Validates practical skills in data analysis, using spreadsheets, SQL, Tableau and Python to prepare and visualize data.
294EntryNo prerequisites
Best Data & Analytics Certifications in 2026
Data credentials cover analysis, business intelligence, data engineering and data management. Choose by the work you want to do and the tools your team uses: analysis and reporting, building pipelines or governing data.
Validates practical skills in Python, SQL, data analysis, visualization, machine learning and generative AI tools for data science.
236EntryNo prerequisites
Validates skills in preparing, modeling and visualizing data with Power BI, and managing and securing reports and models.
165IntermediateNo prerequisites
Validates foundational knowledge of ingesting, transforming, orchestrating, deploying, monitoring and governing data pipelines on the Databricks platform.
200IntermediateNo prerequisites
Validates foundational knowledge of Snowflake architecture, security and governance, data loading, query performance and transformation, and data sharing.
175EntryNo prerequisites
Validates skills in connecting to and transforming data, analyzing it with calculations, and building and publishing Tableau dashboards.
200EntryNo prerequisites
Validates practical skills in data analysis using Excel, SQL, Python and IBM Cognos, including data visualization and dashboards.
236EntryNo prerequisites
Validates knowledge of data ingestion, transformation, store management, pipeline operations, and data security and governance using AWS services.
150IntermediateNo prerequisites
Validates knowledge of data management, including governance, modeling, quality, metadata, architecture, integration and security, based on the DAMA-DMBOK.
311EntryHas prerequisites
Validates knowledge of designing, building and operating data processing systems on Google Cloud, from ingestion to analysis.
200AdvancedNo prerequisites
Validates foundational knowledge of core data concepts, relational and non-relational data, and analytics workloads on Azure.
99EntryNo prerequisites
Validates foundational data analytics skills across data concepts, acquisition and preparation, analysis, visualization, reporting and data governance.
264EntryNo prerequisites
Validates knowledge of preparing data and building semantic models in Microsoft Fabric, including governance, SQL, KQL and DAX.
165IntermediateNo prerequisites
Validates skills in building Microsoft Fabric pipelines, including ingestion and transformation with PySpark, SQL and KQL, and monitoring.
165IntermediateNo prerequisites
How to choose a certification
Which certification fits your goal?
- An introduction to analytics: Google Data Analytics. A beginner Coursera program of nine self-paced courses covering the analyst workflow, from asking questions to cleaning, analyzing and visualizing data, with a portfolio case study.
- Business intelligence: Power BI Data Analyst Associate. Microsoft's exam for Power BI analysts: preparing, modeling, visualizing and securing data with Power Query and DAX. Choose it if your team reports in Power BI.
- Data engineering on AWS: Data Engineer – Associate. Covers building and running data pipelines on AWS: ingestion, transformation, data stores and security. AWS recommends two to three years of data engineering experience.
- Data engineering on Databricks: Data Engineer Associate. An entry-level exam on building data pipelines on Databricks with Spark SQL, PySpark and Unity Catalog. Databricks recommends about six months of hands-on use.
- Data management and governance: CDMP. DAMA's certification in data management, including governance and data quality, based on the DAMA-DMBOK. Your score on the Fundamentals exam sets your level.
What to know before choosing
- Course certificates and exams differ. Google Data Analytics is a course you complete; the others are proctored exams. A course teaches the skills step by step, while an exam checks what you already know.
- Choose by the data job. Reporting, pipeline engineering, governance and machine learning use different skills. Choose the credential for the work you plan to do; for machine learning, see AI certifications.
- Keep an example of your work. A report, analysis or working pipeline shows how you apply what you learned. Include data quality checks and explain your decisions.