![]() Primarily designed for statistical computing, R offers an excellent set of high-quality packages for statistical data collection and visualization. ![]() Often, the choice of one programming language is subjective, but below we will try to greet the strengths of each of the three described languages. INFOGRAPHICĮach of these languages is suitable for a specific type of tasks, besides each developer chooses the most convenient tool for himself. Although there are many specific fields of application of different data science packages, we want to focus on those that are perfectly suited for machine learning, visualization, mathematics and engineering, data manipulation and analysis, and reproducible research. This selection shows how languages relate to each other as well as which libraries have similar application area. ![]() In this post, we have prepared an infographic which shows top 20 libraries in each programming language which are beneficial to data scientists and data engineers work. So while there are many languages that can be useful for a data scientist, these three remain the most popular and are developed to implement data science and machine learning solutions. Recently we have prepared a series of articles where we gave an overview of the top most useful libraries in Python, R, and Scala based on our experience. ![]() Therefore data scientists should be aware of what are the best solutions for the particular tasks. The area of data science use cases and influence is continuously expanding, and the toolkit to implement these applications is growing fast. Data science is a promising and exciting field, developing rapidly. ![]()
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