BMBIT05 Business Data Warehousing and Data Mining 4th Semester AKTU MBA Notes

Unit 1
Data Warehousing and its Business Value	VIEW
Introduction and Evolution of Data Mining, Goals of Data Mining, Myths about Data Mining, The Data Mining Process, Business Relevance	VIEW
Data Warehousing vs. OLTP Systems	VIEW
Roles of Data Warehousing in Business Decisions	VIEW
Roles of Data Mining in Business Decisions	VIEW
Basic Architecture and Components, Overall Architecture of Data Warehouse Systems, Layers: Staging, Integration, Access	VIEW
Enterprise Data Warehouse (EDW)	VIEW
Knowledge Discovery in Databases (KDD), Knowledge Extraction through Data Mining, Steps in KDD Process and Business Applications	VIEW

Unit 2
Data Warehouse Multidimensional Data Modeling: Star, Snowflake, and Fact Constellation Schema	VIEW
Dimensional modeling and its Business use Cases	VIEW
OLAP (Online Analytical Processing) Concepts of OLAP Cubes	VIEW
OLAP Operations: Roll-up, Drill-down, Slice, Dice, Pivot	VIEW
OLAP vs. OLTP	VIEW
OLAP Applications in Business Analytics	VIEW
ETL Processes: Data extraction, Transformation, and Loading	VIEW
Data Integration	VIEW
Metadata Management	VIEW
Data Quality and Warehouse Implementation Approaches, Methods for improving Data Quality	VIEW
Warehousing Architectures (Centralized, Federated, Real-Time), Challenges and Best Practices in implementation	VIEW

Unit 3
Data Pre-processing	VIEW
Data Exploration	VIEW
Data Preparation Techniques: Data cleaning, Integration, Transformation, Data reduction, Discretization, Concept Hierarchy	VIEW
Feature Engineering: Feature extraction	VIEW
Data Transformation for Mining	VIEW
Data Visualization	VIEW
Statistical Summaries, Data Summarization	VIEW
Data Visualization for business	VIEW
Data Visualization Issues and Challenges: High dimensionality, Scalability, Missing Values	VIEW

Unit 4
Data Mining Methods	VIEW
Association Rule Mining	VIEW
Mining Frequent Patterns	VIEW
Market Basket Analysis	VIEW
Apriori algorithm Mining	VIEW
Advanced Mining Techniques	VIEW
Constraint-based Mining	VIEW
Correlation Mining	VIEW
Introduction to Classification and Prediction in Data Mining	VIEW
Decision Tree Classifiers	VIEW
Bayesian Classifiers (Naïve Bayes)	VIEW
Support Vector Machines (SVM)	VIEW
Rule-Based Classifiers	VIEW
Regression for Prediction	VIEW
Model Evaluation Metrics (Confusion Matrix, Accuracy, Precision, Recall, F1-Score)	VIEW
Prediction Accuracy and Error Measures (MAE, MSE, RMSE)	VIEW
Ensemble Methods (Bagging, Boosting, Random Forests)	VIEW
Business Use Cases of Classification and Prediction	VIEW
Introduction to Clustering, K-Means Clustering Algorithm	VIEW
Hierarchical Clustering (Agglomerative, Divisive)	VIEW
Density-Based Clustering	VIEW
Grid-Based Clustering	VIEW
Clustering High-Dimensional Data	VIEW
Clustering Outlier Detection (Anomaly Detection)	VIEW
Clustering Applications in Customer Segmentation	VIEW
Clustering Applications in Targeted Marketing	VIEW
Clustering Applications in Fraud Detection	VIEW

Unit 5
Web, Text, Multimedia Mining, Concepts and Business Applications	VIEW
Spatial Data Mining, Techniques and Relevant Uses	VIEW
Temporal Data Mining, Techniques and Relevant Uses	VIEW
Business Intelligence and Case Studies	VIEW
Case Studies in CRM, Financial Analytics, Marketing, Social-Media, Retail, Insurance	VIEW
Trends in Data Mining:
Big Data	VIEW
Cloud Data Warehousing	VIEW
Real-time Analytics	VIEW
AI-driven Mining	VIEW
Data Mining Implementation	VIEW
Data Mining Ethics	VIEW
Data Mining Evaluation and Validation – Accuracy, Overfitting, Underfitting, Cross-Validation	VIEW
Business Integration, Aligning Mining Outcomes with Business Strategy, User Adoption and Deployment	VIEW
Privacy, Security, and Ethical Issues: Data Privacy Challenges, Security in Warehousing/Mining	VIEW
Regulations and Best Practices in Warehousing/Mining	VIEW
Ethical implications in Data Analysis and Usage	VIEW