Best Julia Books 2024

Julia is a high-level dynamic programming language designed for use in high-performance numerical analysis. Julia was first released in 2012 by Jeff Bezanson, Alan Edelman, Stefan Karpinski and Viral B. Shah. Some distinctive features include a sophisticated compiler, distributed parallel execution, numerical accuracy, extensive mathematical function library, etc.

Practical Julia: A Hands-On Introduction for Scientific Minds

by Lee Phillips. Dive in with a thorough guide to Julia’s syntax, data types, and best practices, then transition to craft solutions for challenges in physics, statistics, biology, mathematics, scientific machine learning, and more. Whether you’re solving computational problems, visualizing data, writing simulations, or developing specialized tools, Practical Julia will show you how. As you work through the book, you’ll:

• Use comprehensions and generators, higher-level functions, array initialization and manipulation, and perform operations on Unicode text
• Create new syntax and generate code with metaprogramming and macros, and control the error system to manipulate program execution
• Visualize everything from mathematical constructs and experimental designs to algorithm flowcharts
• Elevate performance using Julia’s unique type system with multiple dispatch
• Delve into scientific packages tailored for diverse fields like fluid dynamics, agent-based modeling, and image processing

Julia as a Second Language: General purpose programming with a taste of data science

by Erik Engheim introduces Julia to readers with a beginning-level knowledge of another language like Python or JavaScript. You’ll learn by coding engaging hands-on projects that encourage you to apply what you’re learning immediately. You will:

Data types like numbers, strings, arrays, and dictionaries
Immediate feedback with Julia’s read-evaluate-print-loop (REPL)
Simplify code interactions with multiple dispatch
Sharing code using modules and packages
Object-oriented and functional programming styles

Think Julia: How to Think Like a Computer Scientist

by Ben Lauwens and Allen B. Downey uses Julia 1.0 to guide you through programming step by step, starting with programming basics before moving on to more advanced features like creating new types and sending multiples. Designed from the ground up for high performance, Julia is a versatile language ideal not only for numerical analysis and computer science, but also for web programming and scripting. Through the exercises in each chapter, you will test programming concepts as you learn them. This is one of the best books to learn is perfect for high school or college students, as well as self-taught and professionals who need to learn the basics of programming.

Using Julia for Introductory Econometrics

by Florian Heiss and Daniel Brunner. is a comprehensive guide that combines the basics of econometrics with the Julia programming language. The book is designed for students of introductory econometrics who are familiar with the textbook “Introductory Econometrics” by Wooldridge (2019). It provides a seamless transition from theory to practice by implementing econometric methods in Julia. The book’s structure is organized around the chapters of Wooldridge’s textbook, with each chapter covering the same material but using Julia to implement the methods. This approach allows readers to easily replicate examples and work on assignments using Julia, which is essential for understanding econometric models and methods.

Julia Programming Projects

by Adrian Salceanu illustrates how to analyze the Iris dataset using DataFrames. You will explore the functions and type system, methods, and multiple shipping while building a web scraper and web application. Next, you will dive into machine learning, where you will create a book recommendation system. You will also see how to apply unsupervised machine learning to perform clustering on the San Francisco enterprise database. After meta-programming, the final chapters will cover dates and times, time series analysis, visualization, and forecasting. You will:

Leverage Julia’s strengths, best packages, and best IDE options
Analyze and manipulate data sets with Julia and DataFrames
Write complex code while creating real Julia applications
Develop and run a web application using Julia and the HTTP package
Build a recommendation system using supervised machine learning
Perform exploratory data analysis
Apply unsupervised machine learning algorithms
Perform analysis, visualization and forecasting of time series data.

Julia High Performance

by Avik Sengupta begins with an explanation of how Julia uses type information to achieve its performance goals, as well as how to leverage multiple dispatches to assist the compiler in producing high-performance machine code. Following that, you’ll learn how to analyze Julia programs and spot problems with time and memory usage. We show you how to accurately use Julia’s typing features to develop high-performance code, as well as how the Julia compiler leverages type information to generate fast machine code.

Hands-On Design Patterns and Best Practices with Julia

by Tom Kwong will demonstrate how to take advantage of design patterns with real-world applications. Starting with an overview of design patterns and application design best practices, you will learn about some of Julia’s more fundamental features, such as modules, data types, functions / interfaces, and meta-programming. You will then become familiar with modern Julia design patterns to create large-scale applications with an emphasis on performance, reusability, robustness, and maintainability. The best book to learn julia also covers anti-patterns and how to avoid common errors and pitfalls in development of data visualization. You will see how traditional object-oriented models can be implemented differently and more efficiently in Julia. Finally, you will explore various use cases and examples, such as how expert Julia developers use modern Julia design patterns in their open source Julia programs.

The Little Book of Julia Algorithms

by Ahan Sengupta, William Lau attempts to teach the Julia programming language while explaining essential computer science topics. Julia is a good high-level language for beginners because it is quick and productive. Programming has a high learning curve, and this book seeks to make it easier by encouraging practice and progressively introducing more complicated topics. The book includes 50 programming tasks to inspire readers to create their own programs. All of the obstacles have solutions at the end of the book. This book will prepare readers for more advanced programming in college math, science, or computer science classes by making them comfortable with utilizing computers to solve any problem. The reader should be more comfortable with: •Loops and conditionals after completing the tasks in this book. •Simple Statistics and Plotting •Structuring code using functions •Reading and writing files •Installing and using packages •Sorting and searching

Julia 1.0 Programming

by Ivo Balbaert starts by learning how to set up a running Julia platform, before exploring its different built-in types. With practical examples, this book guides you through two important types of collections: tables and matrices. In addition, you will learn how type conversions and promotions work. During the course of the julia language books, you will be introduced to the concepts of homo-iconicity and metaprogramming in Julia. You will learn:

Configure your Julia environment for high productivity
Create your own types to extend the built-in type system
Visualize your data in Julia with tracking packages
Explore the use of built-in macros for testing and debugging, among other uses
Apply Julia to solve problems simultaneously
Integrate Julia with other languages ​​like C, Python, and MATLAB

Tanmay Teaches Julia for Beginners

by Tanmay Bakshi shows step-by-step how to create custom programs using Julia, the intuitive open source programming language. Written by 15-year-old tech phenom Tanmay Bakshi, the best julia programming language books is presented in an accessible style that makes learning easy and enjoyable. The great book clearly explains Julia’s programming basics and reviews cutting edge machine learning apps. You will learn how to interface his Julia applications with code written in Python.

You will learn to:

• Install and configure your Julia environment
• Get started writing your own Julia applications
• Define variables and use them in your programs
• Use conditions, iterations, for loops, and while loops
• Create, browse, and modify tables
• Create an application to manage the things you lend and charge to your friends
• Create and use dictionaries
• Simplify the maintenance of your code thanks to the functions
• Apply functions in arrays and use functions recursively and generically

The Julia Language Handbook

by George Root is updated from Julia v1.02 and all the examples, of which there are dozens, have been tested and all work. You will learn how to install and use the Julia REPL mode and the Jupyter Notebook mode to create and test your code. Other topics include: Data types Functions and packages Tuples Data matrices Data frames Data structures Flow control Loops and iteration Input / Output: formatted printing: writing and reading data files Line and scatter graphs Other types of graphs Random Numbers Optimizing with Optim and JuMP. This is one of the best julia programming books.

Julia Programming for Operations Research

The Julia Language is as fast as C, as convenient as MATLAB, and as general as Python with a flexible algebraic modeling language for mathematical optimization problems. With the great support from Julia developers, especially the developers of the JuMP—Julia for Mathematical Programming—package, Julia makes a perfect tool for students and professionals in operations research and related areas such as industrial engineering, management science, transportation engineering, economics, and regional science.

Statistics with Julia

by Yoni Nazarathy and Hayden Klok uses the Julia language to guide the reader through an exploration of the fundamental concepts of probability and statistics, all with a view of mastering machine learning, data science, and artificial intelligence.