ISACA Data Science Fundamentals (ITCA)

Many IT teams now handle data they were never trained to govern. That means PII in shared spreadsheets, reports nobody trusts, and analytics projects that stall between the business and IT. This course gives IT Directors a common data foundation to assign across the team before those gaps turn into compliance or decision-quality problems.

Role fit: The course is designed for a wide audience, from Excel-based analysts to programmers. Python examples and Google Colab notebooks are explained in simple terms, so non-developers aren’t blocked from working through the concepts.

Risk and compliance: Dedicated skills cover:

• Data governance, management, and compliance, including a PII reference resource
• Data ethics and professional codes of conduct
• Data access and protection

These help teams handle sensitive data consistently.

Time investment: Plan for 16 skills and about 12.5 hours per learner. That scope gives Training Managers and Team Leads a clear way to schedule the course without pulling staff off operations for extended blocks of time.

Change management: Skills build on each other in order. Learners are encouraged to revisit fuzzy concepts before moving on, so a fixed sequence works better than ad hoc viewing. The course closes with communicating insights to stakeholders, which strengthens how IT reports findings to the business.

Every skill ends with an ungraded, scenario-based challenge, often in Google Colab notebooks or spreadsheets. These let Team Leads see whether concepts landed before learners sit for the ISACA ITCA Data Science Fundamentals certification.

IT teams take this training to turn raw data into decisions the business can trust. Instead of relying on slides alone, instructor Jonathan Barrios works through use cases and optional Google Colab and Google Sheets challenges. Practitioners with a statistics or Excel background can follow every concept without writing code.

• Make sound storage and architecture calls: Your team works through a simulated data lake for an online retail business and a Data Corp scenario. These help practitioners judge when a data lake, a data warehouse, or an operational database fits the workload, including systems with different access patterns, storage needs, and time-sensitivity requirements.
• Reduce governance and privacy risk: Learners study data governance, PII handling, ethics codes of conduct, and data access and protection. The course includes a PII reference resource and use-case questions designed to help teams avoid common compliance pitfalls.
• Run analytics projects with a repeatable framework: Using the CRISP-DM model, teams learn to evaluate a model against real outcomes, decide whether to deploy it or refine it, and align the results with business processes.
• Communicate findings stakeholders act on: Practitioners learn presentation techniques and reporting tools, including business intelligence tools used to build weekly reports that expose operational bottlenecks.

The result is a team that shares a common data vocabulary, from sampling techniques to data cleaning, and can contribute to data-driven projects with less hand-holding.

Knowledge

• How data types, dimensions, and core data structures (linear and non-linear, such as graphs and trees) support analysis and machine learning work
• How populations, samples, and sampling techniques shape the validity of statistical conclusions
• The differences between operational databases, data lakes, data warehouses, and data management platforms, and when each fits a business need
• Data governance, compliance, and PII handling principles, along with data access and protection practices
• Data ethics guidelines and professional codes of conduct that maintain trust in an organization's data practice
• The CRISP-DM lifecycle, from business understanding through evaluation and deployment

Ability

• Form a hypothesis, then plan quantitative and qualitative data collection to test it
• Clean and pre-process (munge) raw data to prepare it for analysis and machine learning workflows
• Perform exploratory data analysis to manipulate data, visualize it, and uncover hidden relationships
• Work through hands-on Python examples in Google Colab, such as a simulated retail data lake. Programming experience is optional, because solution videos walk through each challenge
• Evaluate whether a model is ready to deploy or needs further refinement
• Present data science findings to stakeholders using clear presentation techniques and collaborative review of team-built tools