Google · on Coursera

Google Advanced Data Analytics Professional Certificate

4.8(14,000) on Coursera·260K enrolled
Intermediate 197 hours English Professional CertificateFREE
SkillsPythonStatisticsRegressionMachine learningData analysisPredictive modelling

Is this course right for you?

Our take
The follow-on to Google's beginner Data Analytics certificate, moving into Python, statistics and machine learning. Audit is free; the certificate runs on a Coursera subscription.

Good for: people with data-analytics basics who want to move up towards data science.

Skip if: you are completely new to data, or you want the beginner Google Data Analytics certificate.

Seven courses shift from spreadsheets and SQL to Python for data wrangling, hypothesis testing, regression and ML, with every module framed around making business decisions and a capstone you present to a stakeholder. It targets the skills for senior-analyst and junior-data-scientist roles.

What it does not do is start from zero — it expects the core Data Analytics certificate or equivalent SQL and spreadsheet experience — so a complete beginner should do that first instead. Google keeps it updated; the certificate needs a subscription, and the free audit covers the learning.

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About this course

Google Advanced Data Analytics picks up where the core Google Data Analytics certificate leaves off. The curriculum shifts from spreadsheets and SQL to Python for data wrangling, statistical testing, and machine learning — covering regression models, hypothesis testing, and a final project that uses real workplace datasets. Seven courses, roughly 197 hours, at an intermediate pace.

Instructor

GC
Google Career Certificates
Coursera instructor
260K+ learners7 courses4.8 instructor rating

Taught by Google's Career Certificates team with contributions from Google data scientists and analysts who work on real business problems internally.

Frequently asked questions

It is aimed at people who already have data-analytics basics — ideally graduates of the original Google Data Analytics certificate or those with equivalent experience. You should be comfortable with SQL, spreadsheets and pivot tables, and the general analysis workflow. Prior Python is helpful but not strictly required, since the course introduces it gradually, though arriving with zero coding background makes the heavily Python-based later parts a steep climb.

The first certificate is a true beginner program built around spreadsheets, SQL, Tableau, and R for foundational analysis. This advanced one assumes you already have those basics and goes much further — deep into Python, statistics, regression, hypothesis testing, and machine learning. In short, the original teaches you to analyse data; the advanced teaches you to model and predict with it, which is a meaningfully higher-level skill set.

Yes, heavily — Python is the backbone of the program, with the bulk of the coursework built around it. You work with pandas and NumPy for data handling, and scikit-learn for machine learning, alongside statistics and predictive modelling. This is a large part of the certificate's value in 2026, since recruiters increasingly filter data-analyst applicants on Python, and the original Google cert does not demonstrate it.

For someone with the prerequisites aiming at higher-paying analyst or junior data-scientist roles, yes. It genuinely signals Python and machine-learning competence that the beginner certificate does not, which matters to 2026 recruiters. The value is real only if you have the groundwork and treat the capstone as a serious portfolio piece — going in without the basics, or coasting through, wastes what makes it worthwhile.

You can audit the courses on Coursera to watch the videos free, but the graded work, labs, and the certificate need a subscription — and since the hands-on Python practice is the point, the paid track is where the value sits. On time, Google suggests under six months at roughly ten hours a week, though real-world completion often runs closer to seven months at a steadier pace.
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