Python has the capability of supporting object-oriented, procedural and functional programming. Created in 1991, it was used and implemented in simple systems at the time when machine learning had a niche market. Similar to R, Python also is an open-source programming language deployed for statistical and machine learning models like regression and classification which is employed in many systems. R language runs on the R Studio platform which helps in initiating and executing codes and packages in R. It has the capability of creating some powerful charts and dashboard quality graphs to demonstrate and monitor the monthly sales or profit of a company. Tons of packages are already available and they help in exploratory data analysis, basic data exploration and data representation in the form of graphs. R includes a ton of inbuilt libraries that offer a wide variety of statistical and graphical techniques which include regression analysis, statistical tests, classification models, clustering and time-series analysis. It first came into the picture in August 1993. R is an open-source programming language mostly used by statisticians and data engineers who utilize it to build various algorithms and techniques for statistical modeling and data analysis. These technologies are being constantly deployed for algorithms in machine learning, deep learning, artificial intelligence and much more cutting-edge discoveries that have taken the world and visionaries by surprise. Following the advent of machine learning and data analytics, the focus shifted to technologies such as R, Python, and SAS. Earlier, the IT sector used to place a lot of emphasis on technologies like Java which includes Spring and Hibernate for writing and testing code. The data science community has come a long way and has matured a lot in the last 5 years. Popularity among masses due to higher employment opportunities.R vs Python: Important Differences, Features.Shaking the market with artificial intelligence.Why Do We Use Them and Their Applications?.
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