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.
Units
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
DBMS vs. Data Warehouse, Data marts, Metadata, Multidimensional data model, Data Cubes, Schemes for Multidimensional Database: Stars, Snowflakes and Fact Constellations.
Data Warehouse Architecture, Distributed and Virtual Data Warehouse, Data Warehouse Manager, OLTP, OAP, MOLAP, HOLAP, types of OLAP, Servers.
Computation of Data Cubes, modeling: OLAP data, OLAP queries, Data Warehouse back end tools, tuning and testing of Data Warehouse.
Data Mining definition and Task, KDD versus Data Mining, Data Mining techniques, tools and application.
Data mining query languages, data specification, specifying knowledge, hierarchy specification, pattern presentation & visualization specification, data languages and standardization of data mining.
Mining Association Rules in Large Database: Association Rule Mining, why Association Mining is necessary, Pros and Cons of Association Rules, Apriori Algorithm.
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.
Mining complex Types of Data: Mining Text Databases, Mining the World Wide Web, Mining Multimedia and Spatial Databases.