Introduction In the field of data analysis there are different aptitudes that you are required to ace before you can be known as a CHAMPION of the ring, yet the most critical dialects among different others is R or Python. These are the two dialects that you have to ace before you flourish in this god-like field. In any case, there is dependable this confusion or sentiment of logical inconsistency among different data analysts that which one of the both is the best of all, so is it R or is it Python well we as a whole are here to discover that so why not proceed and discover the rest. You most likely need to know both in light of the fact that the activity advertise is kind of part right now (perhaps for a valid justification). On the off chance that you totally need to concentrate on one right now, here is the way it’s part in my psyche. R programming : R is easier than Python for “data munging”,” taming bad or irregular data, transforming data, filtering data, etc. If you add NumPy in your definition of Python, that brings the two closer, but if you then bring in R packages such as plyr and data.table, things strongly tip in R’s favor. By the way, data.table is blinding fast, there are over 5″,000 packages available for R. R has much more statistical packages. Nearly anything you need to do statistically is accessible in R. (With some weird exemptions like partial reliance plot, for instance.) Furthermore, numerous Python executions of R functionalities are either missing or are actualized with some difficulty.R is a details particular dialect, and there is no way to avoid that. In case you’re doing specific statistical work, R packages cover more strategies. You can discover R packages for a wide variety of statistical undertakings utilizing the CRAN task view. R packages cover everything from Psychometrics to Genetics to Finance. In spite of the fact that Python, through SciPy and packages like statsmodels, covers the most widely recognized systems, R is far ahead. R was worked in light of insights and information investigation, such a large number of apparatuses that have been added to Python through packages are incorporated with base R. there is still nothing closely approaching RStudio as an IDE. Though Jupyter notebook is one of the finest but still RStudio is an IDE which is preferred by most of the population over Jupiter notebook data.tables is way overpowered comparing to pandas (and comparing to its ugly jealous cousin, dplyr). There are multiple examples, but the ease and elegance of munging data that are built-in into the logic of data.tables are unmatched by anything. In addition, R’s commercial applications increase by the minute, and companies appreciate its versatility. Taking these points into mind is it feasible to say that learning only R can make you go through your day to day analysis process, well I would suggest that it might not be true as there are various companies which prefer to work on python rather than on R, thus this makes learning both of the languages equally important. Now it might seem that why do we still use python if R is so perfect, well here is some of the important factors that lead us to use python: 1. If your company has a large data science team that is building an internal platform for machine learning/data analysis, a better decision is to build it in Python (so, chances are, that’s what they’re using), just because engineering a product in R is a nightmare. 2. Python is more likely to let you talk to some random database or open-source API. At one point, I needed to submit a list of latitudes/longitudes to an API and get the approximate altitude for each location. There is a Google Maps API and an easy-to-find example on StackOverflow in Python. Good luck finding the same in R. (This is a good example of why you might want to know a bit of both, even if you primarily use one over the other.) 3.Through packages like Lasagne, caffe, keras, and tensorflow, creating deep neural networks is straightforward in Python. Although some of these, like tensorflow, are being ported to R, support is still far better in Python. I hope this provides you with some basic idea go the difference between R and Python “,Both R and Python are in parity to each other regardless of your problem. But, if you want something which is multi purpose, growing yet flexible for data analysis then R is the winner of the game!