MS-DS Master of Data science Master of Data science Exploratory Data Analysis 1

Question 1

Which statistical measure is most resistant to the influence of outliers when describing the center of a dataset?

Accepted Answer

Median

Answer

The median is resistant to outliers because it depends only on the middle value(s) of an ordered dataset, not the magnitude of extreme values. The mean, by contrast, is pulled toward outliers.

Question 2

A histogram of a feature shows that most values cluster near zero with a long tail extending to very large positive values. What transformation would most likely normalize this distribution?

Accepted Answer

Square root or logarithmic transformation

Answer

Log or square root transformations compress large values and spread small values, which is effective for right-skewed distributions common when data clusters near zero with a long positive tail. Standardization and scaling preserve shape.

Question 3

During EDA, you notice that two features have a Pearson correlation coefficient of –0.87. What does this indicate?

Accepted Answer

As one feature increases, the other tends to decrease strongly

Answer

A Pearson correlation of –0.87 indicates a strong negative linear relationship: as one variable increases, the other tends to decrease. Correlation does not imply causation, and Pearson only measures linear association.

Question 4

What is the primary purpose of a box plot in exploratory data analysis?

Accepted Answer

To summarize the distribution of a continuous variable and highlight outliers

Answer

A box plot visualizes the five-number summary (minimum, Q1, median, Q3, maximum) and flags potential outliers as points beyond 1.5×IQR from the quartiles, making it effective for understanding spread, skew, and anomalies.

Question 5

In EDA, what does a high proportion of missing values in a feature column most directly suggest to an analyst?

Accepted Answer

The feature may be systematically missing and requires investigation before deciding on imputation or removal

Answer

High missingness warrants investigation into whether the data is missing at random, missing completely at random, or missing not at random. The mechanism determines whether imputation, removal, or alternative handling is appropriate.

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Master of Data science Exploratory Data Analysis 1