Data Analysis Basic Course (Self-Paced)

Module 1: Introduction to Data Analytics

• Understand the role of data analytics in modern organizations.
• Differentiate between data and information and recognize the importance of context.
• Identify how questions and data availability shape analytical approaches.

Module 2: Data Structures & Types

• Distinguish between structured and unstructured data.
• Understand tables, databases, rows, and columns.
• Recognize challenges in analyzing emails, images, PDFs, and other unstructured formats.

Module 3: Internal & External Data Sources

• Identify common internal systems (ERP, HR, POS, financial systems).
• Explore external data sources including government and partner data.
• Evaluate privacy, quality, and legal considerations when using external data.

Module 4: Transactional vs. Analytical Systems

• Compare transactional systems with data warehouses and data marts.
• Understand ETL (Extract-Transform-Load) concepts.
• Recognize how combining systems supports strategic decision-making.

Module 5: Data Quality & Governance

• Identify common data mismatches and transformation challenges.
• Understand data definitions, stewardship, and governance principles.
• Learn how poor governance can lead to operational failures.

Module 6: Data Privacy & Compliance

• Differentiate PII and PHI data types.
• Review major privacy regulations such as GDPR and CCPA.
• Apply best practices for handling sensitive data responsibly.

Module 7: Data Visualization

• Understand why visualization enhances learning and insight discovery.
• Differentiate between static and dynamic visualizations.
• Use visualization techniques to identify trends and outliers.

Module 8: Reporting & Analytics Tools

• Survey common tools such as Excel, Access, Tableau, and Power BI.
• Understand when to use visualization, statistical, or audit-specific tools.
• Recognize strengths and limitations of different analytics platforms.

Module 9: Excel for Data Analysis

• Apply sorting, filtering, and common math functions.
• Create pivot tables and pivot charts.
• Use Excel to answer basic business and audit questions.

Module 10: Analytical Techniques & Case Studies

• Perform stratification, duplicate detection, and normalization.
• Analyze vendor, employee, and transaction data.
• Apply techniques such as Benford’s Law, sampling, and date comparisons.

Module 11: AI & Emerging Trends in Data

• Understand the growth of big data and AI-driven analytics.
• Compare search engines and AI chat systems.
• Apply AI best practices and prompt fundamentals responsibly.