MUJ MBA 3 SEM DADS Solved Assignments September 2025
| SESSION | JULY-AUGUST 2025 |
| PROGRAM | MASTER OF BUSINESS ADMINISTRATION (MBA) |
| SEMESTER | III |
| COURSE CODE & NAME | DADS301 PROGRAMMING IN DATA SCIENCE |
Assignment Set – 1
Q1. (a) Describe Data wrangling. Name the package used for Data wrangling in R and describe some of its features.
(b) Interpret vectors. Explain the creation of vectors with examples. Also, describe how to identify and handle missing values. 5+5
Ans 1.
(a) Description of Data Wrangling and Package in R
Data wrangling refers to the systematic process of transforming raw, unstructured, or semi-structured data into a clean and organized format suitable for analysis. Real-world datasets often contain missing values, inconsistencies, incorrect formats, and redundant information. Data wrangling involves tasks such as filtering, selecting variables, renaming columns, reshaping data, handling outliers, and merging multiple datasets. In R, data wrangling is central to any data science workflow because the quality of analysis depends heavily on how
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Q2. (a) Explain the chaining operator with an example.
(b) Articulate the summary () method with an example in R. 5+5
Ans 2.
(a) Explanation of the Chaining Operator
The chaining operator, often referred to as the pipe operator, is used in R to create readable and intuitive sequences of data transformations. The most widely used chaining operator is %>%, provided by the magrittr package and commonly used through the tidyverse. It passes the output of one function directly as the input to the next, reducing nested function calls and improving code clarity. Instead of writing deeply nested expressions, the programmer can express operations as a logical sequence. For example, without the chaining operator, a user
Q3. (a) Criticize with an example the syntax of various looping constructs in R – For, While and Repeat statements.
(b) Discuss continuous random variable. How can that be created using R? 5+5
Ans 3.
(a) Syntax and Use of Looping Constructs with Example
R provides three primary looping constructs: for, while, and repeat. These control structures allow repetitive execution of statements, although many operations in R are better handled through vectorization or apply-family functions. The for loop iterates over elements of a sequence. For example:
sum_val <- 0
for (i in 1:5) {
sum_val <- sum_val + i
Assignment Set – 2
Q4. (a) Explain with examples – Set, List and Tuples. What are the similarities and differences between the same.
(b) Illustrate how strings are converted into iterables in Python? Give a suitable example. 5+5
Ans 4.
(a) Sets, Lists and Tuples – Meaning, Examples, Similarities and Differences
In Python, lists, tuples and sets are fundamental collection data types used to store multiple values in a single variable, but they differ in structure, mutability and behaviour. A list is an ordered, mutable collection that can store heterogeneous elements. For example, marks = [80, 75, 90] defines a list, and elements can be updated as marks[1] = 78. Lists preserve insertion order and support operations like appending, slicing and iteration, which makes them ideal
Q5. (a) Summarize “waffle charts”. When is it used? Explain with an example in python.
(b) Discuss how simple and complex pattern searches can be performed on lists. 5+5
Ans 5.
(a) Waffle Charts – Meaning, Usage and Example in Python
A waffle chart is a visual representation used to display parts-of-a-whole relationships in a grid of small squares. Each square represents a fixed proportion, such as one percent of the total, and coloured squares indicate the contribution of a category. Unlike a pie chart, a waffle chart uses a rectangular grid, which many viewers find easier to interpret and compare. It is commonly used to show composition, such as the share of customer segments, product categories, or survey responses.
In Python, waffle charts can be built using libraries such as pywaffle in combination with
Q6. (a) Contrast the difference between loc and iloc attributes with example.
(b) When is agg() method used. Explain with example. 5+5
Ans 6.
(a) Difference Between loc and iloc with Example
In the pandas library, loc and iloc are two powerful indexers used to access subsets of rows and columns in a DataFrame. The loc attribute is label-based, meaning it selects rows and columns using explicit index and column names. For instance:
import pandas as pd
df = pd.DataFrame({
“name”: [“Asha”, “Ravi”, “Meera”],
“marks”: [85, 90, 88]
| SESSION | JULY-AUGUST 2025 |
| PROGRAM | MASTER OF BUSINESS ADMINISTRATION (MBA) |
| SEMESTER | III |
| COURSE CODE & NAME | DADS302 EXPLORATORY DATA ANALYSIS |
Assignment Set – 1
Q1. What is Data Science? Discuss the role of Data Science in various Domains. 10
Ans 1.
Data Science
Data Science is an interdisciplinary field that focuses on extracting meaningful insights from structured and unstructured data using scientific methods, algorithms, processes, and computational tools. It integrates principles from statistics, computer science, artificial intelligence, and domain expertise to transform raw data into actionable knowledge. With digitalization increasing across industries, organizations generate massive volumes of data, and Data Science helps convert this data into valuable business intelligence. In modern
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Q2. Explain various measures of dispersion in detail using specific examples. 10
Ans 2.
Dispersion
Measures of dispersion describe the extent to which data values in a dataset vary from one another. While measures of central tendency such as mean, median, and mode indicate the typical value, dispersion measures describe how spread out the data is around the central value. Understanding dispersion is essential in Exploratory Data Analysis (EDA) because it reveals variability, consistency, and reliability in datasets. A dataset with high dispersion
Q3. Discuss various techniques used for Data Visualization. 10
Ans 3.
Data Visualization
Data visualization refers to the graphical representation of information and data using charts, graphs, and visual elements. It transforms complex datasets into clear and understandable visuals, allowing users to identify patterns, trends, and relationships quickly. In Exploratory Data Analysis (EDA), visualization plays a critical role because it helps simplify large amounts of data and supports faster decision-making. Effective visualizations enable analysts
Assignment Set – 2
Q4. What is feature selection? Discuss any two feature selection techniques used to get optimal feature combinations. 10
Ans 4.
Feature Selection and Two Major Techniques
Feature selection refers to the systematic process of identifying the most relevant variables from a dataset that contribute meaningfully to predictive modelling or pattern recognition. Modern datasets often contain dozens or even hundreds of attributes, many of which may be redundant, noisy, or irrelevant. Including such features can reduce model accuracy, increase computational cost, and lead to overfitting. Feature selection improves model performance by reducing dimensionality, simplifying interpretation, and ensuring that only the most
performance is a priority and when the dataset is of manageable size.
Q5. Discuss in detail the concept of Factor Analysis 10
Ans 5.
Concept of Factor Analysis
Factor Analysis is a multivariate statistical technique used to identify underlying structures or latent variables within large datasets. When researchers work with many correlated variables, it becomes difficult to interpret relationships meaningfully. Factor Analysis simplifies this complexity by grouping related variables into smaller sets called factors. Each factor represents a hidden dimension underlying observable variables. This method is widely used in psychology, social sciences, marketing research, finance, and any domain where complex
Q6. Differentiate between Principal Component Analysis and Linear Discriminant Analysis 10
Ans 6.
Difference Between Principal Component Analysis and Linear Discriminant Analysis
PCA and LDA
Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are two widely used dimensionality reduction techniques, but they serve fundamentally different purposes. Both methods transform high-dimensional datasets into lower-dimensional representations, helping analysts visualize patterns, improve computational efficiency, and reduce noise. However, PCA focuses on capturing maximum variance in the data, while LDA aims to maximize class separability. Understanding the distinction between these techniques
| SESSION | JULY-AUGUST 2025 |
| PROGRAM | MASTER OF BUSINESS ADMINISTRATION (MBA) |
| SEMESTER | III |
| COURSE CODE & NAME | DADS303 INTRODUCTION TO MACHINE LEARNING |
Assignment Set – 1
Q1. What do you mean by Machine Learning? Discuss the concept of Supervised, Unsupervised and Reinforcement learning. 10
Ans 1.
Machine Learning
Machine Learning refers to a branch of artificial intelligence that enables computer systems to learn patterns from data and make predictions or decisions without being explicitly programmed for every scenario. Instead of following a rigid set of instructions, a machine learning model improves its performance over time as it is exposed to additional data. The core principle behind machine learning is the ability to identify relationships, extract meaningful insights, and generate accurate predictions in dynamic environments. As organizations collect massive volumes of data, machine learning has become a fundamental
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Q2. What is Support Vector Machine? What are the various steps in using Support Vector Machine? 10
Ans 2.
Support Vector Machine
Support Vector Machine (SVM) is a powerful supervised learning algorithm used primarily for classification and, to some extent, regression problems. The central idea of SVM is to identify an optimal separating boundary, known as a hyperplane, that best differentiates between different classes in the dataset. SVM works by maximizing the margin, which refers to the distance between the hyperplane and the nearest data points from each class. These nearest points are called support vectors. By maximizing this margin, the algorithm aims to
Q3. Discuss Decision Tree in detail for classification. How we can make a balance model taking decision tree. 10
Ans 3.
Decision Tree Classification and Creating a Balanced Decision Tree Model
Decision Tree Classification
A Decision Tree is a supervised learning method used for classification and prediction by dividing a dataset into branches based on decision rules. It resembles a tree-like structure with a root node, internal decision nodes, and leaf nodes representing outcomes. Decision Trees are intuitive because they mimic human decision-making by splitting data according to conditions. They are widely used in risk analysis, medical diagnosis, marketing segmentation, operational planning, and various prediction tasks. The core objective of a decision tree is to
Assignment Set – 2
- Explain the K-Means Clustering algorithm 10
Ans 4.
K-Means Clustering
K-Means is one of the most widely used unsupervised learning algorithms for clustering data into groups based on similarity. The algorithm aims to partition data points into a predefined number of clusters, represented by the variable K. Each cluster has a centroid, which serves as the central point around which similar data points are grouped. K-Means is effective because it is computationally efficient and works well when the structure of data exhibits
- Discuss various validation measures used for Machine Learning in detail. 10
Ans 5.
Validation Measures in Machine Learning
Validation measures play an essential role in evaluating the performance and reliability of machine learning models. They assess how well a model generalizes to unseen data and help prevent issues such as overfitting or underfitting. Validation ensures that model predictions are credible, stable, and suitable for real-world deployment. Since machine learning solutions often guide critical decisions in finance, healthcare, marketing, and security, selecting the
- What is a recommendation system. Discuss various types of recommendation systems. 10
Ans 6.
Meaning of Recommendation Systems and Types of Recommendation Systems
Recommendation Systems
A recommendation system is an intelligent information filtering tool used to suggest relevant products, services, or content to users based on their preferences and behavior. These systems play a central role in modern digital platforms, shaping user experience on e-commerce sites, streaming services, social media, and online learning environments. The core purpose of recommendation systems is to personalize content, increase engagement, boost satisfaction, and enhance business outcomes such as sales or retention.
Concept and Working of Recommendation Systems
The system analyzes user data such as browsing history, ratings, purchases, search patterns,
| SESSION | JULY-AUGUST 2025 |
| PROGRAM | MASTER OF BUSINESS ADMINISTRATION (MBA) |
| SEMESTER | III |
| COURSE CODE & NAME | DADS304 VISUALIZATION |
Assignment Set – 1
Q1. Compare and contrast bar charts and Pie charts in terms of their use cases, strengths, and limitations. 10
Ans 1.
Comparison Between Bar Charts and Pie Charts
Bar and Pie Charts
Bar charts and pie charts are among the most widely used visualization tools for summarizing and presenting categorical data. Although both serve the purpose of comparing categories, they differ significantly in structure, interpretability, and the type of insights they can convey. Understanding their similarities and differences helps analysts choose the appropriate visualization method depending on dataset characteristics and communication needs.
Use Cases of Bar Charts
A bar chart displays category names on one axis and their corresponding values on the other.
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Q2. Describe the role of user interface (UI) and server function in an R-Shiny app. 10
Ans 2.
Role of User Interface (UI) and Server Function in an R-Shiny App
R-Shiny App Structure
R-Shiny is an interactive web application framework that allows analysts to build dashboards and data-driven tools directly from R. A Shiny app operates through two core components: the user interface (UI) and the server function. These components work together to create dynamic and responsive applications that enable users to interact with datasets, models, and visualizations in real time.
Role of the User Interface (UI)
The UI defines how the application appears visually to the user. It includes layout, input
Q3. Define a dashboard in the context of data visualization. Discuss its importance with suitable example. 10
Ans 3.
Dashboards
A dashboard in data visualization refers to a consolidated visual display that compiles multiple charts, indicators, and analytical components into a single interface. Dashboards provide real-time insights by summarizing key metrics and trends associated with business operations, performance, customer behavior, or system status. They are widely used in decision-making environments because they present complex data in a structured and easily
Assignment Set – 2
Q4. What are the benefits of grouping data in Tableau when analyzing large datasets? 10
Ans 4.
Benefits of Grouping Data in Tableau When Analyzing Large Datasets
Grouping in Tableau
Grouping in Tableau is a feature that allows users to combine related members of a dimension into a single higher-level category. When exploring large datasets with many unique values, grouping becomes an effective way to simplify the visualization, uncover hidden relationships, and provide a clearer analytical view. The process helps analysts convert granular information into meaningful segments, enabling more strategic
Q5. Explain how LOD expressions allow aggregations at different levels of granularity beyond the visualization’s view. 10
Ans 5.
LOD Expressions
Level of Detail (LOD) expressions in Tableau are advanced analytical constructs that enable users to perform aggregations at different levels of granularity, independent of the visualization’s current view. LOD expressions allow analysts to define exactly how data should be grouped and aggregated, overcoming the limitations of traditional row-level or view-level computations. This results in highly flexible and precise analysis, especially when
Q6. Define data cleansing and explain why it is essential in Power BI. 10
Ans 6.
Data Cleansing in Power BI
Data cleansing in Power BI refers to the process of identifying, correcting, or removing inaccurate, inconsistent, incomplete, or irrelevant data before analysis. Since Power BI integrates data from multiple sources—such as Excel files, SQL databases, APIs, and cloud platforms—data quality issues are common. Clean data ensures the accuracy of dashboards, reports, and business decisions. Power Query, the data preparation engine in Power BI,
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