Python or R. What to Choose in Data Science?

Data scientists around the globe are given complex problems to solve. While talking about complex problems, data science can help to have set of insights that can change the way how organizations function. With a view of forming strategic decisions, data scientists usually leverage programming tools and languages.

In order to crunch massive amounts of data, dynamic and advanced programming tools are needed. Two of the most popular and common programming tools in data science are Python and R.

Python and R in Data Science

Python is a general-purpose programming language while R is widely used for exploratory data analysis and data visualization in statistical computing.

If you have some prior programming experience, learning python would be a smart move as Python’s syntax is more similar to other languages and it reads more like human language. On the other hand, R’s syntax is regarded as unintuitive by many programmers.

Both Python and R are widely used in various industries.If we go industry wise, Python is the trend and many organizations are shifting from R to Python. However, if we consider academia, especially statistics, R is widely adopted than Python because it’s more of a statistician language.

Python’s simplicity enables data scientists to use it for solving data science problems at scale by writing maintainable and powerful code.

Python language is mainly concerned with predictive accuracy. Scikit-learn is the most popular machine learning package for python. It makes it easy to cross-validate and switch between multiple models. If you are teaching statistical learning, then probably R is a better choice. However, Python’s Statsmodel is also a very nice package for statistical modeling which duplicates some functions of R.

When we talk about data cleaning, Python has much better data cleaning capabilities than R as it has a rich set of data structures and superior implementation of regular expressions.

In the field of data visualization, Python is making quite a lot of progress. R language is the best tool in the field of data virtualization providing everything in virtual like visualization charts, statistical models, data manipulation etc. Data scientists can overlook the old aged bar charts, line plots and create exceptional data visualizations with tools like ggplot2, ggiraph, dygraphs, RColorBrewer.

Organizations using Python

Google has been leveraging Python since the beginning. Dozens of Google engineers are using python. Google is looking for more and more people with skills of Python.
Mozilla is using Python for exploring their broad code base. Moreover, a lot of open source packages developed using Python are released by Mozilla.
Dropbox, the most popular file hosting service is completely developed with Python language.
Creative processes at Walt Disney are enhanced with Python language.
Airbnb, online hospitality service, and the marketplace is using Python language for their workflow management platform.
Philips is advancing in the field of automation by using python for the sequencing language which tells the robot what steps to take.

Python is a versatile language but its execution speed is slow. Sometimes, it is much slower than other languages like C, C++. However, tools like Numba and PyPy have helped in solving the speed issues and overlooking the aspect of slow speed.

What to choose?

Both Python and R are very useful. However, each of the languages has its own set of applicability in data science. If you know the kind of project you will be working on, then one can decide which language to go for. Moreover, your personal interests also matter and which language is hassle-free to learn.

We, at GreyAtom, have selected Python as the language of choice for our Full Stack Data Science Engineering program. Python is a fully featured programming language which can be used to create real software. Python also has other sets of capabilities which are not explored in R like network programming, web framework – think Django, Natural Language Processing (NLP), Web scraping in Data Science.