Data Warehousing and Data Mining - Syllabus

Embark on a profound academic exploration as you delve into the Data Warehousing and Data Mining course () within the distinguished Tribhuvan university's CSIT department. Aligned with the 2065 Syllabus, this course (CSC-451) seamlessly merges theoretical frameworks with practical sessions, ensuring a comprehensive understanding of the subject. Rigorous assessment based on a 60+20+20 marks system, coupled with a challenging passing threshold of , propels students to strive for excellence, fostering a deeper grasp of the course content.

This 3 credit-hour journey unfolds as a holistic learning experience, bridging theory and application. Beyond theoretical comprehension, students actively engage in practical sessions, acquiring valuable skills for real-world scenarios. Immerse yourself in this well-structured course, where each element, from the course description to interactive sessions, is meticulously crafted to shape a well-rounded and insightful academic experience.

Course Synopsis: Analysis of advanced aspect of data warehousing and data mining.
Goal: This course introduces advanced aspects of data warehousing and data mining, encompassing the principles, research results and commercial application of the current technologies.


Unit 1

Concepts of Data Warehouse and Data Mining including its functionalities, stages of Knowledge discovery in database (KDD), Setting up a KDD environment, Issues in Data Warehouse and Data Mining, Application of Data Warehouse and Data Mining

Unit 2

DBMS vs. Data Warehouse, Data marts, Metadata, Multidimensional data model, Data Cubes, Schemes for Multidimensional Database: Stars, Snowflakes and Fact Constellations.

Unit 3

Data Warehouse Architecture, Distributed and Virtual Data Warehouse, Data Warehouse Manager, OLTP, OAP, MOLAP, HOLAP, types of OLAP, Servers.

Unit 4

Computation of Data Cubes, modeling: OLAP data, OLAP queries, Data Warehouse back end tools, tuning and testing of Data Warehouse.

Unit 5

Data Mining definition and Task, KDD versus Data Mining, Data Mining techniques, tools and application.

Unit 6

Data mining query languages, data specification, specifying knowledge, hierarchy specification, pattern presentation & visualization specification, data languages and standardization of data mining.

Unit 7

Mining Association Rules in Large Database: Association Rule Mining, why Association Mining is necessary, Pros and Cons of Association Rules, Apriori Algorithm.

Unit 8

Classification and Prediction: Issues Regarding Classification and Prediction, Classification by Decision Tree Induction, Introduction to Regression, Types of Regression, Introduction to Clustering, K-mean and K-Mediod Algorithms.

Unit 9

Mining complex Types of Data: Mining Text Databases, Mining the World Wide Web, Mining Multimedia and Spatial Databases.