When it comes to data science solutions, there’s always a need for fast prototyping. Editor’s note: Welcome to Throwback Thursdays! Because Jupyter Notebooks are a relatively recently-developed tool, they don’t (yet) follow or encourage consensus-based software development best practices. Jupyter Notebook Best Practices for Data Science September 15th, 2016. Jupyter Notebook Best Practices. Follow. Dominik Haitz. Concise advice to use Jupyter notebooks more effectively. Every third Thursday of the month, we feature a classic post from the earlier days of our company, gently updated as appropriate. Best Practices for Jupyter Notebooks. Be it a sophisticated face recognition algorithm or a simple regression model, having a model that allows you to easily test and validate ideas is incredibly valuable. Using a Jupyter notebook template (which sets up default imports and structure) and the Table of Contents (toc2) extension, which automatically numbers headings.

Data scientists, typically collaborating on a small project that involves experimentation, often feel they don’t need to adhere to any engineering best practices.

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