AKTU Business Data Warehouse and Data Mining (BMBIT05) Syllabus PDF – MBA 4th Semester

Course Objectives

• To understand the role and architecture of data warehouses in business intelligence.
• To develop multidimensional data models and explore OLAP operations for business analytics.
• To analyze preprocessing steps for mining and implement feature extraction methods.
• To apply various data mining techniques for classification, clustering, and association rule mining.
• To examine advanced mining areas including text, web, spatial, and multimedia mining.
• To evaluate ethical, legal, and implementation issues associated with data mining.

Unit - I: 6 hrs

Introduction to Data Warehousing and Data Mining: Fundamentals and Definitions: Data warehousing and its business value, Introduction and evolution of data mining, goals of data mining, Myths about data mining, The Data Mining process; Business Relevance - Data warehousing vs. OLTP systems, Roles of data warehousing and mining in business decisions; Basic Architecture and Components - Overall architecture of data warehouse systems, Layers: staging, integration, access, Enterprise Data Warehouse (EDW); Knowledge Discovery in Databases (KDD)

• Knowledge Extraction through Data Mining, Steps in KDD process and business applications

Unit - II: 7 hrs

Data Warehouse Modeling and Implementation: Multidimensional Data Modeling - Star, snowflake, and fact constellation schemas, Dimensional modeling and its business use cases; OLAP (Online Analytical Processing) - Concepts of OLAP cubes, OLAP operations: roll-up, drill-down, slice, dice, pivot, OLAP vs. OLTP, applications in business analytics; ETL Processes - Data extraction, transformation, and loading, Data integration and metadata management; Data Quality and Warehouse Implementation Approaches - Methods for improving data quality, Warehousing architectures (centralized, federated, real-time), Challenges and best practices in implementation

Unit - III: 7 hrs

Data Preprocessing and Exploration: Data Preparation Techniques - Data cleaning, integration, transformation, Data reduction, discretization, concept hierarchy; Feature Engineering -Feature extraction and transformation for mining; Visualization and Statistical Summaries -Data summarization, data visualization for business; Issues and Challenges - High dimensionality, scalability, missing values

Unit - IV: 10 hrs

Data Mining Methods: Association Rule Mining - Mining frequent patterns, market basket analysis, Apriori algorithm and advanced techniques, Constraint-based and correlation mining;

Classification and Prediction - Decision trees, Bayesian classifiers, SVM, rule-based classifiers, Regression, prediction accuracy, and evaluation, Ensemble methods and business use cases;

Clustering -Clustering algorithms: k-means, hierarchical, density-based, grid-based, Page 82 Clustering high-dimensional data, outlier detection, Applications in segmentation, targeting, fraud detection

Unit - V: 10 hrs

Advanced Mining Topics and Applications: Web, Text, and Multimedia Mining - Concepts and business applications; Spatial and Temporal Data Mining - Techniques and relevant uses; Business Intelligence and Case Studies -CRM, financial analytics, marketing, social media, retail, insurance;

Trends in Data Mining -Big Data, cloud data warehousing, real-time analytics, AI-driven mining Data Mining Implementation and Ethics: Evaluation and Validation - Accuracy, overfitting, underfitting, cross-validation; Business Integration - Aligning mining outcomes with business strategy, User adoption and deployment; Privacy, Security, and Ethical Issues - Data privacy challenges, security in warehousing/mining, Regulations and best practices, Ethical implications in data analysis and usage

Course Outcomes

Suggested Readings

• Ali ABM Shawkat and Wasimi Saleh A: Data Mining; Method and Technique, Cengage Publication
• L. T., Moss, S. Atre, A. Wesley: Business Intelligence Roadmap: The Complete Project Lifecycle for Decision- Support Applications, Information, Technology Series.
• Ralph Hughes: Agile Data Warehousing; Delivering World-Class Business Intelligence Systems Using Scrum and XP, Ceregenics Inc.
• S. Rizzi & M. Golfarelli: Data Warehouse Design; Modern Principles and Methodologies, Tata McGrawHill Education
• Chris Eaton, Dirk Deroos, Tom Deutsch et al., “Understanding Big Data”, McGrawHIll, 2012.
• Alberto Cordoba, “Understanding the Predictive Analytics Lifecycle”, Wiley, 2014.
• Eric Siegel, Thomas H. Davenport, “Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die”, Wiley, 2013.
• James R Evans, “Business Analytics – Methods, Models and Decisions”, Pearson 2013.
• R. N. Prasad, Seema Acharya, “Fundamentals of Business Analytics”, Wiley, 2015.
• S M Ross, “Introduction to Probability and Statistics for Engineers and Scientists”, Academic Foundation, 2011.
• David Hand, Heiki Mannila, Padhria Smyth, “Principles of Data Mining”, PHI 2013. COOPERATIVE MANAGEMENT (CM) GROUP