<ul><li>Python is a general-purpose, object-oriented programming language.</li><li>It emphasises code readability by using white space.</li><li>It is easy to learn.</li><li>It is a favourite of programmers and developers.</li><li>Python is very well suited for use in machine learning at a large scale.</li><li>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.</li></ul>

Python

They approach data science differently: <ul><li>R is used for statistical analysis.</li><li>Python provides a more general approach to data.</li></ul> <ul><li>R is built by statisticians and leans heavily into statistical models and specialised analytics.</li><li>Python is a multipurpose language that can provide data analysis or machine learning in scalable production environments.</li></ul> <ul><li>You might use R for customer behaviour analysis or genomics research.</li><li>You might use Python for developing a machine learning application. </li></ul>

The main difference between R and Python

The language to choose depends on your situation. Points to consider:<ul><li>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.</li><li>What do your colleagues use? Academics, engineers and scientists use R. Python is used in a wide range of industries.</li><li>What problems are you trying to solve? R is used for statistical learning, and Python for machine learning and large-scale applications.</li></ul>

Python vs. R: which one to choose

Data collection<ul><li>Python supports all kinds of data formats.</li><li>R is designed for data analysts to import data from Excel, CSV and text files.</li></ul> Data exploration<ul><li>In Python, you explore data with Pandas, the data analysis library for Python.</li><li>With R, you can build probability distributions, apply different tests, use standard machine learning and data mining techniques.</li></ul> Data modelling<ul><li>Python has standard libraries.</li><li>With R, you'll sometimes have to rely on packages outside of R's core functionality.</li></ul>

Other key differences

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

Explore the basics of these two open-source programming languages, the key differences that set them apart and how to choose the right one for your situation.