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| SESSION | JAN-FEB 2026 |
| PROGRAM | MASTER OF BUSINESS ADMINISTRATION (MBA) |
| SEMESTER | IV |
| COURSE CODE & NAME | DADS402 UNSTRUCTURED DATA ANALYSIS |
Assignment Set – 1
Q.1. a) Define unstructured data and explain its importance in business applications with suitable examples. b) Describe the process of text preprocessing and explain the role of tokenization and lemmatization in text analysis. (5+5 = 10 Marks)
Ans 1.
- a) Unstructured Data and Its Importance
Unstructured data refers to information that does not follow the pre-defined model of data or organized schema. It is not able to be easily stored in relational databases that have columns and rows fixed. Examples are text documents, messages on social media, emails as well as audio recordings, images, video files, sensor logs, and web pages. Approximately eighty to ninety percent of all data generated globally is non-structured and this proportion continues to grow because of the increasing use
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Q.2. a) What is a word cloud, and how does it help in understanding large text datasets? b) Explain the TF-IDF technique and how it improves the identification of important words compared to simple frequency methods. (5+5 = 10 Marks)
Ans 2.
- a) Word Cloud and Its Role in Text Analysis
Word clouds, sometimes known as a tag cloud or text cloud, is an image representation of terms that are frequently used in text corpus. The words that are frequently used are displayed in larger size fonts and in more prominent places, while less frequently used word types appear smaller. The display of visuals typically utilizes diverse colors, shapes and spatial layouts to create an engaging visual representation of the information contained in huge text datasets.
Word clouds assist analysts in being able to find the most important issues, subjects, and concepts within a vast body of written text without needing to study the entire text. For example, analysing hundreds of reviews from customers for an item using a word cloud immediately reveals the most
Q.3. a) Explain the concept of text classification and its applications in real-world scenarios. b) Describe topic modelling and explain how it helps in discovering hidden patterns in text data. (5+5 = 10 Marks)
Ans 3.
- a) Text Classification and Real-World Applications
Text classification is the process of processing language that assigns categorical labels for text files based upon their content. It’s asupervised machine learning task wherein a model is trained on the examples of text that are labelled to study how to connect text characteristics and the categories they belong to and thereby allowing the model classify new unseen text into the proper category in a timely manner.
The most common pipeline used to classify text involves processing the raw text, extracting quantitative features by using techniques such as bag-of-words, TF-IDF or word embeddings, training a classification algorithm
Assignment Set – 2
Q.4. a) What are the key features of MongoDB, and why is it suitable for handling unstructured data? b) Explain how audio data is pre-processed and transformed into features for classification tasks. (5+5 = 10 Marks)
Ans 4.
- a) Key Features of MongoDB and Suitability for Unstructured Data
MongoDB is a leading open-source NoSQL document-oriented data store that holds data in flexible, JSON-like documents called BSON (Binary JSON) rather than in fixed-schema relational tables. Every document has distinct structure that allows the database to be able to hold a variety of types of data without requiring schema changes. This feature allows MongoDB fundamentally different from relational databases. It is especially suited to managing semi-structured
Q.5. a) Define image processing and explain its significance in analyzing unstructured visual data. b) Describe the process of image classification and its applications in real-world scenarios. (5+5 = 10 Marks)
Ans 5.
- a) Image Processing and Its Significance
Image processing is the art that applies computational techniques on digital images in order to improve the quality of images, to extract valuable data, or convert images into formats that are suitable for analysis further and automated decision-making. It is a foundational technology of computer vision. It plays a critical role in making unstructured visual data accessible and
Q.6. a) Explain the process of video data analysis and how patterns are extracted from video sequences. b) Describe how machine learning techniques are used for fake news detection using textual data. (5+5 = 10 Marks)
Ans 6.
- a) Video Data Analysis and Pattern Extraction
Video data is one of the most rich and demanding types of unstructured data. It is simply composed of pictures presented at one fixed frame speed, together with an audio track and temporal metadata. Data analysis of video involves obtaining significant patterns, incidents, and information from this continuous streaming of audio and visual information for applications including activity identification, anomaly detection the moderation of content, sports analytics as well as surveillance.
The video analysis pipeline starts by preparing the frame, such as extraction at a certain frequency to decrease computational load,