Posted by Marbenz Antonio on November 29, 2022
The use and importance of digital credentials have grown over the past few years. Many professionals have dozens of accolades, especially in the Information Technology (IT) industry or business.
However, digital credentials are not uniform, just like those who earn them. Some symbolize academic success. Others demand passing a test. Additional examples show experience adding value to an organization.
Two primary certificates hold digital credentials in the field of data science. These have equivalents in similarly technical professions, such as IT architecture.
Product certification comes first. These are given by significant software and cloud providers (also known as “hyperscalers”) to candidates who pass a test and show they have a solid understanding of data science, and more specifically, machine learning, in the context of their provider’s application(s) or technical stack. The hiring company can be assured that the candidate understands the product(s) and terminology they will be working with and has received training in the fundamentals of data science and/or machine learning.
These should not be confused with the second kind, which is a professional certification that’s also a system. The Certified, Master, and Distinguished Data Scientist certifications, which are given out by The Open Group, are awarded at three different levels and demonstrate not only the acquisition and maintenance of knowledge through education but also the application of skills and a methodology to produce business outcomes. A candidate can show a potential employer that he or she has real-world experience at a specific level through the achievement of one or more of these certifications, not just in terms of technical expertise but also in terms of business acumen and dealing with team members and stakeholders.
The question, “Which type is more significant? But doing that is similar to picking a favorite child and is therefore improper. Instead, there is a small overlap between the two types but significant value when they are combined.
Think of a scenario that is both realistic and fantastic. A position on a data science team calls for mid-career experience in a particular cloud environment. By looking for a combination of Open Certified Master Data Scientist and the appropriate “hyper-scale” data science or machine learning certification, the hiring team or manager can locate fully qualified experts very quickly. The candidates will be able to successfully execute a data science methodology and deliver solutions in a professional setting, in addition to understanding the technologies with which they will be expected to work. The professional could continue to have a “hyper-scale” certification while moving upward in the company and becoming an Open Certified Distinguished Data Scientist.
Data science is a team sport, so it’s important to keep in mind that a strong team will also include data analysts, engineers, and architects in addition to data scientists. The same strategy for acquiring all necessary credentials also applies to them.
For these roles, the major cloud providers offer equivalent “hyper-scale” certifications, some software vendors offer application certifications, and The annual events offer Open Certified Architect (Open CA) and Open Certified Technical Specialist (Accessible CTS) paths, the latter of which has many specializations, including Business Analysis, Data Engineering, and Data Platform.
The smooth and proper execution of the chosen technique inside the preferred infrastructure is ensured by assembling a team of highly credentialed data science professionals. This objectively demonstrates organizational excellence to attract more talent and/or clients.
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