Why the Language You Use in Data Science Is Not All That Important

People like to argue, especially about things they can reason with. However, just because you can justify that your view has merit, giving some practical examples or through logical reasoning, this doesn't make alternative views invalid. If there are several programming languages in data science, perhaps an oversimplification like “X is the best language for data science because Y” doesn't hold much water. Let’s examine why.

Although it is possible to rule out certain languages (e.g. Assembly or C) as optimal for data science, this doesn't mean that the problem has a clear-cut solution. Also, the assumption that a single programming language can cover all the use cases of a data science professional is a quite unjustifiable one. Some data scientists use two or three programming languages, sometimes in combination, getting the best of each, for optimal overall performance.

Also, data science is all about solving a business problem in a scientific manner. Just because say Dr. Smith prefers to use language X over Y, it doesn't mean that you have to follow her example. Maybe she has used language X during her PhD and didn't have time to learn another language, or she attained mastery of that language, so she feels more comfortable doing her data science work with that. She may be a successful data scientist but following her programming habits won’t make you a great data scientist necessarily.

Moreover, with new languages and new packages in the existing languages coming about all the time, which language is best is like the best performing basketball team. Definitely not something particularly stable! Besides, it’s often the case that a particular project may requite special handling, so what is a top-performer now, may not be the best option for that particular case.

In addition, the almost religious attitude towards programming languages that many people have (not just data scientists) is by itself problematic. If a potential employer sees you arguing about how your language of choice is the best and that you are not open to consider alternatives, he may not be so eager to hire you, since this kind of attitude creates disharmony and difficulty in collaboration among the members of a team. Besides, in most companies nowadays, they rarely ask for a specific language in the candidate requirements. As long as you can do the task that’s required of you, they don’t really care much what your programming background is. Of course companies that have already invested in a particular language and have all their code in that language may not be so flexible, but that shouldn't be the principle factor in your decision about which language you learn.

Finally, when it comes to deep learning, many modern frameworks, like Apache’s MXNet, have APIs for a variety of programming language. So if your A.I. guru friend tries to convince you that you should learn language X because that’s the best deep learning language, take that suggestion with a pinch of salt!

The important thing is for whatever language you decide to learn for data science, you make sure that you learn it well. Familiarize yourself with its packages, use it to solve various problems, and learn the best strategies for debugging code written in that language. If you do that, you can still make good use of it for your data science projects, even if the majority of people prefer this or the other language instead.