MS-DS Master of Data science Exploratory Data Analysis Questions and Answers

Question 1

A data scientist is analyzing a dataset of housing prices and wants to understand the distribution of the 'price' variable, including its central tendency, spread, and the presence of potential outliers.
Which of the following visualization techniques is most suitable for this univariate analysis?

Accepted Answer

Box Plot

Answer

Scatter Plot

Answer

Correlation Matrix

Answer

Bar Chart

Question 2

During Exploratory Data Analysis of a retail dataset, you create a correlation matrix for variables such as 'customer_age', 'items_purchased', 'time_spent_minutes', and 'total_spend'. You observe a correlation coefficient of +0.85 between 'time_spent_minutes' and 'total_spend'. What is the correct interpretation of this finding?

Accepted Answer

There is a strong positive linear relationship, indicating that as time spent increases, total spend tends to increase.

Answer

There is a strong negative linear relationship between time spent and total spend.

Answer

There is no discernible linear relationship between the two variables.

Answer

The relationship is causal; spending more time in the store causes customers to spend more money.

Question 3

You are tasked with exploring a high-dimensional dataset with over 100 features to understand its underlying structure and to simplify it for future modeling. Which EDA technique is primarily used for dimensionality reduction by transforming a large set of variables into a smaller one that still contains most of the information?

Accepted Answer

Principal Component Analysis (PCA)

Answer

Cluster Analysis

Answer

Box-Cox Transformation

Answer

Outlier Detection using Z-scores

Question 4

A data analyst is performing EDA on a dataset and is currently focused on examining the relationship between two variables at a time. For instance, they are creating scatter plots of 'Age' vs. 'Income' and box plots of 'Education Level' vs. 'Income'. What type of analysis is being performed?

Accepted Answer

Bivariate Analysis

Answer

Univariate Analysis

Answer

Multivariate Analysis

Answer

Predictive Analysis

Question 5

Which of the following statements best distinguishes between Feature Engineering and Feature Selection within the context of data analysis?

Accepted Answer

Feature engineering involves transforming raw data into useful features, while feature selection is the process of choosing the most relevant existing features for a model.

Answer

Feature engineering is the process of removing irrelevant features, while feature selection is about creating new, more informative features.

Answer

Both terms are synonymous and refer to the process of preparing data for a machine learning model.

Answer

Feature selection uses algorithms like PCA to reduce dimensions, whereas feature engineering focuses on handling missing values.

Question 6

A financial analyst is using a box plot to analyze the daily returns of a stock. They notice that the median line within the box is located much closer to the first quartile (Q1) than to the third quartile (Q3), and the upper whisker is significantly longer than the lower whisker. What does this suggest about the distribution of the stock's daily returns?

Accepted Answer

The distribution is positively skewed (skewed right).

Answer

The distribution is perfectly symmetrical.

Answer

The distribution is negatively skewed (skewed left).

Answer

The distribution has no outliers.

A data scientist is analyzing a dataset of housing prices and wants to understand the distribution of the 'price' variable, including its central tendency, spread, and the presence of potential outliers.Which of the following visualization techniques is most suitable for this univariate analysis?

A data scientist is analyzing a dataset of housing prices and wants to understand the distribution of the 'price' variable, including its central tendency, spread, and the presence of potential outliers.
Which of the following visualization techniques is most suitable for this univariate analysis?