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IBM Data Science Professional Certificate

4.6(102,000) on Coursera·950K enrolled
Beginner 110 hours English Professional CertificateFREE
SkillsPythonSQLData analysisData visualisationMachine learning basicsJupyterPandas

Is this course right for you?

Our take
One of the stronger entry credentials for switching into data science, with more real Python depth than most beginner certificates. Audit is free; the certificate runs on a Coursera subscription.

Good for: beginners who want a hands-on route into data science with a recognised certificate.

Skip if: you already know Python and SQL, or you want advanced, specialised material.

Across twelve courses it builds the full toolkit from no coding — Python, SQL, analysis and visualisation, and hands-on machine learning — ending in a capstone you can show. The newer version adds generative AI and a career course, so it tracks how the field is moving.

It will not make you a senior data scientist on its own, and it is broad rather than deep, so pair it with your own projects. If you already know Python and SQL, or you want advanced material, start higher up. IBM keeps it current (as of 2026).

Comparison · LBS

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About this course

The IBM Data Science Professional Certificate is a 12-course program that most people finish in several months — often four to six at a few hours a week. You can audit the lessons free; the certificate runs on Coursera's monthly subscription, so finishing faster costs less.

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Instructor

I
IBM
Coursera instructor

Created by IBM's data-science team and taught by IBM practitioners. The program reflects the tools and workflow IBM uses on real projects, from Python notebooks to deployment.

Frequently asked questions

They aim at different roles. IBM's is more technical — it teaches Python programming, databases and, now, some AI, heading toward data science. Google's leans on spreadsheets, SQL, Tableau and R for an entry-level analyst job. Choose IBM if you want to code and lean toward data science; choose Google if you want a broader, less code-heavy analyst start.

It's a long programme — around twelve courses — so expect several months, commonly four to six at a steady pace. It's genuinely more content than most single certificates, which is part of its value, but go in knowing it's a real commitment, not a weekend project.

You work in Python with Jupyter, pandas and SQL, do data visualisation, and yes — it covers building and evaluating machine-learning models with scikit-learn, plus a capstone project. Recent versions also fold in generative-AI tools for data work. It's a broad, hands-on toolkit rather than a deep dive into any one area.

Not heavily. It's more about coding and applying tools than deriving statistics, so comfort with basic arithmetic and a willingness to think logically is enough to start. The deeper statistical and mathematical grounding matters more later, if you move from entry-level work toward serious data science.

Be realistic: despite the name, finishing it puts you at junior-analyst-to-entry-data-role level, not full data scientist. That's still a genuine, valuable place to reach from scratch. 'Data scientist' proper usually needs stronger statistics, more advanced ML, and real project experience beyond any single certificate.

It's a credible entry-level credential and one of the more technical ones, but on its own it rarely lands a job. Employers look hardest at the projects you build along the way — so treat the certificate as proof you did the work, and your project portfolio as the thing that actually gets you interviews.
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