The IBM Data Analyst Professional Certificate is ten courses covering what an entry level analyst is expected to touch: spreadsheets, SQL, Python, pandas, data cleaning, visualisation and dashboards, finishing with a capstone project. It sits directly against the Google Data Analytics certificate in the market and the comparison is unavoidable, so let me start there. Google's is better taught. The instruction is clearer, the pacing is better judged, the production is current, and the pedagogical decisions are more considered.
IBM's advantage is that it goes further into Python and pandas, where Google leans harder on spreadsheets, SQL and Tableau. If you know you want to end up writing Python, IBM gets you closer to that. If you want the strongest general grounding and the better learning experience, Google wins. The Python and pandas courses here are the strongest part.
They are pitched properly at beginners, the labs run in a browser environment with no installation, and by the end you can load a messy CSV, clean it, group it, and produce a chart from it without stopping to look up basic syntax. That is a real skill and quite a few analyst certificates skip it. The SQL course is fine and covers joins, aggregation and subqueries at a level that will handle most day to day questions. The IBM Cloud dependency is my main structural complaint.
Substantial portions of the course run inside IBM's own environment and use Cognos Analytics for dashboarding. Cognos is real enterprise software with real customers, and if you go into the job market and look at analyst postings, you will see Tableau and Power BI and increasingly Looker, and you will see Cognos rarely. Time spent learning Cognos is time not spent learning a tool a hiring manager cares about. The concepts transfer, and the specific fluency does not, and the specific fluency is what gets you through a technical screen.
Production quality is uneven. Some modules feel current and others were clearly filmed some years ago, with interface screenshots that no longer match the products. Nothing is wrong exactly, and it undermines confidence when the screen you are looking at does not match the screen the video is describing. The assessments are the usual Coursera problem.
Multiple choice quizzes that test whether you watched the video, peer reviewed assignments where the quality of feedback depends entirely on who marks you. The capstone is better and asks you to run an actual analysis end to end, and it is the piece worth spending real effort on because it is the only part that produces evidence of ability. Statistics coverage is thinner than an analyst certificate should have. You get descriptive statistics and some visualisation guidance and not much on sampling, confidence, significance or the ways an analysis can mislead.
Analysts are asked whether a difference is real more often than they are asked to make a chart, and this certificate prepares you better for the chart. On the certificate's market value, be realistic. It is a credential from IBM delivered by Coursera and hiring managers have seen thousands of them. It will not hurt you and it will not distinguish you.
The portfolio you build while doing it is the asset. Take the capstone seriously, put it on GitHub with a proper README explaining the question and the method, and that artefact will do more work than the PDF. My three point six is for broad, cheap, adequate coverage that is dragged down by vendor lock in and dated delivery. Good value on a monthly subscription if you finish in three months.
Take Google's for the better teaching, and come here afterwards if you want the extra Python.