IGNOU MCA-NEW MCS 221 Data Warehousing and Data Mining Solved Assignment ENGLISH

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MCS 221 (July 2026 - January 2027) - ENGLISH

Course Code : MCS-221
Course Title : Data Warehousing and Data Mining
Assignment Number : MCA_NEW/MCAOL(II)/221/Assign/2026-27Maximum Marks : 100
Weightage : 30%
Last Date of Submission : 31st October, 2026 (For July, 2026 Session)
15th April, 2027 (For January, 2027 Session)
Note: This assignment has ten questions. All the questions are compulsory and there is no choice. Rest 20 marks are for viva voce. You may use illustrations and diagrams to enhance the explanations. Please go through the guidelines regarding assignments given in the Programme Guide.
Q1: A university intends to build an enterprise data warehouse by integrating admissions, examinations, finance and LMS databases. Explain the need for a data warehouse, discuss its characteristics, and
justify whether the Inmon or Kimball approach is more suitable. 

Q2: Illustrate a three-tier data warehouse architecture for a healthcare organization. Explain the role of
data sources, staging area, ETL, metadata repository, warehouse, OLAP server and front-end tools.  
Q3: A supermarket chain wants to analyse monthly sales, customer behaviour and product performance. Design a dimensional model identifying fact table(s), dimensions, measures and appropriate schema. Justify your design.

Q4: Explain the complete ETL process for integrating data from e-commerce, ERP and CRM systems into a data warehouse. Discuss common data quality issues and suitable transformation strategies.
  Q5: Compare OLTP and OLAP systems with suitable examples. Discuss OLAP operations (roll-up, drill-down, slice, dice and pivot) and explain how they support managerial decision making.
Q6: A financial institution has noisy, missing and inconsistent customer data. Explain suitable data preprocessing techniques including cleaning, integration, transformation, reduction and discretization before mining.

Q7: Explain the working of association rule mining using an illustrative transaction dataset. Describe the Apriori algorithm and interpret support, confidence and lift for generated rules. 

Q8: Compare decision tree, Naïve Bayes, k-nearest neighbour and support vector machine classifiers. Recommend the most appropriate algorithm for credit-risk prediction with justification.

Q9: Differentiate partitioning, hierarchical and density-based clustering techniques. Discuss the working of K-means and DBSCAN and compare their suitability for real-world applications involving noisy data. 

Q10: Discuss emerging trends in data warehousing and data mining such as cloud data warehouses, big data analytics, text mining, web mining and data stream mining. Explain how these technologies enhance business intelligence. 

MCS 221 (January 2025 - July 2025) - ENGLISH

Course Code :MCS-221

Course Title :Data Warehousing and Data Mining

Assignment Number :MCA_NEW(II)/221/Assign/2024-25

Maximum Marks100

Weightage30%

Last Date of Submission

31st October, 2024 (For July, 2024 Session) 15th April, 2025 (For January, 2025 Session)

This assignment has ten questions. All the questions are compulsory and there is no choice. Rest 20 marks are for viva voce. You may use illustrations and diagrams to enhance the explanations. Please go through the guidelines regarding assignments given in the Programme Guide.

Q1: Discuss the role of ETL (Extract, Transform, Load) processes in data warehousing. Provide a detailed explanation of each phase and its importance. Illustrate your answer with examples of common tools used in ETL and the challenges that may arise during these processes.

Q2: (a) Explain the concept of Data Warehousing architecture. Compare and contrast the different types of architectures such as Single-tier, Two-tier, and Three-tier. Provide examples of scenarios where each architecture might be most beneficial.

(b) Analyze the concept of OLAP (Online Analytical Processing) and its significance in data warehousing. Describe the differences between MOLAP, ROLAP, and HOLAP. Discuss the advantages and disadvantages of each type with respect to data analysis and querying performance.

Q3: Design a data warehouse schema for a retail company. Include fact tables, dimension tables, and consider the star schema and snowflake schema designs. Justify your design choices and discuss how your schema supports efficient query processing and business intelligence needs.

Q4: Explain the use of metadata in data warehousing. Discuss the different types of metadata and their roles. Provide examples of how metadata can enhance the usability, maintenance, and performance of a data warehouse.

Q5: Evaluate the role of data warehousing in supporting business intelligence and analytics. Discuss the process of transforming raw data into actionable insights. Provide examples of business intelligence tools and techniques that leverage data warehousing to enhance decision-making processes.

Q6: Analyze various data pre-processing techniques such as data cleaning, data integration, data transformation, and data reduction. Explain the significance of each technique in improving the quality of data for mining and provide examples of scenarios where each technique would be applied.

Q7: Compare and contrast the various classification algorithms used in data mining, such as Decision Trees, Naive Bayes, Support Vector Machines, and Neural Networks. Discuss the strengths and weaknesses of each algorithm and provide examples of appropriate use cases for each.

Q8: Evaluate the different clustering techniques, including K-means, hierarchical clustering and DBSCAN. Explain the underlying principles of each technique, and discuss their advantages, limitations, and practical applications.

Q9: Examine the role of association rule mining in data mining. Describe the Apriori algorithm and its variations. Discuss the challenges associated with association rule mining, such as the generation of large numbers of rules and the need for efficient computation.

Q10: Analyze the role of feature selection and dimensionality reduction in data mining. Discuss techniques such as Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA), and feature selection algorithms. Explain how these techniques help in improving model performance and reducing computational complexity.

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