One of the most common question people ask is which IDE / environment / tool to use, while working on your data science projects. As you would expect, there is no dearth of options available – from language specific IDEs like R Studio, PyCharm to editors like Sublime Text or Atom – the choice can be intimidating for a beginner.

If there is one tool which every data scientist should use or must be comfortable with, it is Jupyter Notebooks (previously known as iPython notebooks as well). Jupyter Notebooks are powerful, versatile, shareable and provide the ability to perform data visualization in the same environment.

Jupyter Notebooks allow data scientists to create and share their documents, from codes to full blown reports. They help data scientists streamline their work and enable more productivity and easy collaboration. Due to these and several other reasons you will see below, Jupyter Notebooks are one of the most popular tools among data scientists.

In this article, we will introduce you to Jupyter notebooks and deep dive into it’s features and advantages. By the time you reach the end of the article, you will have a good idea as to why you should leverage it for your machine learning projects and why Jupyter Notebooks are considered better than other standard tools in this domain!

Jupyter Notebook is an open-source web application that allows us to create and share codes and documents. It provides an environment, where you can document your code, run it, look at the outcome, visualize data and see the results without leaving the environment. Read more from analyticsvidhya.com…

thumbnail courtesy of analyticsvidhya.com