R and Python are both open source programming languages which are extensively used in the fields of Data Sciences. It comes as no surprise why someone looking for a career in the same might want to gain expertise in either or preferably both of these tools to be able to find their niche in the rapidly expanding market of data science and analytics. But how should a beginner go about when aspiring to learn them both?
Both Python and R are significantly discrete beasts in their own right and learning them simultaneously might not be the most clever idea as it can end up confusing the learner a great deal.
Mastering one before moving on to the other is the more advisable way to go. And the decision to pick one over the other to start with first might not be the same for everyone. It will depend on two factors majorly-The experience background of the aspirant and the goal they intend to achieve. It helps when there is a clear vision of what they plan to do or in what way they aspire to implement the knowledge. Resources, experts and fellow workers they wish to collaborate with is another parameter that has to be factored in while making the decision.
At the end of the day, a skilled Data Scientist is an amalgamation of a programmer and a statistician.
R is primarily a functional oriented programming language. If as aspirant comes from the academic fields of statistics or data mining, starting with R would be a good move. Not only would it be familiar territory to begin with but it would also aid speed up the stiff learning curve. Opting to begin with R is also a valid thought when the ultimate goal has more of statistical computation than machine learning. Also when one is working with massive chunks of messy data or requires high quality graphics, knowledge of R comes in very handy before one learns the fundamentals of Python.
Python, unlike R, is an object oriented programming language, which makes it a pretty easy task to pick up for someone who has had any experience or knowledge in object oriented programming. It is recommended to start with Python when one wishes to work with web based applications too. Programmers who can code in any of the object oriented programming languages will find it extremely helpful to learn Python and start comfortably using it. One massive upside of learning Python first is the ‘RPy2’ library which forms the bridge to make a smooth transition to R after Python.
To wrap it up, it can be concluded, with individual exceptions, that a statistician might want to begin their journey in Data Sciences with R, whereas a software programmer will want to get fluent in Python first and then move on to R. Ultimately, it is the combination of the two skill sets that a successful Data Scientist will need.