MS-DS Master of Data science MS-DS Master of Data science Data Wrangling and Preprocessing Questions and Answers 2

Question 1

Which technique is most appropriate for handling multicollinearity among predictor variables during preprocessing?

Accepted Answer

Variance Inflation Factor analysis followed by feature removal

Answer

VIF analysis quantifies multicollinearity and guides removal of highly correlated predictors to stabilize model coefficients.

Question 2

When working with a dataset that has 40% missing values in a single feature, which approach is generally preferred over simple deletion?

Accepted Answer

Multiple imputation

Answer

Multiple imputation generates several plausible datasets to account for uncertainty in missing data, outperforming simple deletion when missingness is substantial.

Question 3

What is the primary purpose of applying a Box-Cox transformation during data preprocessing?

Accepted Answer

To make the data distribution more closely approximate a normal distribution

Answer

Box-Cox transformation applies a power transformation to stabilize variance and make data more normally distributed for parametric modeling.

Question 4

In a data wrangling pipeline, what does the term 'tidy data' specifically require?

Accepted Answer

Each variable forms a column, each observation forms a row, and each type of observational unit forms a table

Answer

Tidy data, as defined by Hadley Wickham, requires variables in columns, observations in rows, and each observational unit in its own table.

Question 5

Which method is best suited for detecting outliers in a multivariate dataset?

Accepted Answer

Mahalanobis distance

Answer

Mahalanobis distance accounts for correlations between variables, making it effective for identifying multivariate outliers that univariate methods miss.

MS-DS Master of Data science Practice Test

MS-DS Master of Data science Practice Test

MS-DS Master of Data science MS-DS Master of Data science Data Wrangling and Preprocessing Questions and Answers 2