MUJ MBA 4 SEM DADS Solved Assignments JAN FEB 2026
| SESSION | JAN FEB 2026 |
| PROGRAM | MASTER OF BUSINESS ADMINISTRATION (MBA) |
| SEMESTER | IV |
| COURSE CODE & NAME | DADS401 ADVANCED MACHINE LEARNING |
Assignment Set – 1
Q.1. (a) Discuss the objectives of Time Series Analysis. (b) Explain Autoregressive model. (5+5 = 10 Marks)
Ans 1.
- a) Objectives of Time Series Analysis
Time series analysis is a mathematical method that is applied to data that are recorded or collected at successive identically spaced intervals in time. Its primary objective is to learn the structure and fundamentals of temporal data and use that understanding to make accurate predictions of the future’s value. Time series analysis serves several distinct and related goals in applied statistics and data science.
The initial goal is description by summarizing and displaying the features of the time series
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Q.2. Interpret ETS Model. Define the ARCH Model. Explain its usage. (5+5 = 10 Marks)
Ans 2.
ETS Model
The ETS model is a reference to Error, Trend and Seasonality. It is a model for exponential smoothing state space models that are used in forecasting time series. The name captures the three elements that the model is composed of and model separately. ETS models form a group of models instead of one model. every component being classified as All, Additive, or Multiplicative, generating the possibility of a wide range of combinations that are able to be
Q.3. Describe a few risks associated with Artificial Intelligence. Appraise some challenges or limitations we face with Deep Learning. (5+5 = 10 Marks)
Ans 3.
- a) Risks Associated with Artificial Intelligence
Artificial Intelligence presents several significant risks that must be carefully controlled as AI technology becomes more pervasive across industries as well as the public sector. Knowing the risks is crucial to ensure the responsible AI development and deployment.
Bias and fairness risk arises as AI systems trained on historical data are able to inherit and
Assignment Set – 2
Q.4. (a) Discuss ANN classification models. (b) Explain the classification of layers of CNN. (5+5 = 10 Marks)
Ans 4.
- a) ANN Classification Models
Artificial Neural Networks (ANNs) are computer-generated models that draw inspiration from the form and function of neuronal cells in the human brain. They’re composed of interconnected nodes that are organized in layers which handle input data using connections and weighted ones to create output predictions. They are extensively used in various classification tasks in a variety of domains such as the recognition of images, spam detection medical diagnosis, as well as
Q.5. (a) Describe the classification of RNN based upon architecture. (b) Illustrate the difference between SARSA and Q-Learning. (5+5 = 10 Marks)
Ans 5.
- a) Classification of RNN Based on Architecture
Recurrent Neural Networks (RNNs) are the name given to a group of neural networks that are specifically developed to handle sequential data in a hidden state that collects the information of previous times. Unlike feedforward networks that process each input independently, RNNs have the ability to have recurrent connections, which allows information to persist across the time, which makes them ideal to be used in tasks where the context of time is important such as speech
Q.6. (a) Demonstrate the phases we need for doing Neural Network Analysis. (b) Reframe some algorithms commonly used with Image recognition system. (5+5 = 10 Marks)
Ans 6.
- a) Phases of Neural Network Analysis
Neural network analysis is a planned method that has several steps to construct, verify, and deploy a model that is able to learn from evidence. Each step should be performed with care to ensure that the final model is precise solid, reliable, and adapts easily to data from new sources.
- The initial phase involves collecting and exploring data, that is the process of obtaining sufficient labeled datasets from relevant sources as well as performing an exploratory
MUJ MBA 4 SEM DADS Solved Assignments JAN FEB 2026
| 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
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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
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
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-
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
MUJ MBA 4 SEM DADS Solved Assignments JAN FEB 2026
| SESSION | JAN-FEB 2026 |
| PROGRAM | MASTER OF BUSINESS ADMINISTRATION (MBA) |
| SEMESTER | IV |
| COURSE CODE & NAME | DADS403 BUSINESS ANALYTICS |
Assignment Set – 1
Q.1. What is Business Analytics? Discuss the role of Business Analytics in various domains of business. (5+5 = 10 Marks)
Ans 1.
Definition of Business Analytics
Businesses Analytics (BA) refers to the process of utilizing analytical techniques, such as statistical analysis and predictive modelling, data mining, and machine learning techniques to transform raw business data into actionable information that aids better decision-making. It involves the systematic analysis of a company’s data assets for competitive advantages through
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JAN-FEB 2026
Q.2. A. Discuss any one purpose of using Conjoint analysis in detail. B. Explain the following terms and how they are calculated: TRP, GRP. (5+5 = 10 Marks)
Ans 2.
- Purpose of Conjoint Analysis: Product Design and Feature Prioritisation
Conjoint analysis is a marketing research method which is employed to discover how buyers consider different features and features. It does this by providing these with product descriptions that are hypothetical and asking them to make choices or place their preferences in relation to other options. One of the most important and frequently used uses is design of products and feature prioritisation. This helps companies make educated decisions about what product
Q.3. Discuss MDS and Perceptual Map in detail. (5+5 = 10 Marks)
Ans 3.
Multidimensional Scaling (MDS)
Multidimensional Scaling (MDS) is a technique that uses multivariate statistics to visualise the similarity or dissimilarity between sets of objects. It does this by presenting them as a series of points within some low-dimensional area, usually with two or three dimensions. The fundamental aim of MDS is to arrange objects in a geometric space such that the distances between points in that space are as close as possible to the original comparable or dissimilarity
Assignment Set – 2
Q.4. What is Propensity Modelling? Briefly discuss different types of Propensity Modelling. (5+5 = 10 Marks)
Ans 4.
Definition of Propensity Modelling
Propensity modeling is a mathematical and machine learning technique used to forecast the chance or chance that a particular individual or entity will take a particular action or exhibit a specific behaviour within certain time frames. It makes use of historical information about behaviors, demographic characteristics such as patterns of transactions, contextual variables to
Q.5. Briefly discuss any five Digital Marketing techniques. (10 Marks)
Ans 5.
Digital marketing covers any marketing strategy that employs digital platforms, channels and technology to communicate with and entice target audience. The following five strategies for digital marketing are the most popular and successful in the current practice.
Search Engine Optimisation (SEO)
SEO is the practice of optimizing a website’s search engine visibility. the organic search results
Q.6. Elaborate RPA. Discuss the 5-step implementation approach of RPA. (5+5 = 10 Marks)
Ans 6.
Robotic Process Automation (RPA)
Automated Process Automation (RPA) is the term used to describe a method of using software robots, also known as bots to perform repetitive, rules-based, high-volume digital jobs previously carried out by human workers. RPA robots communicate with digital systems, applications, and interfaces in the same way that a human would, which includes studying data on screens by entering data into the fields, pressing buttons, copying and pasting between different
| SESSION | JAN-FEB 2026 |
| PROGRAM | MASTER OF BUSINESS ADMINISTRATION (MBA) |
| SEMESTER | IV |
| COURSE CODE & NAME | DADS404 DATA SCRAPING |
Assignment Set – 1
Q.1. What factors should you consider when identifying a source for data scraping? (10 Marks)
Ans 1.
Finding the correct information source can be the vital first step in every data scraping endeavor. Unskillfully chosen sources can produce inaccurate data, legal complications as well as technical hurdles, which could lead to eventually unusable information. Certain key aspects must be carefully evaluated before making a decision on a source of automated data extraction.
Data Relevance and Quality
The main consideration is whether the source has the data fields that are specific to it as well as
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Q.2. Why are Wikipedia pages preferred source for data scraping? Write steps to scrape data from Wikipedia page using python library BeautifulSoup. (5+5 = 10 Marks)
Ans 2.
Why Wikipedia is a Preferred Source for Data Scraping
Wikipedia is widely considered among the top and widely accessible sites for scraping data science and research for a variety of compelling motives. For one, Wikipedia offers an enormous and varied collection of subjects that cover science, history, technology, geography, culture, sports and almost every other domain of human expertise, making it an ideal repository for the
Q.3. What are the advantages and disadvantages of API based Scraping? (5+5 = 10 Marks)
Ans 3.
Advantages of API-Based Scraping
API-based scraping is the collection of data through an application or site’s official Application Programming Interface rather than simply parsing raw HTML content. APIs are structured endpoints supplied by service providers exclusively for programsmatic access to data, and offer a number of advantages over standard web scraping.
Structured as well as Clean Data is the most immediate advantage of APIs. The information
Assignment Set – 2
Q.4. Why is scraping tweets useful for data analysis? Explain the process of collecting tweets using an API from X. (5+5 = 10 Marks)
Ans 4.
Why Scraping Tweets is Useful for Data Analysis
Twitter is now being rebranded to X Twitter, is among the most popular social media networks where millions of users express opinions and share information, talk about brands, engage with celebrities and post updates to current developments in real time. The constant stream of tweets that are publicly accessible data make X extremely valuable information source that can be used in a variety of areas of research.
Sentiment Analysis is among the most used applications. Business and research analysts analyze
Q.5. Explain how data wrangling improves the quality of data with examples. (10 Marks)
Ans 5.
Data wrangling, also called data munging or data preprocessing, is the process of cleaning, structuring, transforming to enrich raw data into a format which is accurate, consistent, and suitable for analysis and machine-learning. Raw data collected from web scraping sensors, databases APIs or manually entered data entry usually has issues, inconsistencies or even missing value, formatting and other issues that can lead to misleading or untrue analytical results
Q.6. Discuss the importance of using dplyr for preprocessing raw data. (10 Marks)
Ans 6.
dplyr is a powerful and widely used program for manipulating data in the R programming language. It was developed by Hadley Wickham as part of the tidyverse community. It is a unified user-friendly, easy to understand, and understandable grammar of manipulating data that allows preprocessing raw datasets more efficient, appealing, and less susceptible to error in comparison to the basic R functions. For data scientists and analysts working with raw or scraped
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