Description

SESSION JUL – AUG 2024
PROGRAM MASTER OF BUSINESS ADMINISTRATION (MBA)
SEMESTER III
COURSE CODE & NAME DADS302 EXPLORATORY DATA ANALYSIS

Assignment Set – 1

1. Explain various measures of dispersion in detail using specific examples.
Ans 1.
Measures of Dispersion
Measures of dispersion are statistical tools used to describe the spread or variability of data
within a dataset. While measures of central tendency like mean, median, and mode
summarize the data by providing a single representative value, measures of dispersion
highlight how much the data points deviate from this central value. Understanding dispersion
is crucial in data analysis as it gives insights into the reliability and consistency of the data.
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2. What is Data Science? Discuss the role of Data Science in various Domains.
Ans 2.
Data Science
Data Science is an interdisciplinary field that uses scientific methods, algorithms, and
systems to extract insights and knowledge from structured and unstructured data. It combines
elements of mathematics, statistics, computer science, and domain-specific knowledge to
process, analyze, and interpret large datasets. The goal of Data Science is to uncover hidden
patterns and trends, enabling organizations to make data-driven decisions.
At its core, Data Science involves multiple steps: data collection, data preprocessing,
3. Discuss various techniques used for Data Visualization.
Ans 3.
Data visualization is the graphical representation of data that helps in understanding trends,
outliers, and patterns. It transforms complex data into visual formats, such as charts, graphs,
and maps, to make it more comprehensible and accessible for decision-making. Effective data
visualization is critical in fields like business analytics, scientific research, and social media
Assignment Set – 2
4. What is feature selection? Discuss any two feature selection techniques used to get
optimal feature combinations.
Ans 4.
Feature Selection
Feature selection is the process of identifying the most relevant features (or variables) from a
dataset to improve the performance and efficiency of machine learning models. It involves
removing irrelevant, redundant, or noisy features that do not contribute significantly to the
predictive power of a model. Feature selection not only enhances model accuracy but also
reduces computation time, mitigates overfitting, and simplifies the interpretability of the
5. Discuss in detail the concept of Factor Analysis
Ans 5.
Concept of Factor Analysis
Factor analysis is a statistical technique used to identify underlying relationships between
variables in a dataset. It reduces the dimensionality of data by grouping correlated variables
into latent factors, simplifying the dataset while retaining most of the important information.
The technique is widely applied in fields such as psychology, finance, marketing, and social
6. Differentiate between Principal Component Analysis and Linear Discriminant
Analysis
Ans 6.
Difference Between Principal Component Analysis (PCA) and Linear Discriminant
Analysis (LDA)
Principal Component Analysis (PCA) and Linear Discriminant Analysis (LDA) are
dimensionality reduction techniques used to simplify large datasets by transforming them into
lower-dimensional spaces. While they share the objective of reducing dimensionality, their
methodologies and applications differ significantly.
Principal Component Analysis (PCA) is an unsupervised learning technique that focuses on