Python

The practical application of Machine Learning boils down to computations. The primary method of running these computations before 2000s were using commercial statistical or machine learning packages, and there were not many. As the Machine Learning has grown and ML community has grown, the alternatives to running ML computations have emerged. Though commercial solutions still exist, open sources alternatives are very competitive and commonly used. Python has become de facto language of these open source alternatives. Even though libraries are written in other languages such as C++, the primary means of access to these libraries are through Python APIs.

There are several reasons for this: ease of learning, a massive and growing community of users, an eco-system of libraries, the ease of binding with C, C++. Though a perpetual commitment to a certain piece of technology should be avoided by Data Scientists, it is safe to say that Python will be here for a while, and every Data Scientist should be knowledgable about Python programming skills and a set of important Python libraries such as Pandas, NumPy, Matplotlib and Scikit-learn.