Data Science is a technical field that applies scientific rigour to the understanding of business data and the associated processes and products. Like traditional science, it can be full of jargon that leads to unclear business messages and a failure of findings to be understood and acted upon. Having reviewed countless Data Science reports and presentations that are not ready for business readers, I thought it would be useful to provide a list of business terms to replace Data Science jargon.
Data Science Jargon Buster
|Term||Data Science Jargon||Business term|
|1||Factor / independent variable||inputs|
|2||Dependent variable||metric / KPI/ Outcome|
|3||Monotonically increasing||always increasing|
|8||Null Hypothesis||Status quo|
|9||Type 1 error||False positive / wrong prediction|
|10||Type 2 error||Failed to detect the effect|
|11||Sensitivity / Recall||Completeness|
|12||harmonic mean, geometric mean||average|
Eradicating Data Science jargon from your team
Do you have some jargon you would like to eradicate from your team? Get in touch! Let’s build this list together.
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