IT274 Data Warehousing and Data Mining
Data Warehousing and Data Mining notes
9 chapter notes, in syllabus order. Each starts with the key points.
Unit 1
Data Warehousing: Core Concepts, Purpose & FoundationsUnit 1 of Data Warehousing and Data Mining introduces the why, what, and how of data warehouses: their definitions, purpose, architecture, and distinction from traditional databases, with real-world examples from Nepal’s e-commerce and banking sectors.8 min readUnit 2
Data Warehouse Architecture & Modelling: Schemas, ETL, and OLAPUnit 2 of Data Warehousing and Data Mining covers the foundational architecture of data warehouses (star schema, snowflake schema, fact/conglomerate schemas), ETL processes, and modelling techniques for OLAP systems, with real-world applications in business intelligence and analytics.15 min readUnit 3
OLAP: Cubes, Dimensions, Measures & Query TechniquesUnit 3 of Data Warehousing and Data Mining covers Online Analytical Processing (OLAP), including multidimensional data modeling, OLAP operations (slice, dice, drill-down), OLAP tools, and their applications in business intelligence. This note explains how OLAP cubes work, how to perform OLAP queries, and compares OLAP 12 min readUnit 4
Data Preprocessing · note coming
Unit 5
Data Mining: Definitions, Tasks, Techniques & Real-World ImpactUnit 5 of Data Warehousing and Data Mining introduces the core concepts of data mining—its definition, key tasks (classification, prediction, clustering, etc.), techniques (supervised vs. unsupervised learning), and real-world applications in Nepalese and global industries. This note explains how data mining extracts a12 min readUnit 6
Association Rule Mining: Algorithms, Metrics & ApplicationsUnit 6 of Data Warehousing and Data Mining covers how to discover hidden patterns in transactional data using association rules, including support, confidence, lift metrics, Apriori and FP-Growth algorithms, and real-world applications in market basket analysis and recommendation systems.11 min readUnit 7
Classification: Models, Algorithms & Real-World ApplicationsUnit 7 of Data Warehousing and Data Mining covers supervised learning techniques for classification, including decision trees, naive Bayes, neural networks, and support vector machines, with step-by-step examples, algorithm comparisons, and real-world applications in Nepalese and global industries.9 min readUnit 8
Prediction & Regression: Models, Algorithms & ApplicationsUnit 8 of Data Warehousing and Data Mining covers prediction vs. regression, linear vs. nonlinear models, polynomial regression, decision trees for regression, evaluation metrics (RMSE, MAE, R²), and real-world applications in finance, logistics, and healthcare—with visual step-byys, worked examples (e.g., predicting D12 min readUnit 9
Cluster Analysis: Techniques, Algorithms & ApplicationsUnit 9 of Data Warehousing and Data Mining explores unsupervised learning’s cluster analysis—how to group unlabeled data, key algorithms (K-means, hierarchical, DBSCAN), distance metrics, validation methods, and real-world applications in customer segmentation, fraud detection, and image compression.8 min readUnit 10
Complex Data Mining & Real-World ApplicationsUnit 10 of Data Warehousing and Data Mining explores advanced techniques for mining unstructured data (text, multimedia, streams, graphs), real-time analytics, and practical applications in business, healthcare, and social networks—with Nepalese and global case studies.12 min read