# 5 Free Julia Books For Data Science

Discover the full potential of the Julia programming language for data analysis and modeling with a comprehensive guide that covers everything from its syntax to advanced techniques.

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You might be hearing a lot about Julia and why it is the future of data science, but you don’t know where to start. I have the perfect solution for you. You can start by checking out the awesome list of free books on the Julia programming language that will prepare you for software engineering and data science-related tasks.

You will learn about Julia libraries for data frames, data visualization, machine learning, and creating and running a web service. Moreover, you will learn object-oriented programming, metaprogramming, and parallel computing.

# 1. Think Julia

Think Julia: How to Think Like a Computer Scientist by Ben Lauwens and Allen B. Downey is a book for anyone who wants to learn Julia, from beginners to experienced programmers.

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The book begins with an introduction to Julia, explaining what it is, how it works, and why it is different from other programming languages. The authors then provide a brief history of Julia and discuss its current development and future potential.

The book has simple examples to illustrate each concept and provides exercises at the end of each chapter to reinforce learning.

The book also covers more advanced topics, including arrays, matrices, strings, and input/output. Moreover, It covers object-oriented programming, metaprogramming, and parallel computing.

# 2. Julia as a Second Language

Julia as a Second Language by Erik Engheim is another beginner-friendly guide for programmers who are already familiar with another programming language and want to learn Julia as a second language.

The book starts with an introduction to Julia and its history, followed by a discussion of its features, advantages, and unique selling points. It compares Julia to other popular programming languages, such as Python, MATLAB, and R.

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It also covers object-oriented, functional programming, and advanced topics, such as arrays, matrices, strings, and input/output. The author also covers metaprogramming, parallel computing, and how to work with external libraries and packages.

# 3. Statistics with Julia

Statistics with Julia by Hayden Klok and Yoni Nazarathy is a comprehensive guide to statistical analysis using the Julia programming language. This is for anyone who wants to learn how to perform statistical analysis and modeling using Julia.

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The book covers basic syntax, followed by an introduction to the fundamentals of statistics, including probability theory, descriptive statistics, statistical inference, statistical methods, and models, including linear regression, logistic regression, clustering, and time series analysis.

It also covers how to work with external data sources, including CSV files and databases, and how to visualize data using Julia's plotting libraries.

You will be learning about reproducibility in statistical analysis and how to organize and document code and data.

# 4. Julia Data Science

Julia Data Science by Storopoli, Huijzer, and Alonso is an open-source and open-access book on how to use Julia programming for data science-related tasks.

The book begins by explaining what is data science and software engineering and then explains why you should spend time learning the language that is never mentioned in the job posting.

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The book starts by introducing the Julia syntax, data structures, file systems, and standard libraries. Then, it moves to topics that are important for data analysis and modeling using data frames and data visualization libraries.

The book is well-written, easy to follow, and provides a comprehensive introduction to data analysis and modeling libraries for beginners or experienced data scientists.

# 5. Julia for Data Analysis

Julia for Data Analysis by Bogumi? Kami?ski is a practical guide to data analysis using Julia for experienced data analysts, programmers, and beginners who want to learn how to use Julia for performing effective data analysis and reporting.

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The book is divided into two parts.

The first part is about the basics of Julia programming, where you will learn syntax, loop, and data structures. Next, you will learn elements of the Julia language that are important when creating scalable projects.

The second part is about the toolbox for data analysis. In this part, you will learn to handle data using a dataframe, clean, manipulate, and transform data for analysis, and create a web service for sharing data analysis results.

**Abid Ali Awan** (@1abidaliawan) is a certified data scientist professional who loves building machine learning models. Currently, he is focusing on content creation and writing technical blogs on machine learning and data science technologies. Abid holds a Master's degree in Technology Management and a bachelor's degree in Telecommunication Engineering. His vision is to build an AI product using a graph neural network for students struggling with mental illness.