Last March, I wanted to break into the Data Science community but was struggling with confidence and starting to doubt I’d make it. I had been reading blog after blog about data science, practiced additional coding and learning new methods at night. And rejection after rejection, I wondered if I was on the right path. Now on the other side, hindsight is 20⁄20. I write this post to my past self, a person seeking for a job they couldn’t seem to get.
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If you’ve ever wanted to tag your data science model, you’ve probably wondered how to version it. Which will it be: vx.4.1, v34.1231.51.21, or v91.x4.dev34? After reading about semantic versioning, I propose a method for versioning data science models. Semantic Versioning for Software Semantic versioning proposes the following: Given a version number MAJOR.MINOR.PATCH, increment the: MAJOR version when you make incompatible API changes, MINOR version when you add functionality in a backwards-compatible manner, and PATCH version when you make backwards-compatible bug fixes.
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Bryan Whiting

father, innovator, data scientist

Data Scientist

Washington, D.C.