9 Best Data Science Certificates to Earn in 2025

Data science certifications can be a game-changer for anyone looking to break into or advance within the rapidly growing field of data science. These certifications validate your skills, provide structured learning, and demonstrate your commitment to mastering the tools and techniques that drive modern data-driven decision-making. With the right certification, you can unlock exciting career opportunities, boost your earning potential, and stand out in a competitive job market.

Whether you’re an aspiring data analyst or a seasoned professional seeking specialization, choosing the right certification tailored to your goals is essential. This article simplifies your decision-making process by highlighting top data science certifications, categorized by skill level and focus. From beginner-friendly programs to advanced credentials, you’ll discover certifications that align with your career ambitions.

By the end of this guide, you’ll know which certifications to invest in, how they fit into your professional journey, and why they matter in today’s data-centric world.

Are Data Science Certifications Worth It?

Yes, data science certifications are worth it! They validate your skills, boost job prospects, and accelerate learning. Choose certifications aligned with your career goals, explore affordable online options, and complement them with hands-on experience and continuous learning to thrive in the competitive data science field.

Data Science Certifications Worth Pursuing

This section will explore the data science certifications that are worth your time and resources, and outline their unique benefits.

Certification Name	Description/Overview
IBM Data Science Professional Certificate	An entry-level program covering Python, machine learning, and data visualization, with hands-on labs using real-world datasets.
Microsoft Certified: Azure Data Scientist Associate	Validates proficiency in designing and implementing data science solutions using Azure Machine Learning.
Google Professional Data Engineer	Assesses the ability to design, secure, and operationalize data systems and machine learning models on Google Cloud.
SAS Certified Data Scientist	Focuses on advanced analytics, big data, and machine learning using SAS tools and open-source platforms.
Databricks Certified Associate Developer for Apache Spark	Tests knowledge of the Spark DataFrame API for data manipulation within Apache Spark.
AWS Certified Machine Learning	Validates expertise in developing and deploying machine learning solutions using AWS services.
DASCA Senior Data Scientist (SDS)	Designed for experienced professionals, focusing on advanced data science techniques and leadership in analytics.
DASCA Principal Data Scientist (PDS)	Aimed at seasoned professionals managing enterprise-wide data strategies and business intelligence systems.
Open Certified Data Scientist (Open CDS)	A vendor-neutral, experience-based certification emphasizing practical skills and peer-reviewed evaluation.

IBM Data Science Professional Certificate

This beginner-level program is ideal for those starting in data science, requiring no prior experience. It includes 10 courses that take approximately five months to complete with a weekly commitment of 10 hours. The curriculum covers tools like Jupyter Notebooks, SQL, Python, machine learning, and data visualization, emphasizing hands-on labs in the IBM Cloud using real-world datasets. Participants gain practical skills and build a project portfolio to demonstrate their expertise.

Best For: Beginners looking to develop a strong foundation in data science and build a project portfolio.

Price: $234

Microsoft Certified: Azure Data Scientist Associate

This certification focuses on validating expertise in Azure Machine Learning and MLflow through Exam DP-100: Designing and Implementing a Data Science Solution on Azure. Microsoft provides learning paths to help candidates prepare, including modules on automating machine learning, managing data, and using the Azure Machine Learning workspace. Certification renewal is free via Microsoft Learn updates, ensuring skills stay current with technology advancements.

Best For: Professionals working on data science projects using Azure who want to validate their expertise.

Price: ~ $165 (varies by region).

Google Professional Data Engineer

This certification assesses the ability to design, secure, and operationalize data processing systems and machine learning models. The 2-hour exam consists of 50-60 multiple-choice questions. While there are no formal prerequisites, Google recommends three years of industry experience, including one year with Google Cloud. Resources like online training and in-person classes are available to help candidates prepare.

Best For: Experienced professionals aiming to showcase their expertise in data engineering and Google Cloud technologies.

Price: $200

SAS Certified Data Scientist

This advanced certification focuses on using open-source tools and SAS Data Management to extract big data insights and build machine learning models. Candidates must complete three credentials, each requiring a separate exam. SAS provides comprehensive training resources, and academic discounts are available for students and educators.

Best For: Experienced data scientists seeking to specialize in big data and advanced analytics using SAS.

Price: Associate Exams: $120 each; Predictive Modeler Exam: $250; Other Exams: $180 each.

Databricks Certified Associate Developer for Apache Spark

This certification evaluates proficiency in the Spark DataFrame API for data manipulation tasks within Apache Spark. The 120-minute exam includes 60 multiple-choice questions. Candidates are encouraged to complete the Apache Spark Programming with Databricks course, which is available in instructor-led or self-paced formats, to prepare effectively.

Best For: Data engineers and analysts wanting to demonstrate proficiency in Apache Spark for big data analytics.

Price: $200 per attempt.

AWS Certified Machine Learning

This certification is tailored for professionals with over one year of experience developing, architecting, or running machine learning workloads on AWS. The 180-minute exam includes 65 multiple-choice or multiple-response questions and can be taken online or at a Pearson VUE testing center. AWS provides resources such as exam guides and sample questions to help candidates prepare.

Best For: Developers and data scientists leveraging AWS for machine learning solutions.

Price: $300

DASCA Senior Data Scientist (SDS)

This certification is designed for professionals with 3-5 years of experience in data science, computing, or analytics. It allows candidates six months to prepare for the exam, with a recommended self-study schedule of 8-10 hours per week. The certification demonstrates advanced capabilities and includes a digital badge to showcase expertise.

Best For: Experienced professionals aiming for senior-level roles in data science and analytics.

Price: $775 (all-inclusive).

DASCA Principal Data Scientist (PDS)

This certification targets seasoned professionals who have demonstrated leadership in data analytics and intelligence. Candidates have one year to complete the exam and assessment milestones. Preparation involves studying for 8-10 hours per week, and the certification validates expertise in managing and architecting enterprise-wide data strategies.

Best For: Thought leaders and senior professionals transforming organizational data strategies.

Price: Open Applications: $1450; Other tracks: $750-$1250.

Open Certified Data Scientist (Open CDS)

This vendor-neutral certification emphasizes real-world experience over theoretical knowledge. Instead of exams, candidates demonstrate their expertise through written applications and peer interviews. The process is structured to highlight practical skills in solving business problems using data.

Best For: Experienced professionals seeking a practical, experience-based certification recognized by global enterprises.

Price: Not publicly disclosed.

Where To Start When Considering Certifications

When considering certifications in data science, it’s essential to consider where you’re starting from and your ultimate career goals.

For instance, if you’re just beginning to explore the field or looking to solidify your foundational skills in data science, you might not need to jump into an official certification right away. In this case, enrolling in a comprehensive data science course, such as Springboard’s Data Science Bootcamp, can be a strategic and efficient way to start. Springboard offers students both a certificate upon completion and the foundational skills that are needed to launch a career in data science.

Springboard’s Data Science Bootcamp is designed to equip you with a solid understanding of the core concepts, techniques, and tools used in data science. You’ll learn through hands-on data science projects and get support from industry professionals and a community of peers. This holistic approach ensures that you don’t just learn the theory but also understand how to apply your knowledge in practical situations. Although this is not an official certification per se, it serves as a stepping stone for further specializations and certifications.

For those who aspire to progress into specific roles such as a data analyst, data engineer, Certified Analytics Professional, or even a data science manager, obtaining a data science certification can give you an edge. Many organizations in tech, healthcare, finance, and other industries value such credentials as they validate your proficiency in certain areas. More than that, some jobs might require data science certifications as a prerequisite. In such cases, having a certificate from an organization like the Data Science Council of America can be advantageous.

Data Science Certificate vs. Certification vs. Course

Feature	Course	Certificate	Certification
Definition	A series of lessons or lectures on a subject	Awarded after completing a course to indicate acquired knowledge or skills	Requires passing a rigorous exam to prove competence in a specific area
Purpose	To provide foundational knowledge on a topic	To validate completion of learning in a specific area	To enhance credibility and job prospects
Requirements	None (can vary by course)	Completion of the associated course	Typically requires education, work experience, and passing an exam
Focus	Learning a particular subject	Certifying specific knowledge or skills	Proving advanced expertise and competency

Understanding What Each Certification Requires

A certification can open doors in the field of data science and will help you demonstrate your skills to employers. There is an increasing demand for certified professionals with this important digital badge of honor pinned to their resume, but before you sign up, study the specific skills required and specific prerequisites. From completing hands-on projects, to gaining real-world experience or completing peer interviews, preparation is the key to success. 

Obtaining certifications in data science can significantly boost your career prospects. Here’s an example of the specific prerequisites and skills required for popular certifications:

America DASCA Senior Data Scientist (SDS):

• Prerequisites: Minimum 5 years of experience in data science or related field, bachelor’s degree in a relevant field like computer science, statistics, or mathematics.
• Skills: Advanced knowledge of statistical methods, machine learning algorithms, deep learning, big data technologies, data visualization, cloud platforms, and communication skills.

Certified Data Scientist (Open CDS):

• Prerequisites: Minimum 3 years of experience in data science or related field, bachelor’s degree in a relevant field, strong portfolio of data science projects.
• Skills: Proven expertise in data analysis, machine learning, programming languages like Python and R, statistical modeling, data visualization, and strong communication skills.

Cloudera Certified Professional (CCP) Programs:

• Cloudera Certified Associate (CCA) Data Analyst:
  • Prerequisites: No prior experience required to become Cloudera Certified Associate, basic understanding of data analysis concepts.
  • Skills: Ability to interpret and analyze data, familiarity with data visualization tools, understanding of basic statistics.
• Cloudera Certified Professional (CCP) program Cloudera-certified Data Engineer:
  • Prerequisites: CCA Data Analyst certification, 1-2 years of experience in data engineering.
  • Skills: Expertise in building and maintaining data pipelines, proficiency in programming languages like Python and Scala, knowledge of Hadoop and Cloud platforms.
• Cloudera Certified Professional (CCP) Data Scientist:
  • Prerequisites: CCA Data Analyst certification, 2-3 years of experience in data science.
  • Skills: Strong grounding in machine learning algorithms, familiarity with deep learning concepts, proficiency in Python and statistical libraries.

Whether you are trying to land your first role or need to advance to becoming a principal data scientist, knowing what the various tracks require (and what every certification requires) will help prepare and prove your worth as a subject matter expert. Bear in mind that technical know-how and professional experience aren’t the only requirements. You’ll need to achieve a specific overall score. Most exams require a fee as well, and these fees can change according to the location. Having a better understanding of what each certification consists of will help you identify the right course material and the essential skills you’ll need to succeed.

Choosing the Best Data Science Certifications for Your Needs

Data science is a lucrative field booming with opportunities. If you’re looking to break into this exciting domain or level up your existing skills, data science certifications can be a valuable stepping stone. But with so many options available, choosing the right one can feel overwhelming. Fear not, fellow data enthusiast! This guide will help you navigate the maze and find the best data science certifications for your aspirations.

First things first: What are your goals? Are you aiming for an entry-level data analyst role, or do you aspire to become a seasoned data science professional tackling complex predictive modeling and AI projects? Identifying your career path will guide your certification choices.

Here are some top contenders, categorized by level and focus:

1. Course Reputation: Opt for certifications with a strong track record of success, positive reviews, and endorsement from industry leaders. A well-regarded program often features a curriculum designed by experts and recognized by hiring managers, making it a solid addition to your resume.
2. Course Support: A top-tier certification program provides access to quality resources like lectures, software, and tools. Look for support systems such as mentorship, community forums, and career coaching, which can offer personalized guidance and enhance your learning experience.
3. Alignment with Career Goals: Choose a certification that matches your career aspirations and the demands of your target job roles. Review job descriptions and ensure the program covers relevant skills, tools, and techniques. For example, roles in AI and machine learning may require specific cloud or programming certifications.
4. Earning Potential: Certifications that increase earning potential or open doors to leadership roles are often worth the investment. Research salary trends and compare the career advancements of professionals with and without specific certifications to gauge the return on investment.
5. Learning Style and Flexibility: Consider whether you prefer self-paced learning, instructor-led programs, or a hybrid approach. If time is a constraint, flexible certifications that allow you to progress at your own pace may be ideal.

Certification Recommendations

Beginner-Friendly:

• Google Data Analytics Professional Certificate: Learn fundamental data analysis skills, including data cleaning, visualization, and manipulation, in a six-month online program. Ideal for aspiring data analysts.
• IBM Data Science Professional Certificate: Dive into Python programming, statistical analysis, and machine learning with a beginner-focused course designed to prepare you for general data science roles.

Intermediate-Level:

• Microsoft Certified: Azure Data Scientist Associate: Validate your expertise in using Microsoft Azure for machine learning and data analysis tasks. Perfect for professionals leveraging Azure in their work.
• Open Certified Data Scientist (Open CDS): A vendor-neutral certification assessing general data science skills, ideal for demonstrating broad competence across tools and methodologies.

Advanced:

• SAS Certified Advanced Analytics Professional: Specialize in advanced analytics and modeling using SAS software. Well-suited for professionals in industries like research or finance.
• DASCA Senior Data Scientist (SDS): A rigorous certification designed for experienced professionals tackling complex data science challenges like natural language processing and predictive modeling.

Beyond Certifications

While certifications are an excellent way to showcase your skills, they are just one piece of the puzzle. Practical experience, hands-on projects, and continuous learning are equally crucial for success in this ever-evolving field.

• Specialized Skills: Look for programs focusing on niches like natural language processing, computer vision, or healthcare analytics.
• Open-Source Tools: Certifications in tools like R or TensorFlow can boost your profile in specific areas.
• Cloud Platforms: Proficiency in platforms like AWS, Azure, or IBM Cloud can set you apart, especially for roles involving big data and AI.

Participating in hackathons, building a portfolio of projects, and staying updated on the latest trends can further solidify your position as a top candidate in the competitive data science job market.

FAQs About Data Science Certification

We’ve got the answers to your most frequently asked questions.