Learning Julia

Learn Julia language for data science and data analytics
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Learning Julia

Anshul Joshi, Rahul Lakhanpal

2 customer reviews
Learn Julia language for data science and data analytics
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Book Details

ISBN 139781785883279
Paperback316 pages

Book Description

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

Chapter 1: Understanding Julia's Ecosystem
What makes Julia unique?
Installing Julia
Julia's importance in data science
Using REPL
Using Jupyter Notebook
What is Juno?
Package management
A brief about multiple dispatch
Understanding LLVM and JIT
Summary
References
Chapter 2: Programming Concepts with Julia
Revisiting programming paradigms
Starting with Julia REPL
Variables in Julia
Integers, bits, bytes, and bools
Floating point numbers in Julia
Logical and arithmetic operations in Julia
Understanding arrays, matrices, and multidimensional arrays
Understanding DataFrames
Summary
Chapter 3: Functions in Julia
Creating functions
Function arguments
Anonymous functions
Multiple dispatch
Recursion
Built-in functions
Summary
Chapter 4: Understanding Types and Dispatch
Julia's type system
Type annotations
More on types
The subtypes and supertypes
User-defined and composite data types
Inner constructors
Modules and interfaces
Module file paths
Multiple dispatch explained
Summary
Chapter 5: Working with Control Flow
Conditional and repeated evaluation
Exception handling
Tasks in Julia
Summary
Chapter 6: Interoperability and Metaprogramming
Interacting with operating systems
Calling C and Python!
Expressions and macros
Built-in macros
Type introspection and reflection capabilities
Summary
Chapter 7: Numerical and Scientific Computation with Julia
Working with data
Linear algebra and differential calculus
Statistics
Optimization
Summary
Chapter 8: Data Visualization and Graphics
Basic plots
Vega
Gadfly
Summary
Chapter 9: Connecting with Databases
How to connect with databases?
Relational databases
NoSQL databases
Introduction to REST
Summary
Chapter 10: Julia’s Internals
Under the hood
Performance enhancements
Standard library
LLVM and JIT explained
Parallel computing
TCP sockets and servers
Creating packages
Summary

What You Will Learn

  • Understand Julia's ecosystem and create simple programs
  • Master the type system and create your own types in Julia
  • Understand Julia's type system, annotations, and conversions
  • Define functions and understand meta-programming and multiple dispatch
  • Create graphics and data visualizations using Julia
  • Build programs capable of networking and parallel computation
  • Develop real-world applications and use connections for RDBMS and NoSQL
  • Learn to interact with other programming languages–C and Python—using Julia

Authors

Table of Contents

Chapter 1: Understanding Julia's Ecosystem
What makes Julia unique?
Installing Julia
Julia's importance in data science
Using REPL
Using Jupyter Notebook
What is Juno?
Package management
A brief about multiple dispatch
Understanding LLVM and JIT
Summary
References
Chapter 2: Programming Concepts with Julia
Revisiting programming paradigms
Starting with Julia REPL
Variables in Julia
Integers, bits, bytes, and bools
Floating point numbers in Julia
Logical and arithmetic operations in Julia
Understanding arrays, matrices, and multidimensional arrays
Understanding DataFrames
Summary
Chapter 3: Functions in Julia
Creating functions
Function arguments
Anonymous functions
Multiple dispatch
Recursion
Built-in functions
Summary
Chapter 4: Understanding Types and Dispatch
Julia's type system
Type annotations
More on types
The subtypes and supertypes
User-defined and composite data types
Inner constructors
Modules and interfaces
Module file paths
Multiple dispatch explained
Summary
Chapter 5: Working with Control Flow
Conditional and repeated evaluation
Exception handling
Tasks in Julia
Summary
Chapter 6: Interoperability and Metaprogramming
Interacting with operating systems
Calling C and Python!
Expressions and macros
Built-in macros
Type introspection and reflection capabilities
Summary
Chapter 7: Numerical and Scientific Computation with Julia
Working with data
Linear algebra and differential calculus
Statistics
Optimization
Summary
Chapter 8: Data Visualization and Graphics
Basic plots
Vega
Gadfly
Summary
Chapter 9: Connecting with Databases
How to connect with databases?
Relational databases
NoSQL databases
Introduction to REST
Summary
Chapter 10: Julia’s Internals
Under the hood
Performance enhancements
Standard library
LLVM and JIT explained
Parallel computing
TCP sockets and servers
Creating packages
Summary

Book Details

ISBN 139781785883279
Paperback316 pages
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From 2 reviews

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