Yoni Nazarathy - Statistics with Julia: Fundamentals for Data Science, Machine Learning and Artificial Intelligence

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The journey of this book began at the end of 2016 when preparing a statistics course for The University of Queensland. At the time, the Julia language was already showing itself as a powerful new and applicable tool, even though it was only at version 0.5. For this reason, we chose Julia for use in the course. By exposing students to statistics with Julia early on, they would be able to employ Julia for data science, numerical computation, and machine-learning tasks later in their careers. This choice was not without some resistance from students and colleagues, since back then, as is still now in 2020, in terms of volume, the R-language dominates the world of statistics, in the same way that Python dominates the world of machine learning. So why Julia?

There were three main reasons: performance, simplicity, and flexibility. Julia is quickly becoming a major contending language in the world of data science, statistics, machine learning, artificial intelligence, and general scientific computing. It is easy to use like R, Python, and MATLAB, but due to its type system and just-in-time compilation, it performs computations much more efficiently. This enables it to be fast, not just in terms of runtime, but also in terms of development time. In addition, there are many different Julia packages. These include advanced methods for the data scientist, statistician, or machine-learning practitioner. Hence the language and ecosystem has a broad scope of application.

Our goal in writing this book was to create a resource for understanding the fundamental concepts of statistics needed for mastering machine learning, data science, and artificial intelligence. This is with a view of introducing the reader to Julia as a computational tool. The book also aims to serve as a reference for the data scientist, machine-learning practitioner, bio-statistician, finance professional, or engineer, who has either studied statistics before, or wishes to fill gaps in their understanding. In todays world, such students, professionals, or researchers often use advanced methods and techniques. However, one is often required to take a step back and explore or revisit fundamental concepts. Revisiting these concepts with the aid of a programming language such as Julia immediately makes the concepts concrete.

Now, 4 years since we embarked on this book writing journey, Julia has matured beyond v1.0, and the book has matured along with it. Julia can be easily deployed by anyone who wishes to use it. However, currently many of Julias users are hard-core developers that contribute to the languages standard libraries, and to the extensive package ecosystem that surrounds it. Therefore, much of the Julia material available at present is aimed at other developers rather than end users. This is where our book comes in, as it has been written with the end user in mind.

This book is about statistics, probability, data science, machine learning, and artificial intelligence. By reading it you should be able to gain a basic understanding of the concepts that underpin these fields. However in contrast to books that focus on theory, this book is code example centric. Almost all of the concepts that we introduce are backed by illustrative code examples. Similarly almost all of the figures are generated via the code examples. The code examples have been deliberately written in a simple format, sometimes at the expense of efficiency and generality, but with the advantage of being easily readable. Each of the code examples aims to convey a specific statistical point, while covering Julia programming concepts in parallel. The code examples are reminiscent of examples that a lecturer may use in a lecture to illustrate concepts. The content of the book is written in a manner that does not assume any prior statistical knowledge, and in fact only assumes some basic programming experience and a basic understanding of mathematical notation.