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 labeling and endorsement 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
• Establish “best practice” expectations for non-formal training.
Currently the BDVe is gathering feedback on an initial proposal for the program. A pilot of the scheme is expected to begin in the second quarter of 2020. If you are interested in participating in the design of initiative you can contact us via email.