Text editors are specialized programs that enable you to process text files. Although they are relatively generic, many of these text editors focus on script files, such as those used to store Julia and Python code (pretty much every programming language makes use of text files to store the source code of its scripts). So, modern text editors have evolved enough to pinpoint particular keywords in the script files and highlight them accordingly. This highlighting enables the user to understand the script better and develop a script more efficiently. Many text editors today can help pinpoint potential bugs (programming jargon for mistakes or errors), making the whole process of refining a script easier.
In data science work and work related to A.I., text editors are immensely important. They help organize the code, develop it faster and easier, optimize it, and review it. Data science scripts can often get bulky and are often interconnected, meaning that you have to keep track of several script files. Text editors make that more feasible and manageable as a task, while some can provide useful shortcuts to accelerate your workflow. Additionally, some text editors integrate with the programming languages themselves, enabling you to run the code as you develop it while keeping track of your workspace and other useful information. This is what people call an IDE, short for Integrated Development Environment, something essential for any non-trivial data science project.
One of the text editors that shine when it comes to data science work is Atom. This fairly minimalist text editor can handle various programming languages, while it also exhibits several plugins that extend its functionality. It's no wonder that it is so widely used by code-oriented professionals, including data scientists. Like most text editors out there, it's cross-platform and intuitive, while it's highly customizable and easy to learn. It's also useful for viewing text files, though you may want to look into more specialized software for huge files.
Another text editor that gained popularity recently, particularly among Julia users, is Visual Studio Code (VS Code). This text editor is much like Atom but a bit easier and slicker in its use. It has a smoother interface, while its integration of the terminal is seamless. The debug console it features is also a big plus, along with the other options it provides for trouble-shooting your scripts. Lately, it has become the go-to editor for Julia programmers, something interesting considering how vested the Julia community had been to the Atom editor and its Julia-centric version, Juno.
Beyond these two text editors, there are other ones you may want to consider. Sublime Text, for example, is noteworthy, though its full version carries a price tag. In any case, the field of text editors is quite dynamic, so it's good to be on the lookout for newer or newly-revised such software that can facilitate your scripting work.
If you want to practice coding for data science and A.I. projects, there are a few books I’ve worked on that I’d recommend. Namely, the two Julia books I've written, as well as the A.I. for Data Science book I've co-authored, are great resources for data science and A.I. related coding. Check them out when you have a moment. Cheers!
Zacharias Voulgaris, PhD
Passionate data scientist with a foxy approach to technology, particularly related to A.I.