The importance of non-formal education in data science is increasing:
• With the growing demand and the insufficient supply of data scientists:
o Employers can offer training to existing employees to meet needs
o Employees interested in career changes can retrain
• In the quickly changing field of data science, professionals constantly need to retrain
• Working professionals can find offerings in formal education impractical
• Non-formal training can be cost effective
The goal of this scheme is to propose a common labelling program for non-formal learning in data science with the following objectives:
• Increase the transparency of the essential characteristics of non-formal training in data science
• Ease the comparison of different non-formal training credentials
• Help to establish “best practice” expectations for non-formal training.
After a number of design and feedback iterations with the BDV community, the labelling program is currently in the proof of concept stage. Below you will find an example BDV Data Science Label.
