I recently had an interesting discussion on certification program, which looking at the current landscape, does feel a strong need for a deeper look into it and where it is needed. Completion or Proficiency I am sure most of us know that the proliferation of certification programs for data analytics, data science and even cybersecurity is a phenomenon in the last decade at least. These certification program if you check them out are mostly “Certification of Completion” rather than “Certification on Proficiency”. Wait, what does it mean and what is the difference?
Hi Koo Ping, I have been the head of data science at my last company before starting a nonprofit and did quite a bit of interviewing and looking at degrees, certifications, etc... I think the industry has been maturing rapidly and experience has been much more important than the certificates, even a short 6 months hands-on project weighs more. That's just been my experience, it's not to say that certificates don't have their uses.
Well put that the different kinds of certification send different signals to the marketplace / employer, ranging from knowledge to skills to competencies. The more a practice can be standardised – e.g. accounting, the more the certification can be geared towards competencies because you can test on well-defined use cases. In that regards, as a practice becomes standardised / codified, certifications around knowledge and skills would be deemed as less useful. With regards to data science, there are part of the practice that has become standardised, e.g. ML-based predictive modelling. However, there are still many areas like Gen AI which are non-standard (and will not be for some time), the certification on knowledge and skills (which are more broad-based) will be the way to go.
Hi Koo Ping, I have been the head of data science at my last company before starting a nonprofit and did quite a bit of interviewing and looking at degrees, certifications, etc... I think the industry has been maturing rapidly and experience has been much more important than the certificates, even a short 6 months hands-on project weighs more. That's just been my experience, it's not to say that certificates don't have their uses.
Well put that the different kinds of certification send different signals to the marketplace / employer, ranging from knowledge to skills to competencies. The more a practice can be standardised – e.g. accounting, the more the certification can be geared towards competencies because you can test on well-defined use cases. In that regards, as a practice becomes standardised / codified, certifications around knowledge and skills would be deemed as less useful. With regards to data science, there are part of the practice that has become standardised, e.g. ML-based predictive modelling. However, there are still many areas like Gen AI which are non-standard (and will not be for some time), the certification on knowledge and skills (which are more broad-based) will be the way to go.