This language is mainly used by developers or programmers to perform data analysis as well as to utilize machine learning in production environments. Even though it requires computer programming skills, Python is actually reasonably easy to learn due to its readable syntax. In turn, Python is a more general-purpose language with a significant focus on production and deployment. These types of visualizations facilitate data interpretation and identification of patterns, outliers (or anomalies), and trends in data sets. Overall, R also attracts attention for its data visualization, regarding graphs, charts, plots, etc. Moreover, researchers often prefer to use R since it provides plots and graphics that can immediately be used for publication, considering it contains the correct mathematical formulae and notation. In fact, this reason is also what makes it so popular among researchers, engineers, statisticians, and other professionals without computer programming skills. It relies heavily on statistical models and does not require many lines of coding to show off its analytics abilities. As mentioned, R's primary purpose is statistical analysis and data visualization. The purpose is probably the core difference between these two languages. In sum, as a general-purpose language, it is pretty much possible to use Python to do everything! R vs Python: key differences Purpose
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Additionally, this multi-paradigm language also provides a vast and impressive number of libraries and tools to handle software development, artificial intelligence (AI), and machine learning (ML). However, Python's popularity does not come exclusively from data science. Close to 70% of ML developers and data scientists report using Python." (SlashData) "The rise of data science and machine learning (ML) is a clear factor in Python's popularity. According to " Developer Economics: State of the Developer Nation 20th edition" ( 2021, SlashData), Python has strongly been conquering Data Scientists' attention as the prime language in the field. It was initially introduced in 1991, designed by Guido von Rossum. Python is one of the world's most popular programming languages. It is a complete ecosystem for data analysis, with an incredible variety of packages and libraries available. Unlike Python (as we will explain later), the R language was developed exclusively to analyze data and to develop applications and software solutions that are able to execute statistical analyses and data mining. Since then, it has come a long way and conquered an admirable reputation for its ability to handle data science, visualization projects, and statistics. This software was first introduced in 1993, designed by Ross Ihaka and Robert Gentleman. In fact, according to TIOBE Index 2021, R currently occupies de 13th place as the most popular programming languages in the world.
R is a programming language that is becoming increasingly popular in the world of data science. ➤ Artificial Intelligence and Machine Learning
Last but not least, we also explain which one to learn and why.
Further, it describes their key differences regarding their abilities to handle data and machine learning applications. This article introduces the importance of both languages for Data Science.
Despite their competitive popularity, R and Python are actually quite different, and one might be more suitable than the other for particular situations. They are both open-source and excel at data analysis. R and Python are the most popular Data Science languages.
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