DADS402 UNSTRUCTURED DATA ANALYSIS JULY-AUGUST 2025

Description

SESSION	JULY-AUGUST 2025
PROGRAM	MASTER OF BUSINESS ADMINISTRATION (MBA)
SEMESTER	4
COURSE CODE & NAME	DADS402 UNSTRUCTURED DATA ANALYSIS

 

 

 

Assignment Set – 1

 

 

Q1.  (Unit 1–2: Unstructured Data & Feature Extraction)

1. a) Define unstructured data and list three common examples in business applications.
2. b) Explain how textual and pictorial data differ in terms of storage and analysis. 5+5

Ans 1.

(a) Unstructured Data and Its Business Examples

Unstructured data refers to information that does not follow a predefined format, schema, or organized structure suitable for traditional relational databases. Unlike structured data stored in rows and columns, unstructured data lacks a consistent data model, making it more complex to store, process, and analyze. It is usually generated through human interaction, digital communication, and multimedia platforms and continues to grow rapidly with digital transformation.

In business environments, unstructured data holds significant value as it captures customer

 

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Q2. (Unit 3–5: Word Cloud, Text Classification, Sentiment Analysis)

1. a) What is a word cloud, and how does it help in exploring unstructured text data?
2. b) Describe how text classification can be used to perform sentiment analysis on customer reviews. 5+5

Ans 2.

 (a) Word Cloud and Its Role in Exploring Unstructured Text Data

A word cloud is a visual representation of textual data where words are displayed in varying sizes based on their frequency or importance within a dataset. Frequently occurring words appear larger, while less common terms appear smaller. Word clouds are commonly used as an exploratory analysis tool to quickly identify dominant themes and keywords within unstructured text data.

In business analytics, word clouds help analysts gain an immediate overview of large text datasets such as customer reviews, survey responses, feedback forms, or social media

 

 

Q3.  (Unit 6–7: Topic Modelling & NoSQL Databases)

1. a) Explain the concept of topic modelling and its use in text mining.
2. b) Differentiate between SQL and NoSQL databases with suitable examples for unstructured data storage. 5+5

Ans  3.

(a) Concept of Topic Modelling and Its Use in Text Mining

Topic modelling is an unsupervised machine learning technique used to identify hidden thematic structures within large collections of unstructured text data. It automatically discovers groups of words that frequently occur together and represents them as topics. Each document in the dataset is treated as a mixture of topics, while each topic is represented as a distribution of words. Topic modelling helps reduce textual complexity and transforms massive text corpora into interpretable thematic patterns.

One of the most widely used topic modelling techniques is Latent Dirichlet Allocation. LDA

 

 

Assignment Set – 2

 

 

Q4.  (Unit 8–10: MongoDB, Audio Data, Audio Classification)

1. a) What are the key features of MongoDB that make it suitable for unstructured data?
2. b) Explain how audio data is pre-processed before classification. 5+5

Ans  4.

(a) Key Features of MongoDB for Unstructured Data

MongoDB is a document-oriented NoSQL database specifically designed to store and manage unstructured and semi-structured data efficiently.

Schema-Less Architecture

One of its most important features is its schema-less architecture, which allows data to be stored without a fixed structure. This flexibility enables applications to evolve rapidly without requiring frequent database redesigns, making MongoDB ideal for handling dynamic

 

 

Q5. (Unit 11–13: Image and Video Data)

1. a) Define image classification and give one real-world example of its application.
2. b) Describe the step-by-step process of classifying video data using deep learning models. 5+5

Ans 5.

(a) Image Classification and Its Real-World Application

Image classification refers to the process of assigning a predefined label or category to an image based on its visual content. In this task, a machine learning or deep learning model analyzes pixel values, color patterns, textures, and shapes to determine the class to which an image belongs. The goal is to enable systems to automatically recognize and categorize images without human intervention. Image classification is a fundamental problem in computer vision and serves as the foundation for many advanced image analysis applications.

In modern systems, image classification is primarily performed using deep learning models,

 

 

Q6. (Unit 14–15: Fake News Prediction & Case Study)

1. a) Explain the approach used for fake news prediction using textual data.
2. b) Write a short note on how case studies in audio data classification help understand real-world unstructured data applications. 5+5

Ans 6.

 (a) Approach for Fake News Prediction Using Textual Data

Fake news prediction using textual data involves identifying misleading or false information by analyzing linguistic patterns and content characteristics. The process begins with data collection, where news articles, social media posts, and online content are gathered from various sources. These text documents form the basis for training prediction models.

Next, text preprocessing is performed to clean the data. This includes removing stop words, punctuation, irrelevant symbols, and converting text into a standardized format. Tokenization