IGNOU MCS 226 SOLVED ASSIGNMENT 2025 2026

Ques 1.

Define the term Data Science. Explain the role of data sampling in Data Science.

Ques 2.

Differentiate between descriptive, inferential, and causal data analysis with suitable examples.

Ques 3.

What is Correlation in statistics? Explain the difference between positive correlation and negative correlation with examples. Discuss how the Central Limit Theorem is applied in Data Science projects.

Ques 4.

Discuss the data cleaning process in detail. Explain missing value imputation techniques and the impact of data quality on predictive model performance.

Ques 5.

What are the Four Vs of Big Data? Give real-world examples for each. Compare Big Data systems with traditional data warehouses in terms of architecture and functionality.

Ques 6.

 Explain the Hadoop Distributed File System (HDFS) architecture with a diagram. Describe the role of NameNode and DataNodes in storing and retrieving large-scale datasets.

Ques 7.

Compare MapReduce and Apache Spark with respect to data processing speed, fault tolerance, and ease of use. Provide a real-world use case where Spark is more beneficial than MapReduce.

Ques 8.

What are Column-based, Document-based, and Graph-based NoSQL databases? For each type, give an example and explain a real-world use case where it is most suitable.

Ques 9.

Explain Cosine Similarity and Jaccard Similarity with examples. How are these measures used in recommendation systems and document similarity analysis?

Ques 10.

 What is the Flajolet-Martin algorithm? Explain its steps for estimating the number of unique elements in a data stream. Compare it with the use of a Bloom Filter.

Ques 11.

 Create a Decision Tree classifier for a sample dataset. Display the results visually using appropriate R plotting functions. Explain the outputs obtained.

Ques 12.

 Apply K-Means clustering to group similar data points. Display the results visually using appropriate R plotting functions. Explain the outputs obtained.

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