ISACA ITCA: Data Science Fundamentals Online Training

Organizations collecting more data need IT teams that can interpret, protect, classify, and explain that data consistently — without turning every data request into a specialist escalation. This foundational ISACA Data Science Fundamentals (ITCA) course fits new IT Practitioners with about a year of IT experience and experienced team members who need to validate core data science knowledge. At roughly 12.5 hours per learner, it is realistic for onboarding plans or a short upskilling sprint.

For IT Directors and Training Managers, the value is standardization: shared vocabulary around data structures, statistical analysis, machine learning concepts, data storage, governance, compliance, ethics, access, and stakeholder communication. Change management is straightforward: assign it before teams take on data-heavy reporting, analytics, or governance work. CBT Nuggets Playlists can sequence the rollout, and Team Reporting helps Team Leads track completion across the group.

IT teams complete this training to build a common operating baseline for data-driven work across support, infrastructure, security, and reporting functions. The course connects foundational data science concepts to the practical work of managing, protecting, analyzing, and communicating with data.

• Improve decision support by helping IT Practitioners describe and summarize statistical data analysis instead of relying on unsupported assumptions.
• Reduce data handling risk by introducing governance, management, compliance, ethics, access, and protection concepts.
• Support cleaner data operations by comparing data management systems, data lakes, data warehouses, storage, collection, classification, and processing methods.
• Improve stakeholder communication by preparing teams to explain data science insights in a way business audiences can use.

Knowledge

• Foundational data science concepts and how data supports informed decisions.
• Characteristics, types, uses, and structures of data.
• Differences between statistics, analytics, and big data concepts.
• Core, linear, and non-linear data structures used in data science.
• Data governance, management, compliance, ethics, access, and protection concepts.
• Data management systems, data lakes, data warehouses, storage, and processing approaches.

Ability

• Explain machine learning fundamentals and related data types at an introductory level.
• Describe and summarize statistical data analysis.
• Compare data storage and management options in data science contexts.
• Identify data collection and classification methods.
• Discuss data mining and analysis frameworks.
• Communicate data science insights to stakeholders with clearer context and terminology.