The different tasks that data scientists may hold are very diverse, but no matter what niche a data scientist fills in their line of work, being specialized in certain technical areas of software development is extremely important. The following skills are some of the most important techniques that a data scientist will need to have in order to perform software development properly.
Statistics knowledge
Before anything else, a data scientist can benefit very much from having a strong foundation in statistics. Even without being a professional statistician by trade, understanding what the relevance of a P-value is can go a long way toward being better capable of articulating the meaning of any data set. Statistics isn’t just a matter of being able to code better, but it is important for being capable of explaining the data clearly and accurately.
Localization
Software localization services are necessary for ensuring that software is capable of crossing the barriers of language and culture for all users around the world. It’s one thing for a piece of software to be advanced on a technological level, but it’s another matter entirely for this technology to be accessible to the globe as a whole.
Software that has been properly localized has a much greater target audience reach than any type of software that isn’t, creating a much higher potential for impact and relevance. Because software localization is different than typical document translation, the specialization of the data scientist is imperative for doing it properly.
Documentation
Data scientist, naturally, will need to be well-versed in the art of documentation. The work of a data scientist is often going to be perused by a high number of people who rely on it, which means that all of the comments left by the data scientist need to be relevant and full of value.
Data scientists need to be capable of more than just creating and translating code; they must also be perfectly capable of breaking it down into an abstracted form that anyone on the team can understand if they need to. Essentially, the organizational skills that a data scientist has can be compared to the outlining of an essay before it goes into its final form. The line of code should be capable of being explained as concisely and clearly as possible, without needing to go into extreme detail to get the gist.
Well-rounded coding skills
Data scientist are going to want to be well-versed in a range of coding that gives them a well-rounded ability to approach all different projects. SQL database may not be one of the most common mediums that data scientists are required to work with, though it certainly doesn’t hurt for a data scientist to be capable of doing so if need be.
Generally speaking, a data scientist will be more likely to work in Python. Python is the most common of all different coding languages, and so it is naturally important for the majority of data scientists to be prepared for working with it.
In addition to Python, there is also the Hadoop platform. Much like SQL, Hadoop platform won’t always be entirely necessitated for the majority of projects; however, there are certain scenarios in which it is heavily preferred over the alternatives.
Code cleaning and refinement for reusability
The code that data scientist write should be easily reusable. Many different data scientists get into programming for different personal reasons, but what they all tend to have in common is the fact that that their coding can be reused without needing to go through a long step-by-step process to manually re-create it. The principal is known as Don’t Repeat Yourself (DRY), and with it, any line of code can be reused without being hampered by its complexity or length. Data scientist will oftentimes go back to previous lines of code in order to see if they can refine it, which is the process of re-factoring.
Conclusion
Being capable of coding, interpreting, and localizing all kinds of data on a diverse number of platforms is essential for the well-prepared data scientist. In addition to these crucial software development abilities, a data scientist should also be a comfortable communicator in general; this will ensure that the nature of the data can always be shared with those on the team who aren’t as technologically savvy, but still need to know as much possible.
