![]() The idea was to write out the program logic in human language with code snippets and macros written as separate comments, known as “WEB”. Introduced in 1984 by Donald Knuth, literate programming was intended as a methodology to create programs that are readable by humans. However, notebooks have a rich history that precedes data science-one dating back to the early 1980s. The History of The Data Science NotebookĪlmost every data scientist today has used a notebook, with the most popular being Jupyter Notebooks. ![]() This article looks at the past, present, and future of the notebook and how notebooks are breaking down silos for data work and collaboration. ![]() More importantly, notebooks are also empowering citizen data scientists to democratize data insights. Notebooks are at the crux of this and are a component of many tooling innovations we see in the modern data stack today. Organizations have been investing heavily in data science and analytics and a key area of investment have been tools that allow data scientists to work efficiently and rapidly experiment with data. They allow data scientists to rapidly experiment and share insights through quick environment creation, interactive output, and code snippets that can be executed in any order. Notebooks have been commonplace in data science for most of the last 10 years and every data scientist has worked with one.
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