Many of the tools used for data science are built on R, Python, or a combination of both. Those who are investing heavily in analytics are increasing productivity, streamlining decision making, and improving their overall financial performance.

The advantages of R and Python in businessĪccording to MicroStrategy’s 2020 Global State of Enterprise Analytics report, 94% of businesses identified data science and analytics as being a key to their digital transformation. The programming language is frequently used by statisticians, scientists, and researchers for its rich data models and robust analytic tools. The programming language features an extensive library data modeling tools that allow you to create in-depth data visualizations using just a few lines of code. However, R is built specifically for data visualization and statistical analysis. Similar to Python, R is an open-source programming language. Additionally, it features high-level data structures which make it a popular choice for machine learning tasks. The general-purpose, object-oriented programming language is ideal for production and deployment tasks such as data processing, software testing, application development, and scientific computing. Because it’s an open-source language, it’s widely used among both beginner and professional developers and programmers for tasks such as website development, machine learning, data visualization, and more. Python is considered to be one of the most readable coding languages available thanks to its extensive use of white space. From data visualization and analytics to workflow automation and application development, these two codes enable you to complete both everyday and complex business tasks with ease. With both Python and R being open-source languages, the use cases for each are virtually endless. Using R and Python How to use R and Python for data science