DATA WAREHOUSING & MINING

Paper Code: 
CBDA 313
Credits: 
3
Periods/week: 
3
Max. Marks: 
100.00
Objective: 

This Course enables the students to

  1.  Understand the basic data warehouse and data mining concepts.
  2.  Understand recent trends in data warehousing.
  3.  Understand architectural components of data warehouse.
  4.  Study various data mining techniques.

 

Course Outcomes (COs).

Course Outcome (at course level)

 

Learning and teaching strategies

Assessment Strategies

 
 

On completion of this course, the students will:

CO116. differentiate between operational and      decision support systems.

CO117. Explain the concepts and architecture of data warehouse.

CO118. Identify steps to implement a Data Warehouse.

CO119. Comprehend Data mining Techniques.

CO120. Identify applications of data mining in different domains.

 

Approach in teaching:

Interactive Lectures, Tutorials, Demonstrations,

Learning activities for the students:

Self-learning assignments, Quizzes, Presentations, Discussions

  • Assignment
  • Classroom activity
  • Multiple choice questions
  • Semester End Examination
 
 

 

9.00
Unit I: 

Need for strategic information, Decision support system, Operational versus Decision-Support Systems, Data Warehousing-the only solution, definitions of Data warehousing and data mining, features of Data warehouse, Data Marts, Metadata.

 

9.00
Unit II: 

Trends in Data Warehousing: significant trends and growth.

Planning Data warehouse, project team, project management considerations, information packages & requirements gathering methods and Requirements definition: Scope and Content.

 

9.00
Unit III: 

Architectural components: Objectives, Data Warehouse Architecture, Distinguishing Characteristics, Architectural Framework. Infrastructure: Operational & Physical.

 

9.00
Unit IV: 

Implementation of Data warehouse, ETL (Extract, Transform and Load in Data warehouse) Physical design: steps, considerations, physical storage, indexing, Data lake vs. Data warehouse

 

 

9.00
Unit V: 

Basics of data mining, related concepts, Data mining techniques, Data Mining Applications.

 

 

ESSENTIAL READINGS: 
  1. Paulraj Ponnian,”Data Warehousing Fundamentals”, John Wiley.
  2. Jiawei Han and Micheline Kamber, ―Data Mining Concepts and Techniques‖, Third Edition, Elsevier, 2012.

 

REFERENCES: 
  1. Jiawei Hen and Micheline Kamber, “ Data Mining Concepts and Techniques”
  2. Sima Yazdani, Shirley S. Wong, “Data warehousing with oracle”
  3. Han Kamber, Morgan Kaufmann, “Data Mining Concepts and Techniques”
  4. “Introduction to Business Intelligence and Data Warehousing”, PHI
  5. Ralph Kimball, “The Data Warehouse Lifecycle tool kit”, John Wiley.

 

E-RESOURCES:

 

JOURNALS:

 

Academic Year: 
2023-24
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