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Full catalog
All Data & Analytics Courses
Every Data & Analytics course in the CBT Nuggets catalog.
Frequently asked questions about Data & Analytics training
Common questions IT directors ask when evaluating Data & Analytics training for their team.
Which roles and experience levels does the Data & Analytics path cover?
CompTIA Data+ and ISACA Data Science Fundamentals suit beginners, from Excel-based analysts to ops staff taking on reporting. PL-300 fits analysts with about a year of analytics experience, and DP-600 targets analytics engineers and BI administrators. AWS DEA-C01 is for data engineers, cloud engineers, and DBAs with several years of data experience.
Which certifications can an IT Director prepare the team for with these courses?
The path prepares learners for CompTIA Data+ (DA0-002), Microsoft Power BI Data Analyst Associate (PL-300), Microsoft Fabric Analytics Engineer Associate (DP-600), AWS Certified Data Engineer – Associate (DEA-C01), and ISACA Data Science Fundamentals. The vendor issues each certification; CBT Nuggets prepares the learner for it.
How should a Training Manager sequence this path across a mixed data team?
Start everyone on CompTIA Data+ or ISACA Data Science Fundamentals so the team shares one approach to data quality and governance. Then route report builders to PL-300, Fabric administrators and analytics engineers to DP-600, and pipeline engineers to AWS DEA-C01. Add Programming for Data Science for staff moving from spreadsheets to Python.
How does this training reduce governance and compliance risk?
Governance appears at every level: PII handling and data governance in ISACA Data Science Fundamentals, privacy and compliance in Data+, workspace controls and content security in PL-300, tenant settings and sensitivity labels in DP-600, and security and governance for AWS pipelines in DEA-C01. That gives auditors a consistent, explainable standard across roles.
Is there hands-on practice beyond watching videos?
Yes. Programming for Data Science walks learners through setting up Anaconda, Jupyter, and Git/GitHub, so Team Leads can standardize environments across the group. ISACA Data Science Fundamentals explains its Python examples in Google Colab notebooks so non-developers can work through them.
How much time per learner should we plan, and how is progress tracked?
Time varies by course and role; for example, PL-300 runs about 8.6 hours across 11 skills, and most learners finish in two to three weeks alongside regular work. Team reporting shows per-learner progress and completion, cohort and group views, and completion exports for audit evidence.