MCS-221 Solved Assignment 2024-25

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MCS-221 Solved Assignment 2024-25 Available

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. (8 Marks)
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.