Lession plan dmw
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This document outlines a lecture plan for a subject on Data Warehousing and Mining (DMW). The plan covers 5 units over 46 lectures and 12 tutorials. Unit I introduces concepts of data warehousing and covers topics like requirements gathering, architecture, and infrastructure. Unit II focuses on data design, modeling, extraction, transformation, and loading of data. Unit III discusses information access, delivery, OLAP, and deployment. Unit IV introduces data mining algorithms, techniques, and applications. The final unit covers advanced topics in web mining, visualization, statistical analysis, and trends in data mining.
Lession plan dmw
- 1. SHRI RAWATPURA SARKAR INSTITUTE OF TECHNOLOGY, NEW RAIPUR LECTURE PLAN Name of the subject: _____DMW_________________________ L-T-P: ___L-T____________________ Department: ________CSE____________________________ Semester: ______8th_________________ Faculty Name: _______ VIVEK KUMAR SINHA _____________________________________ No. of Units: _____5_____ No. of Lecture required: __46______No. of Tutorial required: _____12___ Unit-I Lecture No. Topics to be covered 1 Overview and Concepts: 2 Need for data warehousing 3 Basic elements of data warehousing 4 Trends in data ware housing.: Architectural components,. 5 Planning and Requirements: 6 Collecting the requirements 7 Architecture And Infrastructure 8 Application of Data Mining (Tutorials) 9 Application of Data Mining (Tutorials) 10 Project planning and management 11 Infrastructure and metadata Unit-II Lecture No. Topic Covered 1 Data Design And Data Representation 2 Principles of dimensional modeling 3 Dimensional modeling 4 advanced topics 5 data extraction(Tutorials) 6 transformation 7 loading(Tutorials) 8 data quality 9 Application of data Modeling(Tutorials) Unit-III Lecture No. Topic Covered 1 Information Access and Delivery 2 Matching information to classes of users 3 OLAP in data warehouse (Tutorials)
- 2. 4 Data warehousing and the web 5 Implementation And Maintenance (Tutorials) 6 Physical design process , growth and maintenance 7 data warehouse deployment Unit-IV Lecture No. Topic Covered 1 Data Mining: Introduction: 2 Basics of data mining 3 related concepts of data mining 4 Data Mining Algorithms: Classification 5 Application : case study(Tutorials) 6 Application : case study(Tutorials) 7 Application : case study(Tutorials) 8 Data mining techniques 9 Clustering 10 Association rules. Knowledge Discovery: 11 KDD Process Unit-V Lecture No. Topic Covered 1 Web Mining: Web Content Mining, Web Structure Mining 2 Analytical characterization: analysis of attribute relevance, Data Mining Primitives 3 Advanced Topics: Spatial mining (Tutorials) 4 Visualization : Data generalization and summarization-based characterization 5 Mining class comparisons: Discriminating between different classes, Mining descriptive statistical measures in large databases (Tutorials) 6 Query language, Designing GUI based on a data mining query language 7 Architectures of data mining systems Application and Trends in Data Mining: Application 8 Systems products and research prototypes 9 Additional themes in data mining, Trends in data mining VIVEK KUMAR SINHA HOD (Ass. Prof CSE)