IGNOU MSCRWEE MCS 226 Data Science and Big Data Solved Assignment ENGLISH

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MCS 226 (July 2026) - ENGLISH

Course Code : MCS-226
 Course Title : Data Science and Big Data
Maximum Marks : 100
Weightage : 30%
Last date of Submission : 31st October, 2026 (For latest update, Pl.
 check IGNOU’s Website)
This assignment has ten questions of 8 Marks each, answer all questions. 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 for the format of presentation.
Ql: 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.
Q4: Explain the concept of data visualization. Discuss any four commonly used charts and justify their use for analyzing different types of datasets.
QS: 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.
Q6: 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.
Q8: Explain the concept of NoSQL databases. Compare key-value stores, columnfamily databases, and document databases with suitable examples and use cases.
Q9: Explain the techniques used for similarity measurement in Big Data analytics. Discuss Jaccard similarity, cosine similarity, and their role in recommendation systems.
QlO: 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.

MCS 226 (January 2026) - ENGLISH

Course Code :

MCS-226

Course Title :

Data Science and Big Data

Maximum Marks :

100

Weightage

: :

30%

Last date of Submission

: :

31st March, 2026 (For latest update, Pl. check IGNOU's Website)

This assignment has ten questions of 8 Marks each, answer all questions. 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 for the format of presentation.

Q1: 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.

Q2: 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.

Q3: Discuss the data cleaning process in detail. Explain missing value imputation techniques and the impact of data quality on predictive model performance.

Q4: 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.

Q5: 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.

Q6: 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.

Q7: 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. 

Q8: Explain Cosine Similarity and Jaccard Similarity with examples. How are these measures used in recommendation systems and document similarity analysis?

Q9: 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.

Q10: Write an R program to:

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

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

MCS 226 (January 2025 - July 2025) - ENGLISH

Course Code : MCS-226

Course Title : Data Science and Big Data

Maximum Marks : 100

Weightage : 30%

Last date of Submission : 31st March, 2025 (For latest update, Pl. check IGNOU’s Website)

This assignment has ten questions of 8 Marks each, answer all questions. 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 for the format of presentation.

Q1: Define the term data science. Describe its applications in two industries of your choice (e.g., healthcare, finance, e-commerce). What role does the data science lifecycle play in managing data projects?

Q2: Explain Exploratory Data Analysis (EDA) and its importance. What are the main steps in performing EDA on a new dataset? Describe two methods for detecting outliers and how handling outliers impacts data analysis.

Q3: Describe the role of statistical hypothesis testing in data analysis. What are Type I and Type II errors, and how do they affect decision-making? Provide an example of hypothesis testing in a real-world scenario.

Q4: Discuss the 4 Vs of big data (Volume, Velocity, Variety, and Veracity). Provide a real-world example of each, explaining how these characteristics create challenges in big data management.

Q5: Explain the Hadoop architecture with a focus on HDFS and the master/slave architecture. How do NameNode and DataNodes work together to store and manage large datasets? Provide a basic example of this storage process.

Q6: Compare Apache Spark, Hive, and HBase in terms of functionality, data processing methods, and use cases. When would Spark be preferred over traditional MapReduce, and why?

Q7: Describe the purpose and functionality of a *Bloom filter* in data stream processing. How does the Bloom filter efficiently check for element presence? Describe the FlajoletMartin algorithm for cardinality estimation in data streams.

Q8: What is the PageRank algorithm, and how is it used in li

MCS 226 2025 (JANUARY) - English

Course Code : MCS-226

Course Title : Data Science and Big Data

Maximum Marks : 100

Weightage : 30%

Last date of Submission : 31st March, 2025 (For latest update, Pl. check IGNOU’s Website)

This assignment has ten questions of 8 Marks each, answer all questions. 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 for the format of presentation.

Q1: Define the term data science. Describe its applications in two industries of your choice (e.g., healthcare, finance, e-commerce). What role does the data science lifecycle play in managing data projects?

Q2: Explain Exploratory Data Analysis (EDA) and its importance. What are the main steps in performing EDA on a new dataset? Describe two methods for detecting outliers and how handling outliers impacts data analysis.

Q3: Describe the role of statistical hypothesis testing in data analysis. What are Type I and Type II errors, and how do they affect decision-making? Provide an example of hypothesis testing in a real-world scenario.

Q4: Discuss the 4 Vs of big data (Volume, Velocity, Variety, and Veracity). Provide a real-world example of each, explaining how these characteristics create challenges in big data management.

Q5: Explain the Hadoop architecture with a focus on HDFS and the master/slave architecture. How do NameNode and DataNodes work together to store and manage large datasets? Provide a basic example of this storage process.

Q6: Compare Apache Spark, Hive, and HBase in terms of functionality, data processing methods, and use cases. When would Spark be preferred over traditional MapReduce, and why?

Q7: Describe the purpose and functionality of a *Bloom filter* in data stream processing. How does the Bloom filter efficiently check for element presence? Describe the FlajoletMartin algorithm for cardinality estimation in data streams.

Q8: What is the PageRank algorithm, and how is it used in link analysis? Describe the concept of "flow of rank" in PageRank. Explain how the PageRank of a web page is calculated using the flow model.

Q9: Discuss the challenges of online advertising and recommendation systems. Explain the concept of collaborative filtering with an example, and discuss the role of clustering in social network analysis.

Q10: What is the Random Forest algorithm? Explain how it can be applied to classification problems. Write a program in R to implement a Random Forest classifier on a sample dataset and explain its output.

MCS 226 (July 2025) - ENGLISH

Course Code : MCS-226

Course Title : Data Science and Big Data

Maximum Marks : 100

Weightage : 30%

Last date of Submission : 31st October, 2025 (For latest update, Pl. check IGNOU's Website)

This assignment has ten questions of 8 Marks each, answer all questions. 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 for the format of presentation.

Q1: Define the term data science. Describe its applications in two industries of your choice (e.g., healthcare, finance, e-commerce). What role does the data science lifecycle play in managing data projects?

Q2: Explain Exploratory Data Analysis (EDA) and its importance. What are the main steps in performing EDA on a new dataset? Describe two methods for detecting outliers and how handling outliers impacts data analysis.

Q3: Describe the role of statistical hypothesis testing in data analysis. What are Type I and Type II errors, and how do they affect decision-making? Provide an example of hypothesis testing in a real-world scenario.

Q4: Discuss the 4 Vs of big data (Volume, Velocity, Variety, and Veracity). Provide a real-world example of each, explaining how these characteristics create challenges in big data management.

Q5: Explain the Hadoop architecture with a focus on HDFS and the master/slave architecture. How do NameNode and DataNodes work together to store and manage large datasets? Provide a basic example of this storage process.

Q6: Compare Apache Spark, Hive, and HBase in terms of functionality, data processing methods, and use cases. When would Spark be preferred over traditional MapReduce, and why?

Q7: Describe the purpose and functionality of a *Bloom filter* in data stream processing. How does the Bloom filter efficiently check for element presence? Describe the Flajolet-Martin algorithm for cardinality estimation in data streams.

Q8: What is the PageRank algorithm, and how is it used in link analysis? Describe the concept of "flow of rank" in PageRank. Explain how the PageRank of a web page is calculated using the flow model.

Q9: Discuss the challenges of online advertising and recommendation systems. Explain the concept of collaborative filtering with an example, and discuss the role of clustering in social network analysis.

Q10: What is the Random Forest algorithm? Explain how it can be applied to classification problems. Write a program in R to implement a Random Forest classifier on a sample dataset and explain its output.

 

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