Digests » 345
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Use the best tool for the job. It seems like solid advice, but there's something to say about keeping things simple. There is a training and maintenance cost that comes with supporting an ever growing number of tools. It may be better advice to use an existing tool that works well, although not perfect, until it hurts. It all depends on your specific case.
This article will provide the reader with a brief overview for a number of different Linux commands. A special emphasis will be placed on explaining how each command can be used in the context of performing data science tasks. The goal will be to convince the reader that each of these commands can be extremely useful, and to allow them to understand what role each command can play when manipulating or analyzing data.
The sad truth is the better we get at writing code, the less fun it becomes. We learn about the SOLID principles, immutability, abstraction, composition, and maintainable code. But when we apply these techniques in a curly bracket language like Java or C#, it doesn't feel right. Pretty much all our code is boilerplate: we constantly repeat patterns and sections of code with little alteration between each class and project. Coding becomes incredibly monotomous, and requires a lot of typing to produce little functionality.
In this article, we'll provide a technical comparison of some of the most historically significant Version Control Systems (or VCS). We will discuss the following six VCS.
In a recent study titled Usage and Attribution of Stack Overflow Code Snippets in GitHub Projects, an answer I wrote almost a decade ago was found to be the most copied snippet on Stack Overflow. Ironically it happens to be buggy.