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Radar Recap — Data Science Certification: Is It Worth It?

In this article, we summarize the main takeaways from DataCamp Radar’s session on Data Science Certifications.
Aug 2022  · 6 min read

With the rising demand for data science jobs, certifications have emerged as an excellent way for aspiring practitioners to accelerate the job hunt, certify their skills, and stand out in the hiring process. 

As a part of the DataCamp Radar Conference, we hosted a panel with Jen Bricker, Head of Career Services at DataCamp, Vicky Kennedy, VP of Certification at DataCamp, and Maggie Remynse, VP of Curriculum at DataCamp on the value of data science certifications. Throughout the panel, they gave an overview of data science certifications, discussed the benefits and drawbacks of pursuing certifications, and gave a walkthrough of DataCamp’s data science certifications and how to pass them.

If you’re considering pursuing a data certification yourself, read on to learn more about whether this path is right for you 

Screenshot of Radar Event

Check out the full session here

An Overview of Data Science Certifications

In the session, Kennedy defined data certifications as “specifically around the assessment process. It’s about officially recognizing someone’s level of knowledge, skill, and ability.” She argued this distinction is important because while people can learn in a variety of ways, from on-the-job training and experience to online training and MOOCs, “certifications bring credibility to whatever method you’ve learned.”  

DataCamp’s Certifications were designed to be an official recognition that an individual has achieved the required skill level to be either a data scientist or a data analyst

Vicky KennedyVP of Certification at DataCamp

Kennedy also discussed the wide range of certifications many organizations and developers offer. There is a clear distinction between a certificate of completion for a course or online training and certification of knowledge. The first serves as a good motivator for completing a course, while the latter represents a certification on skill level. 

Remynse then discussed the lifecycle of data certifications. She argued that certain tool-specific ones can run their course quickly if they are not widely adopted or relevant for a long time. Others provide long-term value and demonstrate knowledge that is valuable for many years. She discussed how many of the new certifications are “built with case studies or real-world examples,” which help you prove to yourself and employers that “you are real-world ready.“  

Modern data science certifications are built to validate your ability to do data science in the real world

Maggie Remynse-ChouVP of Curriculum at DataCamp

Kennedy then described the two types of data certifications one can get: tool-based ones, typically offered by the developer of the tool, and role-based ones that are tool or tech agnostic. Examples of tool-based certifications include language-specific certifications for R or Python or ones for data visualization tools. An example of a role-based one would be DataCamp’s Data Analyst or Data Scientist certifications which validate typical skills required for these respective roles. 

The choice between both types of certification is really about what is the learner journey and what is the long term job journey an individual wants to take that’s going to set them up for success.

Maggie Remynse-ChouVP of Curriculum at DataCamp

The Pros and Cons of Pursuing Certifications

With the many types of certifications available, the panelists discussed the pros and cons of pursuing a data certification. They touched on how certification can help supplement career-focused education degrees and strengthen the story you tell recruiters, though they also warned the audience not to take away from that story with too many certifications. 

Remynsed discussed how degrees in education (e.g., bachelor's or post-graduate degrees) are very time intensive with not as many practical, job-specific learning outcomes. She argued that with certifications, “you’re really certifying on the skills that are relevant to the job you’re looking for.” It is still an intense process, but the time spent on the process is a lot more focused and provides more practical job-relevant training. She also mentioned that degrees don’t keep up with up-to-date content as well as most certifications do.  

With certifications, you’re really certifying on the skills that are relevant to the job you’re looking for

Maggie Remynse-ChouVP of Curriculum at DataCamp

Kennedy mentioned that certifications bring credibility to your knowledge in a field, regardless of how you gained that knowledge. That is their main benefit and their value. Certifications also force you to take steps to work on mastering the skill. A certification is a tangible, legitimate reward at the end of an intellectual pursuit, similar to receiving a bachelor's or master's degree after college.

Kennedy then advised that you want to focus on being specific with your certification to build on your story for recruiters. Remynse added that a certification tells the story that you “took time, learned a topic, studied well, had to practice it, had to master it, and were able to show it by earning a certification.”

While this idea of creating a valuable story with certifications is a clear positive, too many certifications and too broad a scope of certifications can detract from your story. From the perspective of Bricker as a recruiter, she says when she looks at a resume with too many certifications, it makes the story less clear and diminishes the value of the relevant, job-related certifications. 

An Overview of DataCamp’s Certification process 

In the final part of this session, Kennedy discussed the DataCamp Certifications and how to pass them. She discussed the data analyst and data scientist certifications, what applicants can expect, and what updates to the certifications applicants can expect. You can learn more about DataCamp’s Certifications here. Diving deeper, here are the different skills and aptitudes that the different certifications test for. 

Data Analyst Certification

The Data Analyst certification assesses skills in the following areas:

  • Data Management: As in the ability to query, clean, and aggregate data with SQL and database tools.
  • Exploratory Analysis: The ability to analyze data and interpret insights from data
  • Coding for Production: The ability to create high-quality, readable code in Python or R and SQL
  • Analytics Fundamentals: Best practices for analyzing data 
  • Statistical Experimentation: The statistical analysis skills needed to create meaningful experiments with data
  • Data Communication: The ability to communicate insights to a variety of stakeholders

Data Scientist Certification

The Data Scientist certification assesses the same skills as the data analyst certification, but goes beyond and tests on the following skillsets

  • Model Development: The ability to create machine learning models in either Python or R
  • Data Science Programming: The ability to create high-quality, readable and production-ready code for data science systems and solutions 

Vicky noted that certification from DataCamp was designed to be “an official recognition that an individual has achieved the required skill level to be either a Data Scientist or Data Analyst.” 

DataCamp’s Certifications were designed to be an official recognition that an individual has achieved the required skill level to be either a data scientist or a data analyst

Vicky KennedyVP of Certification at DataCamp

Beyond the certification process, a benefit for becoming certified is access to career services and exclusive communities on DataCamp for additional career support. 

Watch the Full Session to Learn More

To learn more about the value of certification, what’s new in the DataCamp certification in 2022, and what the roadmap for certifications looks like, make sure to check out the Radar on session in its entirety

Python Data Science Toolbox (Part 1)

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Exploratory Data Analysis in R

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4 hours
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Learn how to use graphical and numerical techniques to begin uncovering the structure of your data.

Exploratory Data Analysis in SQL

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Learn how to explore what's available in a database: the tables, relationships between them, and data stored in them.
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