The pros and cons of using the web’s #1 language for data science
If you have been following the tech landscape in recent years, you have probably noticed at least two things.
You’ve probably also noticed there is a lot of excitement surrounding the field of data science, especially machine learning. Recent advances in theory and technology have made this once-esoteric field much more accessible to developers.
Most data scientists work with some combination of Python, R and SQL. If you are new to the field, these are the languages you should master first.
Data scientists may also specialize in another language such as Scala, or Java. There are many reasons why these languages are so popular.
Let’s start by looking at some important objections, then review some arguments in favour.
- Opportunity cost — Perhaps the main reason data scientists tend not to learn many languages beyond Python and R is due to ‘opportunity cost’. Every hour spent learning another language is an hour that could have been invested in learning a new Python framework, or another R library. While these languages dominate the data science job market, there is more incentive to learn them. And because data science is such a fast-moving field, there’s always something new to learn.
- Product integration — More and more companies are using web technologies with a Node-based stack to build their core product or service. If your role as a data scientist requires you to work closely with product developers, then it cannot hurt to ‘speak’ the same language.
As a first language, the best advice is to learn one of either Python or R. You should also become comfortable using some database language, such as SQL or MongoDB.
However, once you are familiar with the basics, you may want to specialize further. Perhaps you want to learn Apache Spark for working with giant, distributed datasets. Or maybe you’d prefer learn another language such as Scala, or MATLAB or Julia.
Ultimately, the decision is both practical and personal. It depends on which aspects of data science you find most interesting, and what career opportunities excite you most.