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Data Analysis with Python Certification

4.7(5,400) on freeCodeCamp·920K enrolled
Intermediate 300 hours English Course CertificateFREE
SkillsPythonPandasNumPyMatplotlibData analysisData visualisation

Is this course right for you?

Our take
Yes, if you already know some Python and want the data-analysis toolchain. It is free, the certificate is free, and you learn by building projects.

Good for: learners with some Python who want a free, practical route into data analysis.

Skip if: you are new to Python, or you want a guided, video-lecture course.

It moves from general Python into the libraries analysts actually use — NumPy, pandas, matplotlib — through five projects like analysing demographic and medical data. Paired with the Scientific Computing certification it is a complete free path from zero to applied analysis.

It assumes you can already program, so it is not a first Python course, and it is a focused toolchain rather than a full data-science programme — no SQL, statistics depth or machine learning here. If you want guided video lectures rather than project-based learning, look elsewhere. The curriculum is free and kept current (as of 2026).

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About this course

This certification moves from general Python programming into the specific libraries used for real data analysis work: NumPy for numerical computing, Pandas for data manipulation, and Matplotlib for visualization. Learners complete five real-world data analysis projects — including analyzing demographic data and medical examination data — that mirror the kind of work junior data analysts actually do.

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Instructor

FT
freeCodeCamp Team
freeCodeCamp instructor
920K+ learners12 courses4.7 instructor rating

Produced by freeCodeCamp, a nonprofit organization that has helped millions of people learn to code for free through project-based, certificate-backed curricula.

Frequently asked questions

Yes — this is data analysis with Python, not a course to learn Python itself. It assumes you can already write basic Python. If you can't yet, do freeCodeCamp's Scientific Computing with Python certification first, then come here for the analysis libraries.

The analyst's Python stack: pandas and NumPy for handling data, Matplotlib and Seaborn for charts, and reading data from sources like CSVs and SQL. It's the practical toolkit you'd actually use to explore and visualise a dataset, rather than a broad survey.

Five hands-on projects, which is how the certificate is earned — a mean-variance-standard-deviation calculator, a demographic data analyser, a medical data visualiser, a page-view time-series visualiser, and a sea-level predictor. The video lessons are optional; the projects are the real work.

It covers the Python-analysis half genuinely well, but most entry-level analyst roles also expect SQL, spreadsheets, and a business-intelligence tool like Tableau or Power BI. Treat this as a strong, free component of your skill set rather than the whole thing, and add those other pieces alongside it.

This is code-first and free — you learn by writing Python and building projects. Google's is broader and more structured, built around spreadsheets and Tableau with a recognised brand name, and friendlier if you'd rather not code much. Pick this if you want the Python route; pick Google if you want the no-code analyst path with a well-known certificate.

It's mostly tool-focused — how to use pandas and the visualisation libraries. You pick up analytical thinking through the projects, but it's light on statistics and the 'asking the right question' side of analysis. Pair it with some statistics if you want the reasoning as well as the tools.
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