1 Introduction

Data analysis has become one of the core processes in virtually any professional activity. The collection of data becomes easier and less expensive, so we have ample access to it. The crucial aspect is that it allows us to make better decisions cheaper and faster.

The need for data analysis gave rise to several new professions among which a data scientist usually comes first into mind. A data scientist is a person skilled at collecting data, analyzing it, and producing actionable insights. As with all craftsmen, data scientists need tools that will help them to deliver their products efficiently and reliably.

There are various software tools to help data scientists in doing their job. Some of those solutions use a graphical interface and thus are easy to work with but usually have limitations on how they can be used. The vast array of tasks that needs to be done by a data scientist typically quickly leads to the conclusion that one needs to use some programming language to achieve the required flexibility and expressiveness.

1.1  What is Julia and why is it useful?

1.2  Key features of Julia from data scientist’s perspective

1.2.1  Julia is fast because it is a compiled language

1.2.2  Julia provides a full support for interactive workflows

1.2.3  Julia programs are highly reusable and easy to compose together

1.2.4  Julia has an in-built state-of-the-art package manager

1.2.5  It is easy to integrate existing code with Julia

1.3  The usage scenarios of tools presented in the book

1.4  Julia’s drawbacks

1.5  What data analysis skills will you learn?

1.6  How can be Julia used for data analysis?

1.7  Summary