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Putting the science back in data science

ne of key tenets of science (physics, chemistry, etc.), or at least the theoretical ideal of science, is reproducibility. Truly “scientific” results should not be accepted by the community unless they can be clearly reproduced and have undergone a peer review process.

Of course, things get messy in practice for both academic scientists and data scientists, and many workflows employed by data scientists are far from reproducible. These workflows may take the form of: A series of Jupyter notebooks with increasingly descriptive names, such as second_attempt_at_feature_selection_for_part2.ipynb

Source: oreilly.com
Author: Daniel Whitenack

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