MS-DS Master of Data science Exploratory Data Analysis 5

Question 1

Which EDA plot is MOST effective for visualizing the joint distribution of two continuous variables, including the density in dense regions?

Accepted Answer

Hex bin plot

Answer

Hex bin plots aggregate points into hexagonal bins colored by count, avoiding overplotting in dense regions where scatter plots become unreadable.

Question 2

What does it mean when the mean absolute deviation (MAD) of a feature is very close to zero?

Accepted Answer

Almost all values are clustered near the mean with little spread

Answer

A near-zero MAD indicates that observations deviate very little from the mean on average, implying the feature has very low variability.

Question 3

During EDA, you find that a timestamp column contains dates ranging from 1900 to 2025, but the data should only span 2010–2025. What EDA action should you take first?

Accepted Answer

Visualize the temporal distribution and investigate the anomalous early dates before deciding on treatment

Answer

Investigating the anomalous values first reveals whether they are data entry errors, system defaults, or legitimate historical records before any transformation.

Question 4

Which measure is used to quantify the 'peakedness' or tail weight of a distribution relative to a normal distribution?

Accepted Answer

Kurtosis

Answer

Kurtosis measures the weight of a distribution's tails; excess kurtosis > 0 (leptokurtic) indicates heavier tails than normal, while < 0 (platykurtic) indicates lighter tails.

Question 5

A parallel coordinates plot is used in EDA primarily to:

Accepted Answer

Visualize multivariate data and identify patterns or clusters across many dimensions simultaneously

Answer

Parallel coordinates draw each observation as a polyline across multiple parallel axes, making it possible to spot clusters, outliers, and correlations in high-dimensional data.

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Exploratory Data Analysis 5