![]() To find a solution, it is not uncommon for people to search in the language’s official documentation or online community forums. Sometimes you may have difficulty finding the relevant library or package that could help you solve your problem. Since anyone can pick it up in relatively less time, you can say it’s a language for beginners.Īs a data scientist, you are required to solve problems that you haven’t encountered before. Although Python takes inspiration for its syntax from C, unlike C it is uncomplicated. Learning Curves in Python:- Python was designed in 1989 with a philosophy that emphasizes code readability and a vision to make programming easy or simple, the designers of Python clearly succeeded as the language is fairly easy to learn.IDE’s such as RStudio have made R significantly more accessible, but in comparison with Python, R is relatively more difficult to learn. Like Python, much of R’s syntax is based on C, but unlike Python R was not envisioned as a language that anyone could learn and use, as it was specifically initially designed for statisticians and scientists. Learning Curves in R:- It would be wrong to say that R is a difficult language but yes, R is simpler than many languages like C++ or JavaScript.For this reason, it is appropriate to include ease of learning as a metric when comparing the two languages. ![]() Learning a new language can be challenging, especially if it is your first. Many people are looking to get into the data science bandwagon, many of them have little or no programming experience. Kind of true but when the competition is stiff you have to be nitpicky in order to decide which is better. Some would argue that these are not major barriers or can simply be circumvented. Python is lacking in statistical non-linear regression and mixed-effects models. Since you can use these libraries to solve almost any sort of problem for this discussion let’s just look at what you can’t model.These packages will have your back, starting from the pre modeling phase to the post model/optimization phase. The mice package, rpart, party and caret are the most widely used. R has plenty of libraries, approximately 10000 of them. One can build a plethora of models using R. Modeling Libraries in R:- R was developed by statisticians and scientists to perform statistical analysis.One of the biggest reasons why R and Python get so much traction in the data science is because of the models you can easily build with them. Sometimes it’s very hard to do so, data scientists need languages with built-in modeling support. It is rarely or maybe never the case that you as a data scientist need to code the whole algorithm on your own. These sophisticated mathematical methods require robust computation. However, recent developments have tried to make things simpler.ĭata science requires the use of many algorithms. ![]() The library is surely an improvement on matplotlib ‘s archaic style, but it still has the same fundamental problem as creating figures can be very complicated. seaborn builds on top of matplotlib, including more aesthetic graphs and plots. Īlthough matplotlib can make a whole host of graphs and plots, what it lacks is simplicity. It can work well with other Python data science libraries, pandas and numpy. The library is a very powerful visualization tool with all kinds of functionality built-in. The library matplotlib is adapted from MATLAB, it has similar features and styles. The most popular libraries are matplotlib and seaborn. There are plenty of libraries that can be used for plotting and visualizations.
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