What are the advantages and disadvantages ?
First of all, there is no programming language better than another one. However, the fact that Python is more widely used in academia and therefore predominant later in industry is a considerable advantage if you want to train yourself in Data Science and Machine Learning.
Depending on the field you work in, there are real differences between Python and R.
Lets see the different points that you need to know in order to choose the programming language which fits with your needs:
- Machine Learning: It consists of two main steps: the model design and the prediction phase. The first one is done upstream by quite heavy calculations while the predictions are made in real time. Nevertheless, the choice of Python or R for each of these steps does not influence a user’s execution time.
- Libraries: Both languages have a multitude of libraries adapted to Machine Learning. More than 5000 libraries are available in R in a wide variety of domains. Python has fewer but some are almost exhaustive such as Pandas, NumPy, Scikit Learn, SciPy or Matplotlib.
- Development: Many people find Python quite easy to learn, as High-Level type it is closer to the human language, while R requires more effort to learn because of its rather unclear syntax. However, they both have good development environments such as Spyder for Python or Rstudio for R.
- Speed: Initially, R and Python are two relatively slow languages. The emphasis on ease of programming in Python makes this language necessarily slower than low-level languages like R. In addition, R has recently updated its computationally intensive operating systems making it much faster. To make up for this delay, some Python libraries interface with the C language.
- Visualization: In Data Science, data visualization is essential in order to analyze the results, so it is an important factor in choosing your language. Python is not to be complained about in this domain but the R ggplot2 package gives a big advantage over Python and its Matplotlib package for its diversity and ease of use.
Despite these pros and cons, the DataScientest training prefers Python for its ease of learning and its predominance in the professional world.
