The Fully Credentialed Data Science Professional

For several years the popularity and importance of digital credentials have been growing.  Many professionals, particularly in the Information Technology (IT) industry or organization, have earned dozens.

However, like the people that earn them, digital credentials are not homogeneous.  Some signify completion of education.  Others require passing an exam.  Further examples represent hands on experience delivering value to an organization.

In the context of data science, there are two major types of certifications that carry digital credentials.  These have equivalents in similarly technical professions, such as IT architect.

The first is product certification.  These are awarded by major software and cloud providers (a.k.a. “hyperscalers”) to applicants who demonstrate knowledge of data science or specifically machine learning in the context of that provider’s application(s) or technical stack through passing an exam.  A hiring organization requiring skills within that ecosystem can be confident that the candidate understands the product(s) and terminology with which they would be working and has been educated in the basics of data science and/or machine learning.

These are not to be confused with the second type, which is a platform agnostic, professional certification.  Awarded by The Open Group at three levels – Certified, Master, and Distinguished Data Scientist – these demonstrate not only gaining and maintaining knowledge through education but also the application of skills and a methodology to deliver business outcomes.  Through the earning of one or more of these certifications, a candidate can demonstrate to a potential employer that he or she has real-world experience at a given level, not just technically but also in business acumen and working with team members and stakeholders.

It may be tempting to ask, “which type is more important?”  But that should really be thought of in the same light as picking a favorite child – inappropriate.  Rather, the two types are inherently complementary with minimal overlap but significant combined value.

Consider a realistic but hypothetical scenario.  A data science team has an opening that requires mid-career experience within a specific cloud environment.  The hiring team or manager can very quickly find fully credentialed professionals by looking for a combination of Open Certified Master Data Scientist and the applicable “hyperscaler” data science or machine learning certification.  Not only will the candidates understand the tools with which they will be expected to work, but they will also have demonstrated experience successfully executing a data science methodology and deploying solutions professionally.  Further, the professional could advance to a more senior position and become an Open Certified Distinguished Data Scientist, while maintaining his or her “hyperscaler” certification.

It is worth noting that data science is a team sport and that a well-designed team is not exclusively made up of data scientists but also data analysts, data engineers, and data architects.  This same approach to becoming fully credentialed works for them too.

The major cloud providers have equivalent “hyperscaler” certifications for these roles, some software providers offer application certifications, and The Open Group offers Open Certified Architect (Open CA) and Open Certified Technical Specialist (Open CTS) paths, the latter of which has Business Analysis, Data Engineering, and Data Platform specialties among many others.

Building a team of fully credentialed data science professionals helps to ensure the smooth and proper execution of the chosen methodology within the preferred infrastructure, while also objectively demonstrating organizational excellence to attract incremental talent and/or customers.

Source: opengroup.org