Make your project clearer, more efficient, and more professional

Software development is the process followed by developers and programmers to design, write, document, and test codes. Regardless of what programming language you use or what your target application field is, following the specific guidelines of good software development is essential in building a high-quality, maintainable project.

Data science projects — may be more than other types of software projects — should be built with the mentality of maintainability. That is because, in most data science projects, the data is not constant and is frequently updating. Moreover, it is expected from any data science project to be extendable and to be crash-resistant. It should be _immune _to any mistake in the data.

Because every single part of the code in a data science project is build to fit a specific shape or form of data, if a wring data is given to the code, it might break it down. Of course, you never want your code to break, no matter what data it is fed. Hence, when designing and building the code, there are a few things to keep in mind to make your code more resilient.

There are many guidelines to follow to design and write good, stable code. However, in this article, we will focus on what I think is _the 5 most important _rules — or skills — needed to build a solid data science project.

So, let’s get right to it…

#software-development #programming #data-science #software-engineering