Question 1

Define the term Data Science. Explain the role of data sampling in Data Science. Differentiate between descriptive, inferential, and causal data analysis with suitable examples.

Accepted Answer

Data Science is an interdisciplinary field that employs scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It integrates concepts from statistics, computer science, mathematics, and domain expertise to understand phenomena and make better decisions. The core objective is to uncover patterns, build predictive models, and provide actionable intelligence.

Data sampling involves selecting a representative subset (sample) f

Question 2

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.

Accepted Answer

Correlation in statistics is a measure that quantifies the extent to which two variables are linearly related. It describes both the strength and the direction of this linear relationship between two quantitative variables. The most common measure is Pearson's correlation coefficient (r), which ranges from -1 to +1.

A correlation coefficient of +1 indicates a perfect positive linear relationship, -1 indicates a perfect negative linear relationship, and 0 indicates no linear relationship. It's

Question 3

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

Accepted Answer

Data cleaning is a crucial preparatory step in data science, involving the detection and correction of erroneous or inconsistent records from a dataset. As per MCS-226 Unit 3, its primary goal is to improve data quality, ensuring the data is accurate, complete, consistent, and reliable for analysis and modeling. This process typically addresses issues such as missing values, outliers, duplicate records, and structural errors.

The data cleaning process usually begins with data profiling to unde

Question 4

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.

Accepted Answer

Big Data refers to datasets so large and complex that traditional data processing applications are inadequate. Its defining characteristics are often described by the 'Four Vs', which encompass the challenges and opportunities associated with these massive datasets.

**The Four Vs of Big Data**

**1. Volume:** This refers to the immense quantities of data generated and stored. It's the sheer size that necessitates new processing technologies.

*   **Example:** Social media platforms like Faceboo

Question 5

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.

Accepted Answer

The Hadoop Distributed File System (HDFS) is a core component of the Apache Hadoop framework, designed for storing very large files across thousands of commodity servers. It provides highly fault-tolerant storage with high aggregate data bandwidth, making it ideal for big data processing applications that require high throughput, rather than low latency.

**HDFS Architecture with Diagram Description**

HDFS operates on a master-slave architecture. A typical HDFS setup consists of a single **Nam

Question 6

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.

Accepted Answer

Apache MapReduce and Apache Spark are both powerful frameworks for large-scale data processing in big data environments. While MapReduce was foundational, Spark emerged as a more versatile and efficient successor, particularly excelling in specific areas as discussed in MCS-226 course material.

Regarding **data processing speed**, Spark generally outperforms MapReduce significantly. MapReduce, by design, performs disk-based I/O for intermediate results; every Map phase writes to HDFS, and the

Question 7

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.

Accepted Answer

NoSQL databases, as discussed in the MCS-226 course material, offer flexible alternatives to traditional relational databases by supporting various data models optimized for different use cases. These databases are broadly categorized based on their underlying data storage and retrieval mechanisms, providing high scalability, availability, and resilience for modern data challenges.

**Column-based NoSQL Databases**
Column-based, or wide-column store, NoSQL databases organize data into rows, but

Question 8

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

Accepted Answer

Cosine Similarity and Jaccard Similarity are fundamental metrics used to quantify the resemblance between data objects, especially in areas like recommendation systems and document analysis.

Cosine Similarity measures the cosine of the angle between two non-zero vectors in a multi-dimensional space. It quantifies their orientation similarity, independent of their magnitude. A value of 1 indicates identical orientation, 0 indicates orthogonality (no similarity), and for positive data, it ranges

MCS-226 Solved Assignment — January 2026 / July 2025

Data Science and Big Data | IGNOU Master of Science (Renewable Energy and Environment) (MSCRWEE)

MCS-226—January 2026 / July 2025

Data Science and Big Data

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MCS-226 Assignment Questions — July 2025

QQ1. Define the term Data Science. Explain the role of data sampling in Data Science. Differentiate between descriptive, inferential, and causal data analysis with suitable examples.

QQ2. 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.

QQ3. Discuss the data cleaning process in detail. Explain missing value imputation techniques and the impact of data quality on predictive model performance.

QQ4. 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.

QQ5. 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.

QQ6. 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.

QQ7. 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.

QQ8. Explain Cosine Similarity and Jaccard Similarity with examples. How are these measures used in recommendation systems and document similarity analysis?

QQ9. 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.

QQ10. Write an R program to:

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