What Should You Learn: R or Python?

If you want to become a Data Scientist or Data Analyst, it’s imperative that you are develop expertise in both R and Python programming languages. However, which to learn first? Which one is more useful and relevant in the industry? If you are one of those confused analysts eager to further develop your skillset but are just plain then read on

In a KDnuggets poll, “What programming/statistics languages data analysts used for analytics / data mining / data science work in 2014?”, it shows that R is used extensively for data science work.

The reality today however is that companies are looking for Data Scientists, who are good in Python as well as R. However, I am of the opinion that one must be a tad more proficient in R mainly because:

* It is the one of the open source pioneers in the statistical Industry

* It has more than 6000 publicly available packages for advanced exploratory analytics

* You can integrate it with Java language as well as Hadoop distributed framework

* It has extensive packages for visualizations and statistical modeling

* Non-programmers and Mathematician finds it easy to code using R

* Packages like dplyr and ggplot2 are great for data manipulation and visualization with few lines of code

On the other hand, other experts may disagree and say that one should focus more on Python skills, because of the following reasons:

* It is a general purpose programming language.

* Object Oriented programmers finds it easy to code using Python.

* It’s packages like Pandas, Numpy, Scipy, and Seaborn are useful for data analytics

* It can be used to scrape data from web and clean unstructured data

* Using Python you can reuse your code or develop web applications

* It is very good in memory management as compared to R

* It has extensive Machine Learning packages such as scikit-learn

Looks like there are pros and cons to both of them. Infact a quick search on google will give you several discussions and debates on the subject. Let us take a look at the below figure showing the  job trends for Python over R from indeed.com.

Since Python is a general purpose data processing tool, its growth is showing exponential growth, as compared to R. At the same time, R as a statistical as well as data analytics tool, will continue to grow and is well respected. So what’s the end verdict? Well the best thing to do for an aspiring Data Analytics or Data Scientist profession, is to be well versed in both R as well as Python.

Want to find out more about the importance of learning these tools? Take a look at this article Why join Big Data Training?

If you want to explore a career in Big Data, check out Jigsaw Academy’s Big Data courses.

Reference :

https://www.kaggle.com/forums/f/208/getting-started/t/5243/pros-and-cons-of-r-vs-python-sci-kit-learn

https://www.quora.com/Which-is-better-for-data-analysis-R-or-Python

https://www.kdnuggets.com/2015/03/the-grammar-data-science-python-vs-r.html

https://www.dataschool.io/python-or-r-for-data-science/

https://www.kdnuggets.com/2014/08/four-main-languages-analytics-data-mining-data-science.html

https://r4stats.com/2014/02/25/job-trends-improved/

https://www.computerworld.com/article/2475559/big-data-is-python-really-supplanting-r-for-data-work.html

https://blog.revolutionanalytics.com/2013/12/r-and-python.html/

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