📚 Syllabus & Course Curriculum
Data Science & Analytics
Module-by-module breakdown of Introduction to Data Mining & Warehousing, from foundations to a certified capstone project.
Architecture
• OLTP versus OLAP workloads and why they need different structures
• Dimensional modelling: facts, dimensions, star and snowflake schemas
• Slowly changing dimensions and preserving history correctly
Pipelines
• Extract, transform, load versus ELT in modern cloud warehouses
• Incremental loading, idempotency and late-arriving data
• Data quality testing and the cost of discovering errors downstream
Querying
• Aggregation, window functions and analytical query patterns
• Partitioning, clustering and query cost control
• Materialised views and pre-aggregation trade-offs
Mining
• Association rule mining and interpreting support, confidence and lift
• Clustering and segmentation on warehouse data
• Classification and prediction using warehouse features
Governance
• Lineage, cataloguing and documentation of derived tables
• Access control, masking and privacy in a central warehouse
• Avoiding metric divergence when every team defines revenue differently
Earn government-registered certification in Introduction to Data Mining & Warehousing
e-Certificate and e-Marksheet issued on successful completion.
