundamentals of programming in Julia and in-depth informative examples, using a step-by-step approach. You will be taken through concepts and examples such as doing simple mathematical operations, creating loops, metaprogramming, functions, collections, multiple dispatch, and so on. By the end of the book, you will be able to apply your skills in Julia to create and explore applications of any domain.
Table of Contents (17 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
8
Data Visualization and Graphics

Statistics


One of the strongest points of Julia has been its powerful support for statistical and mathematical functions. The Julia community has been very active in identifying the areas that can be covered using packages made purely in Julia, and hence we have now a group that deals purely in packages of Julia for stats. It's called JuliaStats, and can be found on GitHub at https://github.com/JuliaStats.

Some of the notable packages listed on the Julia stats page are as follows:

  • PDMats.jl: Uniform interface for positive definite matrices of various structures
  • Klara.jl: MCMC inference in Julia
  • StatsBase.jl: Basic statistics for Julia
  • HypothesisTests.jl: Hypothesis tests for Julia
  • ConjugatePriors.jl: A Julia package to support conjugate prior distributions
  • PGM.jl: A Julia framework for probabilistic graphical models
  • TimeSeries.jl: Time series toolkit for Julia
  • StatsModels.jl: Specifying, fitting, and evaluating statistical models in Julia
  • Distributions.jl: A Julia package for probability distributions...