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 Basics: Definitions, Types, and OLTP vs. OLAPUnit 1 of Data Warehousing and Data Mining introduces the core concepts of data warehousing, including its definition, purpose, architecture, types, and how it differs from operational databases (OLTP). It also covers the relationship between data warehousing and data mining, along with real-world applications and key 9 min readUnit 2
Data Warehouse Architecture & Modelling: Schemas, ETL, Star/SnowflakeUnit 2 of Data Wareousing and Data Mining: explores how data warehouses are built (architecture), how data is structured (dimensional modelling), and how operational data is transformed into analytical assets via ETL pipelines—with real-world examples from eSewa’s transaction analytics and Daraz’s inventory forecasting13 min readUnit 3
OLAP: Cubes, Drill-Downs, and Real-Time AnalyticsUnit 3 of Data Warehousing and Data Mining explores Online Analytical Processing (OLAP), covering multidimensional data models, OLAP operations (slice, dice, roll-up, drill-down), OLAP architectures (MOLAP, ROLAP, HOLAP), and their applications in business intelligence. This note includes visuals, real-world examples, 9 min readUnit 4
Data Preprocessing: Cleaning, Transforming & Feature EngineeringUnit 4 of Data Warehousing and Data Mining covers the critical preprocessing steps—data cleaning, integration, transformation, reduction, and discretization—that prepare raw data for mining. Learn why 80% of data science effort goes here, with real-world examples from eSewa fraud detection and Daraz recommendation syst11 min readUnit 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, association), and techniques—while linking them to real-world applications in Nepalese and global industries like eSewa, Daraz, and Ncell.11 min readUnit 6
Association Rule Mining: Algorithms, Metrics & ApplicationsUnit 6 of Data Warehousing and Data Mining explores how to discover hidden patterns in transactional datasets using association rules, covering support, confidence, lift metrics, Apriori and FP-Growth algorithms, and real-world applications in market basket analysis and recommendation systems.9 min readUnit 7
Classification in Data Mining: Algorithms, Models & 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 (SVM), with real-world applications in fraud detection, customer segmentation, and medical diagnosis.13 min readUnit 8
Prediction & Regression: Models, Algorithms & ApplicationsUnit 8 of Data Warehousing and Data Mining explores prediction vs. regression, linear/logistic regression mechanics, evaluation metrics (RMSE, R²), and real-world applications in finance, healthcare, and e-commerce—with worked examples using NEPSE stock prices, Daraz delivery delays, and bank loan approvals.7 min readUnit 9
Cluster Analysis · note coming
Unit 10
Complex Data Mining & Real-World ApplicationsUnit 10 of Data Warehousing and Data Mining explores mining unstructured data (text, images, video), spatial/temporal patterns, and real-world applications like fraud detection, recommendation systems, and social network analysis—with case studies from Nepalese and global tech.18 min read