Data Science FREE Data Science Data Wrangling and Preprocessing Questions and Answers 2

Question 1

Which technique is most appropriate for handling missing numerical data when the dataset contains significant outliers?

Accepted Answer

Median imputation

Answer

Median imputation is preferred over mean imputation when outliers are present because the median is robust to extreme values.

Question 2

What is the primary purpose of one-hot encoding in data preprocessing?

Accepted Answer

To convert categorical variables into binary vector representations

Answer

One-hot encoding transforms each categorical value into a separate binary column, enabling algorithms that require numerical input to process categorical data.

Question 3

When performing a log transformation on a feature that contains zero values, what is the standard approach?

Accepted Answer

Apply log1p (log of 1 plus the value) instead of a standard log

Answer

Log1p adds 1 before taking the logarithm, which handles zero values without producing undefined results while preserving the transformation's variance-stabilizing effect.

Question 4

In the context of data wrangling, what does the term 'tidy data' refer to?

Accepted Answer

Data where each variable is a column, each observation is a row, and each value is a cell

Answer

Tidy data follows Hadley Wickham's principles where each variable forms a column, each observation forms a row, and each type of observational unit forms a table.

Question 5

Which method is most effective for detecting multicollinearity among features during preprocessing?

Accepted Answer

Calculating the Variance Inflation Factor (VIF)

Answer

VIF quantifies how much the variance of a regression coefficient is inflated due to collinearity with other predictors, with values above 5-10 indicating problematic multicollinearity.

Data Science Practice Test

Data Science Practice Test

Data Science FREE Data Science Data Wrangling and Preprocessing Questions and Answers 2