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IGNOU MCS-221 - Data Warehousing and Data Mining

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Data Warehousing and Data Mining

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IGNOU MCS-221 Code Details

  • University IGNOU (Indira Gandhi National Open University)
  • Title Data Warehousing and Data Mining
  • Language(s) English
  • Code MCS-221
  • Subject Computer Application
  • Degree(s) MCA (Revised)
  • Course Core Courses (CC)

IGNOU MCS-221 English Topics Covered

Block 1 - Data Warehouse Fundamentals and Architecture

  • Unit 1 - Fundamentals of Data Warehouse
  • Unit 2 - Data Warehouse Architecture
  • Unit 3 - Dimensional Modeling

Block 2 - ETL, OLAP and Trends

  • Unit 1 - Extract, Transform and Loading
  • Unit 2 - Introduction to Online Analytical Processing
  • Unit 3 - Trends in Data Warehouse

Block 3 - Data Mining Fundamentals and Frequent Pattern Mining

  • Unit 1 - Data Mining – An Introduction
  • Unit 2 - Data Preprocessing
  • Unit 3 - Mining Frequent Patterns and Associations

Block 4 - Classification, Clustering and Web Mining

  • Unit 1 - Classification
  • Unit 2 - Clustering
  • Unit 3 - Text and Web Mining
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IGNOU MCS-221 (January 2023 - July 2023) Assignment Questions

Q1. a) Compare and contrast operational database systems with a Data Warehouse. b) Define a Data Mart. What is the importance of Data Marts? Compare and contrast Data Mart with Data Warehouse. Q2. a) Along with the details, draw and illustrate Star, Snowflake and Fact Constellation schemas. Note: Give examples other than the ones discussed in the course material. b) Discuss the three-tier data warehouse architecture with a neat diagram. Q3. Discuss any two Use Cases of implementing Data Warehouse in organizations which includes their dimensional design, ETL, data quality, security aspects, dash boards, data mining techniques etc. Q4. a) Compare k-Means with k-Medoids algorithms for clustering with the help of suitable examples. b) How to evaluate clustering algorithms? Explain with suitable examples. Q5. a) What are key issues in hierarchical clustering? Explain them. b) Discuss Agglomerative Hierarchical clustering algorithm with an example. Q6. a) Explain discretization and concept hierarchy generation for numerical data. b) Why naive Bayesian classification is called “Naive”? Briefly outline the major ideas of naive Bayesian classification. Explain Naive-Bayes classification. Give an example to illustrate your answer. Q7. Describe the functionalities of Orange Data Mining tool. Discuss how it is used for developing, testing, and visualizing data mining workflows. Also illustrate a Use Case of it.
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