ISACA Data Science Fundamentals – ITCA — Lesson 1: Data Science Lifecycle, Business Questions, Data Types, Roles, and Ethics

Learning Objectives

• Explain the roles, information, criteria, dependencies, and decision points governing data-science lifecycle and business questions.
• Implement or assess data types, team roles, and data ethics using controlled evidence and acceptance criteria.
• Evaluate the condition where the planned data-science lifecycle result cannot be trusted because business questions scope, ownership, or prerequisite evidence is incomplete and select a proportionate corrective response.

Detailed Topics

• data-science lifecycle applied to the course scenario, expected behavior, stakeholders, controls, risks, and evidence.
• business questions applied to the course scenario, expected behavior, stakeholders, controls, risks, and evidence.
• data types applied to the course scenario, expected behavior, stakeholders, controls, risks, and evidence.
• team roles applied to the course scenario, expected behavior, stakeholders, controls, risks, and evidence.
• data ethics applied to the course scenario, expected behavior, stakeholders, controls, risks, and evidence.

Instructor Demonstration

The instructor traces a beginner data team turning raw information into reliable analysis while documenting quality, assumptions, ethics, and business value. The walkthrough connects frameworks, processes, tools, decisions, and evidence captured in the data-science project brief, prepared dataset, analytical notebook, and stakeholder presentation. It also tests the exception where the planned data-science lifecycle result cannot be trusted because business questions scope, ownership, or prerequisite evidence is incomplete.

Hands-On Exercise

Complete a controlled ISACA-aligned exercise covering Data Science Lifecycle, Business Questions, Data Types, Roles, and Ethics and preserve the verified result in the data-science project brief, prepared dataset, analytical notebook, and stakeholder presentation.

1. Confirm scope, roles, criteria, prerequisites, information sources, dependencies, and acceptance conditions.
2. Execute the governance, risk, audit, security, data, cloud, or technology workflow while recording decisions and evidence.
3. Test normal behavior, one boundary condition, and the stated exception using a controlled scenario.
4. Document findings, corrections, ownership, retest criteria, communication, and operational handover.

Scenario Analysis

• Identify which role, process, objective, control, data source, or decision most strongly influences data-science lifecycle.
• Compare two defensible responses and explain governance, risk, audit, security, privacy, operational, and business trade-offs.
• Determine how the data-science project brief, prepared dataset, analytical notebook, and stakeholder presentation should be approved, retained, reviewed, retested, and updated.

Troubleshooting Guidance

Start where expected behavior first differs from the observed result. Check scope, criteria, ownership, data quality, process sequence, policy alignment, version compatibility, dependencies, status, and evidence separately. Treat “the planned data-science lifecycle result cannot be trusted because business questions scope, ownership, or prerequisite evidence is incomplete” as a hypothesis until records, interviews, configurations, calculations, and retest outcomes confirm the cause.

Validation Checklist

• Required roles, processes, criteria, controls, information, approvals, and dependencies are present.
• The data-science project brief, prepared dataset, analytical notebook, and stakeholder presentation connects each conclusion to objective, reviewable evidence.
• Normal processing, a boundary condition, dependency behavior, and the stated exception were tested.
• Corrections, ownership, retest criteria, documentation, communication, and operational handover are complete.

Lesson Outcome

Learners can apply Data Science Lifecycle, Business Questions, Data Types, Roles, and Ethics, explain the resulting governance, risk, audit, security, data, or operational behavior, and defend the outcome through the data-science project brief, prepared dataset, analytical notebook, and stakeholder presentation.