*Python is a general-purpose, object-oriented**programming language.**It emphasises code readability**by using white space.**It is**easy to learn**.**It is a favourite of**programmers and developers.**Python is very**well suited for use in machine learning at a large scale.**Its suite of specialised deep learning and machine learning libraries**includes tools like scikit-learn, Keras and TensorFlow. It enables data scientists to develop sophisticated data models that plug directly into a production system.*

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- R is also is an open-source
**programming language.** *It's optimised for**statistical analysis**and**data visualisation**.**R has a**rich ecosystem with complex data models**and practical tools for data reporting.**R is popular among**data science scholars and researchers**.**R provides**various libraries and tools**for cleansing and prepping data, creating visualisations, and training and evaluating machine learning and deep learning algorithms.**R is used within RStudio.**R applications can be used**directly on the web via Shiny.*

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**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.

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**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.

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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.*

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