Both R and Python are open-source programming languages that are frequently used for statistical analysis and data visualization. They also enjoy the highest popularity among language models when it comes to deep learning. Which one is better and what are the key differences between R and Python?
R was designed specifically for statistical analysis and data visualization. Python on the other hand has a much broader range of applications, including web development, machine learning, artificial intelligence, automation as well as data analysis.
R’s syntax is specifically optimized for statistical analysis. Python’s syntax is again more general, allowing users to efficiently solve a variety of tasks.
R’s specific applications cause it to be more functionally oriented. In contrast, Python is capable of both functional as well as object-oriented programming.
R is well known for its library of packages specifically developed for statistical analysis and data visualization. While Python also has packages for these tasks, it has a vast array of packages for different purposes due to its broad applicability.
Python is faster than R and can handle larger datasets as well as more complex tasks.
Our Head of Data Science Dr. Patrick Vetter gave his opinion on the comparison between R vs. Python for data science:
“We don’t care about the programming language used – in the end, it’s the data scientist that matters”.
Curious how we used R or Python for data science projects? Get a general idea with these two use cases:
–> Graph Database Verification and Optimization
–> Detection of Bark Beetle Infestation with ArcGIS Pro
Unsure, whether you should use R vs Python for Machine and Deep Learning? Still uncertain whether R vs Python is better for data analysis? Get in contact with us and let us help you work with big data:
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