In many ways, the two open source languages are very similar.

Although both languages are bringing the future to life - through artificial intelligence, machine learning and data-driven innovation - there are strengths and weaknesses that come into play. If you work in data science or analytics, you’re probably well aware of the Python vs. Let’s get into it.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. In this article, I want to share my top 4 IDE for Data Scientist that I used in a different situation. Hence you might want to look into another IDE that still suits Data Scientist needs but could use for development. This is because Jupyter Notebook is mainly developed for testing and document sharing rather than code development. However, you would realize that Jupyter Notebook lacks any development and debugging purposes. It is a great tool for any level of Data Scientist. Well, you could code in the notepad, but obviously, why you want to do that? - There are many IDE developed that support our works.įor you who already familiar with the Jupyter Notebook, it is also an IDE that is interactive, beginner-friendly, and could be used for presentation. Any people who work with the programming language would need an IDE to make their job easier.

IDE or Integrated Development Environment is a code programming tool used for writing, testing, debugging, and intuitively compile code.

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