DADS302 EXPLORATORY DATA ANALYSIS JULY-AUGUST 2025

Description

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