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IBM Data Analyst Professional Certificate

4.6(31,000)·530K enrolled
Beginner 130 hours English Professional CertificateFREE

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.

The program is built around hands-on labs using Jupyter notebooks and real datasets, culminating in a capstone project where you work through the full analysis lifecycle and present a final report. IBM Skills Network provides the instruction, and the credential is recognised by many employers alongside the Google and Meta data certificates as a standard entry-level data credential.

This certificate works best for people who want to understand the full data analyst toolkit before specialising, or who are targeting roles at companies where IBM enterprise tools (Cognos, Db2) are in use. If you already know which tools your target employer uses, the Google Data Analytics or a platform-specific course might be more efficient.

What you'll learn

Import, clean, and analyse datasets using Python (pandas, NumPy, matplotlib)
Write SQL queries and work with relational databases including IBM Db2
Build dashboards in IBM Cognos Analytics and Microsoft Power BI
Perform data wrangling and feature engineering in Jupyter notebooks
Create a capstone data analysis project for your portfolio
Apply Excel for data manipulation, pivot tables, and chart creation

This course includes

130h
On-demand video
Yes
Certificate
Yes
Mobile access
English
Language
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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.

Requirements

  • No prior programming or data experience required
  • High school level mathematics
  • A computer with internet access and a modern browser

Who this course is for

  • Complete beginners targeting entry-level data analyst roles
  • Professionals in non-technical roles looking to move into data
  • Learners who want breadth across multiple tools before specialising
  • Those targeting companies that use IBM enterprise analytics tools

About this provider

CO
Coursera
University-backed online learning platform. 142M learners, 7,000+ courses from 325+ institutions.
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Frequently asked questions

No — they target different roles. IBM Data Analyst (this certificate) focuses on business intelligence, SQL, Excel, Power BI, and Python for analysis. IBM Data Science Professional Certificate goes deeper into machine learning, statistical modelling, and predictive analytics. Data Analyst is the right entry point if you want to work with existing data; Data Science if you want to build predictive models.
IBM estimates 11 months at 10 hours per week, but most focused learners complete it in 4–6 months. The content is self-paced on Coursera so you can adjust to your schedule.
For complete beginners, yes — it's one of the most comprehensive entry-level data analyst credentials available. IBM's brand carries weight with enterprise employers, and the multi-tool curriculum means you can speak to a range of data workflows in interviews. The certificate is less valuable if you already have Python or SQL experience.
Yes — the program covers both IBM Cognos Analytics and Microsoft Power BI for dashboard creation. It's one of the few beginner certificates that explicitly includes Power BI, which is increasingly the standard BI tool in enterprise environments.
IBM covers more tools (Python, SQL, Excel, Cognos, Power BI). Google is more focused on SQL, R, and Tableau, with stronger job placement infrastructure and a larger employer network behind the credential. If employer-facing job support matters more, Google; if tool breadth matters more, IBM.
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