MCS-226 Solved Assignment 2026

Description

MCS-226 Solved Assignment 2026 Available

Q1: Explain the scope and objectives of Data Science. Discuss the importance of data collection and sampling techniques in data-driven decision making. Illustrate your answer with suitable examples.

Q2: Define measures of central tendency and dispersion. Explain how variance and standard deviation help in understanding data distribution. Discuss the relevance of these measures in Data Science applications.

Q3: What is data preprocessing? Explain the major steps involved in preprocessing large datasets. Discuss methods used for noise removal, normalization, and data transformation.

04: Explain the concept of data visualization. Discuss any four commonly used charts and justify their use for analyzing different types of datasets.

05: Discuss the need for Big Data technologies. Explain the characteristics of Big Data and describe how Big Data processing differs from traditional data processing systems.

06: Describe the architecture of the Hadoop ecosystem. Explain the role of HDFS, YARN, and MapReduce in handling large-scale data processing.

Q7: What is Apache Spark? Explain its core components and advantages over traditional Hadoop MapReduce. Discuss one practical application where Spark is effectively used.

08: Explain the concept of NoSQL databases. Compare key-value stores, column-family databases, and document databases with suitable examples and use cases.

09: Explain the techniques used for similarity measurement in Big Data analytics. Discuss Jaccard similarity, cosine similarity, and their role in recommendation systems.

Q10: Write R programs for the following tasks:

(a) Perform simple linear regression on a sample dataset and interpret the output. (b) Apply a classification technique of your choice on a dataset and explain the results obtained.