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Tribhuvan university CSIT 2065 Syllabus Data Warehousing and Data Mining

Data Warehousing and Data Mining - Syllabus

Course Overview and Structure
Syllabus Notes Old Questions Unit Wise Questions Question Bank

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.

Units

Key Topics

  • Compiler Structure
    UN-1.1

    Analysis and Synthesis Model of Compilation, including different sub-phases within analysis and synthesis phases.

  • Compiler Concepts
    UN-1.2

    Basic concepts related to Compiler, including interpreter, simple One-Pass Compiler, preprocessor, macros, symbol table, and error handler.

  • Institutional Infrastructural Preparedness
    UN-1.3

    Institutional infrastructural preparedness refers to the readiness of government agencies and institutions to adopt and implement e-governance systems.

  • Human Infrastructural Preparedness
    UN-1.4

    Human infrastructural preparedness involves the development of skills and capacities of public officials and citizens to effectively use e-governance systems.

  • Technological Infrastructural Preparedness
    UN-1.5

    Technological infrastructural preparedness refers to the availability and quality of technology infrastructure, including computers, internet connectivity, and other digital tools.

Key Topics

  • E-readiness
    UN-1

    E-readiness refers to the state of preparedness of a country or organization to participate in the digital economy. It involves assessing the availability and quality of digital system infrastructure, legal frameworks, institutional arrangements, human resources, and technological capabilities.

  • Evolutionary Stages in E-Governance
    UN-2

    The evolutionary stages in e-governance refer to the different phases of development and implementation of e-governance initiatives, from basic online presence to integrated and transformative e-governance systems.

  • Internetworking
    UN-3

    Bridges and routers in distributed networking, enabling communication between different networks.

  • Internet Design and Evolution
    UN-4

    History and development of the internet, including its design principles and evolution over time.

  • Data Cubes
    UN-5

    A multidimensional representation of data, where each dimension represents a different aspect of the data, used for fast querying and data analysis.

  • Schemes for Multidimensional Database
    UN-6

    Different schemes used to design and implement multidimensional databases, including Stars, Snowflakes, and Fact Constellations.

  • Stars
    UN-7

    A type of multidimensional database scheme, characterized by a central fact table surrounded by dimension tables.

  • Snowflakes
    UN-8

    A type of multidimensional database scheme, characterized by a central fact table surrounded by multiple levels of dimension tables.

  • Fact Constellations
    UN-9

    A type of multidimensional database scheme, characterized by multiple fact tables connected by dimension tables.

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.

Key Topics

  • Introduction to Virtual Reality
    UN-5.1

    This topic covers the fundamental concepts and principles of Virtual Reality (VR), including its history, applications, and key technologies.

  • Introduction to Animation
    UN-5.2

    This topic provides an overview of the basics of animation, including its history, types, and key concepts, as well as its applications in computer graphics.

  • Automatic Storage Management
    UN-5.3

    Automatic storage management is a feature that automates the management of database storage, including disk space allocation and deallocation. This topic covers the concepts and best practices of automatic storage management.

  • RMAN (Recovery Manager)
    UN-5.4

    RMAN is a utility provided by Oracle for backing up, restoring, and recovering databases. This topic covers the features, benefits, and usage of RMAN in database administration.

  • Data Mining Applications
    UN-5.5

    Examining the various applications of Data Mining in different industries, including marketing, finance, and healthcare. Understanding the benefits and challenges of Data Mining in real-world scenarios.

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.

Key Topics

  • Classification and Prediction Issues
    UN-8.1

    Discussion of common issues and challenges in classification and prediction, including data quality, class imbalance, and overfitting.

  • Classification by Decision Tree Induction
    UN-8.2

    Introduction to decision tree induction as a method for classification, including how to construct and prune decision trees.

  • Introduction to Regression
    UN-8.3

    Overview of regression analysis, including simple and multiple regression, and its applications in data mining.

  • Types of Regression
    UN-8.4

    Exploration of different types of regression, including linear, logistic, and nonlinear regression.

  • Introduction to Clustering
    UN-8.5

    Fundamentals of clustering, including types of clustering, clustering algorithms, and applications in data mining.

  • K-Mean and K-Mediod Algorithms
    UN-8.6

    In-depth look at K-Mean and K-Mediod algorithms, including how they work, advantages, and limitations.

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