Do you need certifications for a successful career in data science? That’s a big and complicated question. Some organizations solely recruit data scientists with certifications; others are happy to hire anyone who can show mastery of data science principles and tools.
If nothing else, a certification can signal you are up-to-date with current data science skills. Although data science is very much an in-demand role (and supposedly the “sexiest” job of the 21st century), employers still want assurance you can analyze massive datasets for key insights. For example, having a certification from the Data Science Council of America (DASCA) indicates that you already have 3-5 years of practical and professional experience in data science.
Do You Need Data Science Certifications?
“What really helps is understanding the pros and cons to each data science cert from price, prerequisites, to niche tool coverage and applicable data examples,” says Troy Kranendonk, senior curriculum manager for Pluralsight Skills Content Development.
“Generally, each [certification] provides some exposure to data problems that need solving,” Kranendonk adds. “I would argue that having a conceptual understanding of the material found in a cert is much different than getting your hands dirty with the data to solve an actual business problem and then clearly communicating the solution to stakeholders.”
In other words, having a data science certification looks good in your application materials, but you should also demonstrate that you can apply your knowledge in practical ways to data science challenges. While some certifications cover broad data science principles, data scientists with some experience may gravitate toward a certification in a specialization such as machine learning.
Three common (and popular) data science certifications include:
- Certified Analytics Professional (CAP), $495 for INFORMS members, $695 for non-members, In-Person at designated test centers, Self-Paced
- Senior Data Scientist (SDS), DASCA: Cost: $775, Online, Self-Paced
- SAS Certified Data Scientist, $180 per exam, Online, Self-paced
Choosing the right data science certification can depend on whether you already have years of work experience under your belt or not. “Some certs are entry-level while others contain concrete prerequisites with the understanding that you already have years of experience with things like statistical analysis,” Kranendonk points out.
What Certifications Do You Need for a Data Science Job?
Kranendonk says tooling, skill, and industry-specific certifications with hands-on validation will be the most valuable in the new future. “The Titanic and Iris datasets commonly used for data science practice will only get you so far,” he says. “Most organizations are slowly understanding that people need data skills coupled with the actual industry-specific data used in the real world.”
This means having a data science certification with a focus on a particular discipline or field, whether that’s healthcare, finance, marketing, or some other industry. It’s also a good idea to continuously take inventory of your skills and knowledge of topics and tooling.
“From there you can shrink your gaps and train your weaknesses with certs that align with your personal analysis,” Kranendonk says. “This will allow you to quickly determine where you’re starting from: ground zero, a bootcamp, a degree in data analytics, or a master’s in data science?”
What is the Best Data Science Certification?
For those just starting out in the “wonderful world of data,” you can opt to pursue “well-rounded” certifications such as those mentioned above—but keep in mind that some employers may want you to have a specialized certification for particular languages or tools; for example, a Python certification.
If you want a certification that shows your skills with a range of tools, consider the IBM Data Science Professional Certification:
- IBM Data Science Professional Certificate, Free, Online, Self-Paced
This certification consists of nine courses ranging from open source tools and data science methodology to data visualization and machine learning.
“Stay hungry and don’t stop learning and practicing,” Kranendonk advises. “In today’s world, staying up to date with data skills is just as valuable as learning them for the first time given how fast technology changes and how quickly tech skills become outdated.”