Comparison of top data science libraries for Python, R and Scala [Infographic]


Originally Posted Here

In this post, Igor Bobriakov team has prepared an infographic which shows top 20 libraries in each programming language which are beneficial to data scientists and data engineers work. This selection shows how languages relate to each other as well as which libraries have similar application area. Although there are many specific fields of application of different data science packages, Igor Bobriakov wanted to focus on those that are perfectly suited for machine learning, visualization, mathematics and engineering, data manipulation and analysis, and reproducible research.

You can see the fields of implementation in different colors on the infographic below.

  • Machine learning packages take care of the building and implementing the top machine learning algorithms, creating workflows, and in general helping to solve machine learning problems. They provide the primary toolkit for different classification, regression, and other problems.
  • As an integral part of data science, data manipulation and analysis fieldrepresents libraries that carry out data scraping, ingestion, cleaning, pre-processing and other operations that allow you to “play with the data” and as a result to perform the analysis itself.
  • With the help of visualization packages, you can display the data visually which is necessary for better understanding and interpreting the data. These packages contain numerous visualization charts as well as different options for representation.
  • Libraries for mathematics and engineering provide the abilities to store numerical data in a convenient form and perform complicated and advanced mathematical operations and scientific computations. Also, these packages are used to process more complexly interpreted data such as text and content.
  • Finally, packages for reproducible research implement the idea of creating documents which combine code, data, and content. Basically, with their help, you can produce a new work out of your project that can immediately be published.

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