DSE - Data Science Exploratory Data Analysis Techniques Questions and Answers

Question 1

A data scientist is beginning to work with a new dataset of customer transactions. Which of the following is the PRIMARY goal of Exploratory Data Analysis (EDA) in this initial phase?

Accepted Answer

To summarize the main characteristics of the data and uncover initial patterns.

Answer

The primary goal of EDA is to understand the dataset before formal modeling or hypothesis testing. This involves using statistical summaries and visualizations to discover patterns, spot anomalies, check assumptions, and understand the data's structure, which is crucial for subsequent analysis.

Question 2

During the EDA process for a housing price dataset, you create a scatter plot of 'square footage' versus 'price' and observe a strong positive linear relationship. This is an example of which type of EDA technique?

Accepted Answer

Multivariate graphical

Answer

A scatter plot that examines the relationship between two variables ('square footage' and 'price') is a form of multivariate graphical analysis. Univariate analysis would focus on a single variable, and non-graphical methods involve summary statistics rather than plots.

Question 3

A data analyst is using a box plot to examine the distribution of salaries for a specific job role. The plot reveals several data points located far beyond the whiskers of the box. What is the most likely interpretation of these points?

Accepted Answer

Potential outliers

Answer

Box plots are a standard graphical EDA technique used to display the distribution of numerical data. The 'whiskers' typically extend to 1.5 times the interquartile range (IQR) from the first and third quartiles. Data points that fall outside of these whiskers are considered potential outliers that may require further investigation.

Question 4

Which of the following techniques is most suitable for understanding the relationships between multiple continuous variables simultaneously during EDA?

Accepted Answer

Correlation matrix with a heatmap

Answer

A correlation matrix calculates the correlation coefficient between all pairs of continuous variables in a dataset. Visualizing this matrix as a heatmap provides a quick, intuitive way to spot strong positive or negative correlations between multiple variables at once, making it a powerful multivariate EDA tool.

Question 5

A data science team is analyzing a dataset with many features (high dimensionality). To simplify the data and aid in visualization, they apply a technique that reduces the number of variables while retaining most of the original information. Which EDA technique are they most likely using?

Accepted Answer

Principal Component Analysis (PCA)

Answer

Principal Component Analysis (PCA) is a classic dimensionality reduction technique used in EDA. It transforms a large set of correlated variables into a smaller set of uncorrelated variables called principal components, making it easier to visualize and analyze high-dimensional data without significant information loss.

DSE - Data Science Practice Test

DSE - Data Science Practice Test

DSE - Data Science Exploratory Data Analysis Techniques Questions and Answers