Why an Active Ontology Matters for Data Science

No matter what language or techniques are being applied, there are enough similarities between data science approaches that some broad parallels can be drawn. Independent of language and model specifics, generalizations can be teased out of data science methods to provide a reference point for the many ways to solve similar problems. Before tackling a complex data science problem developers often check GitHub and other repositories for ideas or snippets to avoid recreating wheels. However, according to IBM researcher Ioana Baldini much can be overlooked when casting such a wide net. The key is to build an ontology of data science methodologies, tie those to real code, and connect the dots via annotations and other code information for many problem sets that are not language or model specific.