Mastering Julia

Develop your analytical and programming skills further in Julia to solve complex data processing problems

Mastering Julia

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Malcolm Sherrington

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Develop your analytical and programming skills further in Julia to solve complex data processing problems
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RRP $54.99
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Book Details

ISBN 139781783553310
Paperback410 pages

Book Description

Julia is a well-constructed programming language with fast execution speed, eliminating the classic problem of performing analysis in one language and translating it for performance into a second. This book will help you develop and enhance your programming skills in Julia to solve real-world automation challenges.

This book starts off with a refresher on installing and running Julia on different platforms. Next, you will compare the different ways of working with Julia and explore Julia's key features in-depth by looking at design and build. You will see how data works using simple statistics and analytics, and discover Julia's speed, its real strength, which makes it particularly useful in highly intensive computing tasks and observe how Julia can cooperate with external processes in order to enhance graphics and data visualization. Finally, you will look into meta-programming and learn how it adds great power to the language and establish networking and distributed computing with Julia.

Table of Contents

Chapter 1: The Julia Environment
Introduction
Getting started
A quick look at some Julia
Package management
What makes Julia special
Summary
Chapter 2: Developing in Julia
Integers, bits, bytes, and bools
Arrays
Char and strings
Real, complex, and rational numbers
Composite types
More about matrices
Data arrays and data frames
Dictionaries, sets, and others
Summary
Chapter 3: Types and Dispatch
Functions
Julia's type system
Enumerations (revisited)
Multiple dispatch
Summary
Chapter 4: Interoperability
Interfacing with other programming environments
Metaprogramming
Tasks
Executing commands
Summary
Chapter 5: Working with Data
Basic I/O
Structured datasets
DataFrames and RDatasets
Statistics
Selected topics
Summary
Chapter 6: Scientific Programming
Linear algebra
Signal processing
Differential equations
Optimization problems
Stochastic problems
Summary
Chapter 7: Graphics
Basic graphics in Julia
Data visualization
Graphic engines
Using the Web
Raster graphics
Summary
Chapter 8: Databases
A basic view of databases
Relational databases
NoSQL datastores
RESTful interfacing
Summary
Chapter 9: Networking
Sockets and servers
Working with the Web
Messaging
Cloud services
Summary
Chapter 10: Working with Julia
Under the hood
Performance tips
Developing a package
Community groups
What's missing?
Summary

What You Will Learn

  • Install and build Julia and configure it with your environment
  • Build a data science project through the entire cycle of ETL, analytics, and data visualization
  • Understand the type system and principles of multiple dispatch for a better coding experience in Julia
  • Interact with data files and data frames to study simple statistics and analytics
  • Display graphics and visualizations to carry out modeling and simulation in Julia
  • Use Julia to interact with SQL and NoSQL databases
  • Work with distributed systems on the Web and in the cloud
  • Develop your own packages and contribute to the Julia Community

Authors

Table of Contents

Chapter 1: The Julia Environment
Introduction
Getting started
A quick look at some Julia
Package management
What makes Julia special
Summary
Chapter 2: Developing in Julia
Integers, bits, bytes, and bools
Arrays
Char and strings
Real, complex, and rational numbers
Composite types
More about matrices
Data arrays and data frames
Dictionaries, sets, and others
Summary
Chapter 3: Types and Dispatch
Functions
Julia's type system
Enumerations (revisited)
Multiple dispatch
Summary
Chapter 4: Interoperability
Interfacing with other programming environments
Metaprogramming
Tasks
Executing commands
Summary
Chapter 5: Working with Data
Basic I/O
Structured datasets
DataFrames and RDatasets
Statistics
Selected topics
Summary
Chapter 6: Scientific Programming
Linear algebra
Signal processing
Differential equations
Optimization problems
Stochastic problems
Summary
Chapter 7: Graphics
Basic graphics in Julia
Data visualization
Graphic engines
Using the Web
Raster graphics
Summary
Chapter 8: Databases
A basic view of databases
Relational databases
NoSQL datastores
RESTful interfacing
Summary
Chapter 9: Networking
Sockets and servers
Working with the Web
Messaging
Cloud services
Summary
Chapter 10: Working with Julia
Under the hood
Performance tips
Developing a package
Community groups
What's missing?
Summary

Book Details

ISBN 139781783553310
Paperback410 pages
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