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To excel in the data science profession, it becomes imperative that one starts making efforts in sharpening their skills to full potential, by not only understanding the underlying models of data science, but escalating their knowledge in programming tools as well. When it comes to programming, the most talked about the debate is between R and Python enthusiasts, claiming one over the other, with reasons and by listing down the advantages of one over other. This debate needs a change of perspective because whenever we enter a debate you are claiming one is better than the other. And for a true data science enthusiast this would not be beneficial, in fact, if one programming language is chosen over another, in some sense it serves more as a disadvantage. A sound data scientist should definitely know the difference between both the languages, so that he or she is sure when to use what, while coming up with solutions, but beyond that, if the data scientist understands the fundamentals of both R and Python, they can then leverage their knowledge of both languages, based on their understanding of the basic data science concepts.
R and Python are just different tools used to perform different tasks, think about it! As a data scientist you will need a tool that will permit you to perform, statistical computations, data analysis etc…, hence R or Python just become elements of knowledge from disciplines such as statistics, computer science, engineering, essentially a computing tool. Now if you are a carpenter, would it just be good for you to have one wrench in your toolbox or a combination of wrench’s which are dynamic in its capabilities? Similarly, with the knowledge of both the languages, a data scientist will be able to express better in data science projects. This analogy gives you enough reasons to learn both the languages.
Learning both the languages will help you gain confidence in communication while working in the field of data science, you will need to interact with both sets of people, who are users of R and Python. Any company or vertical you associate with will have projects done in both languages. Without the basic knowledge of both these languages, it will be very difficult to appreciate the efforts and the entire working around on the project. An added advantage of fluency in both the languages would be your ability to communicate with audiences that are comfortable with either language while working on a project.
Springboard your career opportunity in the field of data science, some companies, or at times, departments within the companies might be comfortable using either of the languages. The last thing a candidate thinking of pursuing his career in data science should be missing out on an opportunity, due to the lack of knowledge in either R or Python. No one is expected to be a professional in any one language, a data scientist is expected to acquire as many skills and tool as they can to be successful in their career. Hence learning basics of both the languages will give you an upper edge.
The best thing is that both the languages are not very difficult to learn, although R and Python are unique languages, they are very similar in many ways. They do have a different syntax and have their own technical advantages, at the same time are similar while using appropriate Python packages like Pandas etc…,
The world of data science is not getting smaller but will only continue to grow in this data-driven world. At such times it is best to enhance your knowledge where ever applicable. Doing an online course to learn R and Python would thus be a very recommended effort.
 

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