Data science certifications are everywhere, but do they actually help you land a job?

We talked to 15 hiring managers who regularly hire data analysts and data scientists. We asked them what they look for when reviewing resumes, and the answer was surprising: not one of them mentioned certifications.

So if certificates aren’t what gets you hired, why even bother? The truth is, the right data science certification can do more than just sit on your resume. It can help you learn practical skills, tackle real-world projects, and show hiring managers that you can actually get the work done.

In this article, we’ve handpicked the data science certifications that are respected by employers and actually teach skills you can use on the job.

Whether you’re just starting out or looking to level up, these certification programs can help you stand out, strengthen your portfolio, and give you a real edge in today’s competitive job market.

1. Dataquest

• Price: \$49 monthly or \$399 annually.
• Duration: ~11 months at 5 hours per week, though you can go faster if you prefer.
• Format: Online, self-paced, code-in-browser.
• Rating: 4.79/5 on Course Report and 4.85 on Switchup.
• Prerequisites: None. There is no application process.
• Validity: No expiration.

Dataquest’s Data Scientist in Python Certificate is built for people who want to learn by doing. You write code from the start, get instant feedback, and work through a logical path that goes from beginner Python to machine learning.

The projects simulate real work and help you build a portfolio that proves your skills.

Why It Works Well

It’s beginner-friendly, structured, and doesn’t waste your time. Lessons are hands-on, everything runs in the browser, and the small steps make it easy to stay consistent. It’s one of the most popular data science programs out there.

Here are the key features:

• Beginner-friendly, no coding experience required
• 38 courses and 26 guided projects
• Hands-on learning in the browser
• Portfolio-ready projects
• Clear, structured progression from basics to machine learning

What the Curriculum Covers

You’ll learn Python, data cleaning, analysis, visualization, SQL, APIs, and basic machine learning. Most courses include guided projects that show how the skills apply in real situations.

“I really love learning on Dataquest. I looked into a couple of other options and I found that they were much too handhold-y and fill in the blank relative to Dataquest’s method. The projects on Dataquest were key to getting my job. I doubled my income!” – Victoria E. Guzik, Associate Data Scientist at Callisto Media.

2. Microsoft

• Price: \$165 per attempt.
• Duration: 100-minute exam, with optional and free self-study prep available through Microsoft Learn.
• Format: Proctored online exam.
• Rating: 4.2 on Coursera. Widely respected in cloud and ML engineering roles.
• Prerequisites: Some Python and ML fundamentals are needed. If you’re brand-new to data science, this won’t be the easiest place to start.
• Languages offered: English, Japanese, Chinese (Simplified), Korean, German, Chinese (Traditional), French, Spanish, Portuguese (Brazil), Italian.
• Validity: 1 year. You must pass a free online renewal assessment annually.

Microsoft’s Azure Data Scientist Associate certification is for people who want to prove they can work with real ML tools in Azure, not just simple notebook tasks.

It’s best for those who already know Python and basic ML, and want to show they can train, deploy, and monitor models in a real cloud environment.

Why It Works Well

It’s recognized by employers and shows you can apply machine learning in a cloud setting. The learning paths are free, the curriculum is structured, and you can prepare at your own pace before taking the exam.

Here are the key features:

• Well-known credential backed by Microsoft
• Covers real cloud ML workflows
• Free study materials available on Microsoft Learn
• Focus on practical tasks like deployment and monitoring
• Valid for 12 months before renewal is required

What the Certification Covers

You work through Azure Machine Learning, MLflow, model deployment, language model optimization, and data exploration. The exam tests how well you can build, automate, and maintain ML solutions in Azure.

You can also study ahead using Microsoft’s optional prework modules before scheduling the exam.

“This certification journey has been both challenging and rewarding, pushing me to expand my knowledge and skills in data science and machine learning on the Azure platform.” – Mohamed Bekheet.

3. DASCA

• Price: \$950 (all-inclusive).
• Duration: 120-minute-long exam.
• Format: Online, remote-proctored exam.
• Prerequisites: 4–5 years of applied experience + a relevant degree. Up to 6 months of prep is recommended, with a pace of around 8–10 hours per week.
• Validity: 5 years.

DASCA’s SDS™ (Senior Data Scientist) is designed for people who already have real experience with data and want a credential that shows they’ve moved past entry-level tasks.

It highlights your ability to work with analytics, ML, and cloud tools in real business settings. If you’re looking to take on more senior or leadership roles, this one fits well.

Why It Works Well

SDS™ is vendor-neutral, so it isn’t tied to one cloud platform. It focuses on practical skills like building pipelines, working with large datasets, and using ML in real business settings.

The 6-month prep window also makes it manageable for busy professionals.

Here are the key features:

• Senior-level credential with stricter eligibility
• Comes with its own structured study kit and mock exam
• Focuses on leadership and business impact, not just ML tools
• Recognized as a more “prestigious” certification compared to open-enrollment options

What It Covers

The exam covers data science fundamentals, statistics, exploratory analysis, ML concepts, cloud and big data tools, feature engineering, and basic MLOps. It also includes topics like generative AI and recommendation systems.

You get structured study guides, practice questions, and a full mock exam through DASCA’s portal.

“I am a recent certified SDS (Senior Data Scientist) & it has worked out quite well for me. The support that I received from the DASCA team was also good. Their books (published by Wiley) were really helpful & of course, their virtual labs were great. I have already seen some job posts mentioning DASCA certified people, so I guess it’s good.” – Anonymous.

4. AWS

• Price: \$300 per attempt.
• Duration: 180-minute exam.
• Format: Proctored online or at a Pearson VUE center.
• Prerequisites: Best for people with 2+ years of AWS ML experience. Not beginner-friendly.
• Languages offered: English, Japanese, Korean, and Simplified Chinese.
• Validity: 3 years.

AWS Certified Machine Learning – Specialty is for people who want to prove they can build, train, and deploy machine learning models in the AWS cloud.

It’s designed for those who already have experience with AWS services and want a credential that shows they can design end-to-end ML solutions, not just build models in a notebook.

Why It Works Well

It’s respected by employers and closely tied to real AWS workflows. If you already use AWS in your projects or job, this certification shows you can handle everything from data preparation to deployment.

AWS also provides solid practice questions and digital learning paths, so you can prep at your own pace.

Here are the key features:

• Well-known AWS credential
• Covers real cloud ML architecture and deployment
• Free digital training and practice questions available
• Tests practical skills like tuning, optimizing, and monitoring
• Valid for 3 years

What the Certification Covers

The exam checks how well you can design, build, tune, and deploy ML solutions using AWS tools. You apply concepts across SageMaker, data pipelines, model optimization, deep learning workloads, and production workflows.

You can also prepare using AWS’s free digital courses, labs, and official practice question sets before scheduling the exam.

Note: AWS has announced that this certification will be retired, with the last exam date currently set for March 31, 2026.

“This certification helped me show employers I could operate ML workflows on AWS. It didn’t get me the job by itself, but it opened conversations.” – Anonymous.

5. IBM

• Price: Included with Coursera subscription.
• Duration: 3–6 months at a flexible pace.
• Format: Online professional certificate with hands-on labs.
• Rating: 4.6/5 on Coursera.
• Prerequisites: None, fully beginner-friendly.
• Validity: No expiration.

IBM Data Science Professional Certificate is one of the most popular beginner-friendly programs.

It’s for people who want a practical start in data analysis, Python, SQL, and basic machine learning. It skips heavy theory and puts you straight into real tools, cloud notebooks, and guided labs. You actually understand how the data science workflow feels in practice.

Why It Works Well

The program is simple to follow and teaches through short, hands-on tasks. It builds confidence step by step, which makes it easier to stay consistent.

Here are the key features:

• Hands-on Python, Pandas, SQL, and Jupyter work
• Everything runs in the cloud, no setup needed
• Beginner-friendly lessons that build step by step
• Covers data cleaning, visualization, and basic models
• Finishes with a project to apply all skills

What the Certification Covers

You learn Python, Pandas, SQL, data visualization, databases, and simple machine learning methods.

You also complete a capstone project that uses real datasets, giving you experience with core data science skills like exploratory analysis and model building. The program ends with a capstone project where you apply all the skills you’ve learned.

“I found the course very useful … I got the most benefit from the code work as it helped the material sink in the most.” – Anonymous.

6. Databricks

• Price: \$200 per attempt.
• Duration: 90-minute proctored certification exam.
• Format: Online or test center.
• Prerequisites: None, but 6+ months of hands-on practice in Databricks is recommended.
• Languages offered: English, Japanese, Brazilian Portuguese, and Korean.
• Validity: 2 years.

The Databricks Certified Machine Learning Associate exam is for people who want to show they can handle basic machine learning tasks in Databricks.

It tests practical skills in data exploration, model development, and deployment using tools like AutoML, MLflow, and Unity Catalog.

Why It Works Well

This certification helps you show employers that you can work inside the Databricks Lakehouse and handle the essential steps of an ML workflow.

It’s a good choice now that more teams are moving their data and models to Databricks.

Here are the key features:

• Focuses on real Databricks ML workflows
• Covers data prep, feature engineering, model training, and deployment
• Includes AutoML and core MLflow capabilities
• Tests practical machine learning skills rather than theory
• Valid for 2 years with required recertification

What the Certification Covers

The exam includes Databricks Machine Learning fundamentals, training and tuning models, workflow management, and deployment tasks.

You’re expected to explore data, build features, evaluate models, and understand how Databricks tools fit into the ML lifecycle. All machine learning code on the exam is in Python, with some SQL for data manipulation.

Databricks Certified Machine Learning Professional (Advanced)

This is the advanced version of the Associate exam. It focuses on building and managing production-level ML systems using Databricks, including scalable pipelines, advanced MLflow features, and full MLOps workflows.

• Same exam price as the Associate (\$200)
• Longer exam (120 minutes instead of 90)
• Covers large-scale training, tuning, and deployment
• Includes Feature Store, MLflow, and monitoring
• Best for people with 1+ year of Databricks ML experience

“This certification helped me understand the whole Databricks ML workflow end to end. Spark, MLflow, model tuning, deployment, everything clicked.” – Rahul Pandey.

7. SAS

• Price: Standard pricing varies by region. Students and educators can register through SAS Skill Builder to take certification exams for \$75.
• Format: Online proctored exams via Pearson VUE or in-person at a test center.
• Prerequisites: Must earn three SAS Specialist credentials first.
• Validity: 5 years.

The SAS AI & Machine Learning Professional credential is an advanced choice for people who want a solid, traditional analytics path. It shows you can handle real machine learning work using SAS tools that are still big in finance, healthcare, and government.

It’s tougher than most certificates, but it’s a strong pick if you want something that carries weight in SAS-focused industries.

Why It Works Well

The program focuses on real analytics skills and gives you a credential recognized in fields where SAS remains a core part of the data science stack.

Here are the key features:

• Recognized in industries that rely on SAS
• Covers ML, forecasting, optimization, NLP, and computer vision
• Uses SAS tools alongside open-source options
• Good fit for advanced analytics roles
• Useful for people aiming at regulated or traditional sectors

What the Certification Covers

It covers practical machine learning, forecasting, optimization, NLP, and computer vision. You learn to work with models, prepare data, tune performance, and understand how these workflows run on SAS Viya.

The focus is on applied analytics and the skills used in industries that rely on SAS.

What You Need to Complete

To earn this certification, you must complete three underlying credentials:

After completing all three, SAS awards the full AI & Machine Learning Professional credential.

“SAS certifications can definitely help you stand out in fields like pharma and banking. Many companies still expect SAS skills and value these credentials.” – Anonymous.

8. Harvard

• Price: \$1,481.
• Duration: 1 year 5 months.
• Format: Online, 9-course professional certificate.
• Prerequisites: None, but you should be comfortable learning R.
• Validity: No expiration.

HarvardX’s Data Science Professional Certificate is a long, structured program.

It’s built for people who want a deep foundation in statistics, R programming, and applied data analysis. It feels closer to a mini-degree than a short data science certification.

Why It Works Well

It’s backed by Harvard University, which gives it strong name recognition. The curriculum moves at a steady pace. It starts with the basics and later covers modeling and machine learning.

The program uses real case studies, which help you see how data science skills apply to real problems.

Here are the key features:

• University-backed professional certificate
• Case-study-based teaching
• Covers core statistical concepts
• Includes R, data wrangling, and visualization
• Strong academic structure and progression

What the Certification Covers

You learn R, data wrangling, visualization, and core statistical methods like probability, inference, and linear regression. Case studies include global health, crime data, the financial crisis, election results, and recommendation systems.

It ends with a capstone project that applies all the skills learned.

“I am SO happy to have completed my studies at HarvardX and received my certificate!! It’s been a long but exciting journey with lots of interesting projects and now I can be proud of this accomplishment! Thanks to the Kaggle community that kept up my spirits all along!” – Maryna Shut.

9. Open Group

• Price: \$1,100 for Level 1; \$1,500 for Level 2 and Level 3 (includes Milestone Badges + Certification Fee). Re-certification costs \$350 every 3 years.
• Duration: Varies by level and candidate; based on completing Milestones & board review.
• Format: Experience-based pathway (Milestones → Application → Board Review).
• Prerequisites: Evidence of professional data science work and completion of Milestone Badges.
• Validity: 3 years, with recertification or a new level required afterward.

Open CDS (Certified Data Scientist) is a very different type of certification because it is fully based on real experience and peer review. There is no course to follow and no exam to memorize for. You prove your skills by showing real project work and presenting it to a review board.

It’s built for people who want a credential that reflects what they have actually done, not how well they perform on a test.

Why It Works Well

This certification focuses on what you’ve actually done. It is respected in enterprise settings because candidates must show real project work and business impact. Companies also like that it requires technical depth instead of a simple multiple-choice exam.

Here are the key features:

• Peer-reviewed, experience-based certification
• Vendor-neutral and recognized across industries
• Validates real project work, not test performance
• Structured into multiple levels (Certified → Master → Distinguished)
• Strong fit for senior roles and enterprise environments

What the Certification Evaluates

It looks at your real data science work. You must show that you can frame business problems, work with different types of data, choose and use analytic methods, build and test models, and explain your results clearly.

It also checks that your projects create real business impact and that you can use common tools in practical settings.

How the Certification Works

Open CDS uses a multi-stage certification path:

• Step One: Submit five Milestones with evidence from real data science projects
• Step Two: Complete the full certification application
• Step Three: Attend a peer-review board review

Open CDS includes three levels of recognition. Level 1 is the Certified Data Scientist. Level 2 is the Master Certified Data Scientist. Level 3 is the Distinguished Certified Data Scientist for those with long-term experience and leadership.

“You fill a form and answer several questions (by describing them and not simply choosing an alternative), this package is reviewed by a Review Board, you are then interviewed by such board and only then you are certified. It was tougher and more demanding than getting my MCSE and/or VCP.” – Anonymous

10. CAP

• Price:
  • Application fee: \$55.
  • Exam fee: \$440 (INFORMS member) / \$640 (non-member).
  • Associate level (aCAP): \$150 (member) / \$245 (non-member).
• Duration: 3 hours of exam time (plan for 4–5 hours total, including check-in and proctoring).
• Format: Online-proctored or testing center, multiple choice.
• Prerequisites: CAP requires 2–8 years of analytics experience (based on education level), while aCAP has no experience requirements.
• Validity: 3 years, with Professional Development Units required for renewal.

The Certified Analytics Professional (CAP) from INFORMS is a respected, vendor-neutral credential that shows you can handle real analytics work, not just memorize tools.

It’s designed for people who want to prove they can take a business question, structure it properly, and deliver insights that matter. Think of it as a way to show you can think like an analytics professional, not just code.

Why It Works Well

CAP is popular because it focuses on skills many professionals find challenging. It tests problem framing, analytics strategy, communication, and real business impact. It’s one of the few certifications that goes beyond coding and focuses on practical judgment.

Here are the key features:

• Focus on real-world analytics ability
• Industry-recognized and vendor-neutral
• Includes problem framing, data work, modeling, and deployment
• Three levels for beginners to senior leaders
• Widely respected in enterprise and government roles

What the Certification Covers

CAP is based on the INFORMS Analytics Framework, which includes:

• Business problem framing
• Analytics problem framing
• Data exploration
• Methodology selection
• Model building
• Deployment
• Lifecycle management

The exam is multiple-choice and focuses on applied analytics, communication, and decision-making rather than algorithm memorization.

“As an operations research analyst … I was impressed by the rigor of the CAP process. This certification stands above other data certifications.” – Jessica Weaver

What Actually Gets You Hired (It’s Not the Certificate)

Certifications help you learn. They give you structure, practice, and confidence. But they don’t get you hired.

Hiring managers care about one thing: Can you do the job?

The answer lives in your portfolio. If your projects show you can clean messy data, build working models, and explain your results clearly, you’ll get interviews. If they’re weak, having ten data science certificates won’t save you.

What to Focus on Instead

Ask better questions:

• Which program helps me build real projects?
• Which one teaches applied skills, not just theory?
• Which certification gives me portfolio pieces I can show employers?

Your portfolio, your projects, and your ability to solve real problems are what move you forward. A certificate can support that. It can’t replace it.

How to Pick the Right Certification

If You’re Starting from Zero

Choose beginner-friendly programs that teach Python, SQL, data cleaning, visualization, and basic statistics. Look for short lessons, hands-on practice, and real datasets.

Good fits: Dataquest, IBM, Harvard (if you’re committed to the long path).

If You Already Work with Data

Pick professional programs that build practical experience through cloud tools, deployment workflows, and production-level skills.

Good fits: AWS, Azure, Databricks, DASCA

Match It to Your Career Path

Machine learning engineer: Focus on cloud ML and deployment (AWS, Azure, Databricks)

Data analyst: Learn Python, SQL, visualization, dashboards (Dataquest, IBM, CAP)

Data scientist: Balance statistics, ML, storytelling, and hands-on projects (Dataquest, Harvard, DASCA)

Data engineer: Study big data, pipelines, cloud infrastructure (AWS, Azure, Databricks)

Before You Commit, Ask:

• How much time can I actually give this?
• Do I want a guided program or an exam-prep path?
• Does this teach the tools my target companies use?
• How much hands-on practice is included?

Choose What Actually Supports Your Growth

The best data science certification strengthens your actual skills, fits your current level, and feels doable. It should build your confidence and your portfolio, but not overwhelm you or teach things you’ll never use.

Pick the one that moves you forward. Then build something real with what you learn.