They approach data science differently:

• R is used for statistical analysis.
• Python provides a more general approach to data.

• R is built by statisticians and leans heavily into statistical models and specialised analytics.
• Python is a multipurpose language that can provide data analysis or machine learning in scalable production environments.

• You might use R for customer behaviour analysis or genomics research.
• You might use Python for developing a machine learning application. 

Data collection

• Python supports all kinds of data formats.
• R is designed for data analysts to import data from Excel, CSV and text files.

Data exploration

• In Python, you explore data with Pandas, the data analysis library for Python.
• With R, you can build probability distributions, apply different tests, use standard machine learning and data mining techniques.

Data modelling

• Python has standard libraries.
• With R, you'll sometimes have to rely on packages outside of R's core functionality.

The language to choose depends on your situation.

Points to consider:

• Do you have programming experience? Python has a learning curve that's linear and smooth. With R, novices can Run data analysis tasks in minutes, but it takes longer to develop expertise.
• What do your colleagues use? Academics, engineers and scientists use R. Python is used in a wide range of industries.
• What problems are you trying to solve? R is used for statistical learning, and Python for machine learning and large-scale applications.