It is really difficult to choose between R and Python for a complete novice in Data Science. Both Python and R are popular programming languages for Data Analysis. The choice depends on the type of data analytical problem you are facing.While R is developed for statistics, Python is popular for being a general purpose language.Both languages has their own pros and cons.
Pros and Cons of R:
- R has a rich visualization functionality.It has many visualization packages such as ggplot2, ggvis, googleVis and rCharts.
- R was developed for statisticians. So it focuses more on statistical problems.
- Statistical models can be more easily written.
- R is comparatively slow.
- R’s learning curve is non-trivial
Pros and Cons of Python:
Python is a general purpose language. It has easy to understand syntax and it is easy to learn.
- It has better performance than R.
- Coding and Debugging is easier.
- It has some rich libraries such as numpy, scipy, pandas, matplotlib, etc.
- Visualizations are complex in python.
R focuses on Visualizations and statistical models.It is more used in academics and research. It has huge number of packages and is great for any data analysis task.If you want to do iterative data analysis tasks on smaller data sets, R would be handy.
Python emphasizes productivity and code reusability. As Python is a fully fledged language, it would be a better choice if your data analysis tasks need to be integrated with database or web apps.If working with engineering environment, Python may be a better choice.