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

Question 1

A data scientist is working with a dataset containing customer information, including an 'Income' feature with a significant number of missing values.
The distribution of the 'Income' feature is heavily right-skewed.
Which of the following methods for handling missing data is most appropriate in this situation to minimize the impact of outliers?

Accepted Answer

Imputing the missing values with the median of the 'Income' feature.

Answer

Deleting the rows with missing income values.

Answer

Imputing the missing values with the mean of the 'Income' feature.

Answer

Replacing missing values with a constant like zero.

Question 2

You are preparing a dataset for a K-Nearest Neighbors (KNN) algorithm. The dataset has features with vastly different scales: 'Age' (ranging from 20 to 70) and 'Salary' (ranging from 30,000 to 150,000). Why is feature scaling a critical step in this scenario?

Accepted Answer

It ensures that features with larger ranges do not disproportionately influence the distance calculations.

Answer

It reduces the dimensionality of the dataset.

Answer

It converts categorical features into a numerical format.

Answer

It helps in identifying and removing outliers from the dataset.

Question 3

Which of the following data wrangling tasks involves creating new variables from existing ones to better represent the underlying patterns in the data for a machine learning model?

Accepted Answer

Feature Engineering

Answer

Data Cleaning

Answer

Data Integration

Answer

Data Validation

Question 4

A dataset contains a categorical feature 'Education Level' with the values: 'High School', 'Bachelor's', 'Master's', 'PhD'. This is an example of what type of data, and which encoding scheme would be most appropriate to preserve its inherent order?

Accepted Answer

Ordinal data; Label Encoding

Answer

Nominal data; One-Hot Encoding

Answer

Nominal data; Dummy Encoding

Answer

Ordinal data; One-Hot Encoding

Question 5

In the context of data preprocessing, what is the primary difference between Standardization and Normalization (Min-Max Scaling)?

Accepted Answer

Standardization (Z-score) transforms data to have a mean of 0 and a standard deviation of 1, while Normalization scales data to a specific range (e.g., [0, 1]).

Answer

Standardization scales data to a fixed range, typically [0, 1], while Normalization centers the data around a mean of 0.

Answer

Standardization is only used for normally distributed data, while Normalization can be used for any distribution.

Answer

Normalization is more robust to outliers than Standardization.

Question 6

A data analyst is cleaning a large dataset and discovers that 5% of the records are exact duplicates. Which of the following is the most compelling reason to remove these duplicate records?

Accepted Answer

To prevent the model from being biased towards the patterns present in the duplicated instances.

Answer

To reduce the computational cost of training the model.

Answer

To decrease the number of features in the dataset.

Answer

To ensure all data types are consistent across the records.