How Jupyter Notebooks played an important role in the incredible rise in popularity of Data Science and why they are its future.

Nowadays, many individuals and teams are flocking to the tools and techniques that enable them to leverage large amounts of data. What makes Jupyter Notebooks so appealing to data scientists?

In this article I will dive into some of the underlying trends that have contributed to the success of Jupyter Notebooks and why I decided to build Orchest to leverage and further contribute to its success.

Underlying technologies of data science

Something that is less talked about is the connection between the many advances of machine learning and data science, and the underlying technologies that have been developed over the past decades. Specifically I’m talking about programming languages such as Python, operating systems like Linux, compiler infrastructure like LLVM, and version control systems such as Git. Just to name a few. It’s important to realize that fundamental projects like these have enabled the vast growth and advances in machine learning and data science.

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