Covers fundamental definition and meaning of data as raw facts and figures, distinguishing between qualitative (descriptive) and quantitative (numerical) data types for foundational understanding.

Explains the transformation process from raw data to structured information, then knowledge, and finally wisdom, illustrating how data gains value through processing.

Traces historical data practices from manual records and ledgers to modern big data, AI, cloud computing, and IoT applications.

Explores data characteristics, lifecycle, and comprehensive classification by structure (structured, semi-structured, unstructured), source (internal/external), usage (operational/analytical), and sensitivity (personal/confidential).

Identifies key qualities of high-quality data including accuracy, reliability, completeness, consistency, timeliness, relevance, validity, and uniqueness for effective decision-making.

Examines various data sources (internal vs. external, human vs. machine-generated) and collection methods including surveys, interviews, observations, sensors, and automated feeds.

Highlights data's role in decision-making, analytics, artificial intelligence, machine learning, and cybersecurity applications for strategic organizational advantage.

Introduces best practices for data storage, processing, management frameworks, and governance policies to ensure data integrity and compliance.

Emphasizes importance of data protection and overview of global data protection laws including GDPR and CCPA for responsible data handling.

Identifies current challenges like data overload, quality issues, and integration problems while exploring emerging trends in predictive analytics and AI-driven insights.

Connects theoretical knowledge to practical applications through case studies and explores future data-driven societies and technological developments.