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IBM Data Analyst Professional Certificate

4.6(99,000) on Coursera·560K enrolled
Beginner 170 hours English Professional CertificateFREE
SkillsExcelSQLPythonData visualisationPower BIData analysis

Is this course right for you?

Our take
A broad, hands-on route into data analysis with a recognised certificate, and a common alternative to Google's. Audit is free; the certificate runs on a Coursera subscription.

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

Skip if: you already know SQL and Excel, or you want advanced data science.

Where Google's certificate goes deeper on a few tools, IBM's covers more of them — Python, SQL, Excel, Cognos, Power BI and visualisation across eleven courses — through Jupyter labs and a capstone. The trade-off is breadth over depth: you meet the whole toolkit but spend less time on each tool.

It does not go deep enough to specialise, so if you already know SQL and Excel, or you know which tools your target employer uses, a focused course is more efficient. Pair it with your own projects. IBM keeps it updated; the certificate needs a subscription, and the free audit covers the learning.

Comparison · LBS

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About this course

The IBM Data Analyst Professional Certificate is the most tool-comprehensive beginner data program on Coursera. Where the Google Data Analytics certificate focuses on depth in SQL, R, and Tableau, IBM's version covers more ground: Python, SQL, Excel, IBM Cognos, Power BI, and data visualisation across 11 courses. The tradeoff is breadth over depth — you get exposure to a wider toolkit but spend less time on any individual tool than a specialist course would.

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Instructor

IS
IBM Skills Network
Coursera instructor
530K+ learners11 courses4.6 instructor rating

Delivered by IBM's Skills Network team — a group of IBM data professionals and instructional designers who build curriculum aligned with IBM's enterprise data and analytics practices.

Frequently asked questions

A genuinely technical stack for a beginner program. You start with spreadsheets and Excel, move into SQL for querying databases, then Python with the pandas library for cleaning and analysis, and finish with IBM Cognos and some dashboarding for visualisation. That lean toward SQL and Python — rather than staying spreadsheet-only — is the main reason people choose it over gentler alternatives, and also why it feels harder early on.

They suit different starting points. Google's is gentler and built around spreadsheets, SQL, R, and Tableau, which makes it kinder for someone with no technical background at all. IBM's pushes harder into SQL and Python, so it feels closer to actual analyst work but is a steeper climb if you have never written code. Neither is 'better' outright — it depends on how much difficulty you want up front.

On its own, usually not, and it helps to be clear-eyed about that. The IBM name and the hands-on labs get a resume taken seriously, but hiring managers still want a portfolio of real analysis and interview-ready SQL you can perform under pressure. Completion rates are also low — many enrol, far fewer finish — so actually completing it plus building your own projects is what turns it into something that moves the needle.

IBM markets it as roughly four months at a few hours a week, which is achievable only if you keep a steady pace and already have some comfort with computers. If you are working alongside it or completely new to coding, five to six months is the more honest figure, because the Python and SQL courses genuinely reward the extra practice rather than a rushed pass.

Yes, on both counts. It comes with an ACE credit recommendation worth up to twelve US college credits at participating institutions, which can matter if you later pursue a degree. It also issues a verifiable Credly digital badge — a link an employer can click to confirm you genuinely earned it, rather than having to take the line on your resume on trust.

It gives you working familiarity rather than mastery, and many finishers say exactly that. The SQL and Python stay fairly introductory, so before you sit technical screens you should practise separately — joins, aggregations, and window functions in SQL, and cleaning genuinely messy datasets in Python. The certificate gets you conversant; targeted practice afterwards is what gets you through the harder interview questions.

No. It is genuinely beginner level and assumes only basic computer literacy and high-school maths — no statistics, no programming beforehand. What helps most is a bit of patience and curiosity about working with data, because the jump from spreadsheets into SQL and then Python is where motivation matters more than any prior qualification you might be missing.
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