DADS302 EXPLORATORY DATA ANALYSIS

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. Common

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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

 

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 analytics, as it allows stakeholders

 

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

 

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 sciences to uncover hidden patterns and

 

 

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