ISACA Data Science Fundamentals – ITCA — Lesson 2: Data Collection, Cleaning, Quality, Exploration, Statistics, and Features

Learning Objectives

• Explain the roles, information, criteria, dependencies, and decision points governing data collection and data cleaning.
• Implement or assess data quality, exploratory analysis, and feature preparation using controlled evidence and acceptance criteria.
• Evaluate the condition where a control passes a basic check but exploratory analysis fails because data quality data, criteria, or dependency assumptions differ and select a proportionate corrective response.

Detailed Topics

• data collection applied to the course scenario, expected behavior, stakeholders, controls, risks, and evidence.
• data cleaning applied to the course scenario, expected behavior, stakeholders, controls, risks, and evidence.
• data quality applied to the course scenario, expected behavior, stakeholders, controls, risks, and evidence.
• exploratory analysis applied to the course scenario, expected behavior, stakeholders, controls, risks, and evidence.
• feature preparation 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 a control passes a basic check but exploratory analysis fails because data quality data, criteria, or dependency assumptions differ.

Hands-On Exercise

Complete a controlled ISACA-aligned exercise covering Data Collection, Cleaning, Quality, Exploration, Statistics, and Features 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 collection.
• 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 “a control passes a basic check but exploratory analysis fails because data quality data, criteria, or dependency assumptions differ” 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 Collection, Cleaning, Quality, Exploration, Statistics, and Features, 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.