R vs Python

R vs Python

If you are seeking high-performance data science tools, you really have two options: R or Python. When starting out, you should pick one, and your choice comes down to what’s right for you. The difference between the R and Python has been described in numerous infographics and debates online, but the most overlooked reason is person-programming language fit. Don’t understand what we mean? Let’s break it down.

Fact 1: Most people interested in learning data science for business are not computer scientists. They are business professionals, non-software engineers (e.g. mechanical, chemical), and other technical-to-business converts. This is important because of where each language excels.

Fact 2: Most activities in business and finance involve communication. This comes in the form of reports, dashboards, and interactive web applications that allow decision makers to recognize when things are not going well and to make well-informed decisions that improve the business.

Python has many strengths, such as its robust data structures such as Dictionaries, compatibility with Deep Learning and Spark, and its ability to be a multipurpose language. However, many scenarios in enterprise analytics require people to go back to basic statistics and Machine Learning, which the classic Data Science packages in Python are not as intuitive as R for. The key difference is that many statistical methods are built into R natively. As a result, there is a gap for when R users must build workflows in Python.

R is great for data exploration, transformation, statistical modeling, and visualizations. However, there is a huge community of Data Scientists and Analysts who turn to Python for these tasks. Moreover, both R and Python experts exist in most analytics organizations, and it is important for both languages to coexist.

What Should You Do?

Think about where you are coming from:

Are you a computer scientist or software engineer? If yes, choose Python.

Are you an analytics professional or mechanical/industrial/chemical engineer looking to get into data science? If yes, choose R.

Think about what you are trying to do:

Are you trying to build a self-driving car? If yes, choose Python.

Are you trying to communicate business analytics throughout your organization? If yes, choose R.