Podcast
Questions and Answers
Which of the following best describes the primary goal of data cleaning?
Which of the following best describes the primary goal of data cleaning?
- To maintain data quality and accuracy by correcting or removing errors and inconsistencies. (correct)
- To reduce the size of datasets for efficient storage.
- To transform data into a format suitable for visualization.
- To add more data points to ensure comprehensive analysis.
Data cleaning is a one-time process and does not need to be repeated as long as the data source remains the same.
Data cleaning is a one-time process and does not need to be repeated as long as the data source remains the same.
False (B)
Name three common types of data inconsistencies that are addressed during data cleaning.
Name three common types of data inconsistencies that are addressed during data cleaning.
Inaccurate data, missing data, inconsistent formatting
The process of filling in missing values in a dataset is known as data ________.
The process of filling in missing values in a dataset is known as data ________.
Match each data cleaning technique with its corresponding description:
Match each data cleaning technique with its corresponding description:
Which statistical method is LEAST suitable for handling missing data in a dataset?
Which statistical method is LEAST suitable for handling missing data in a dataset?
Using the mean to impute missing values is always the best option, regardless of the data distribution.
Using the mean to impute missing values is always the best option, regardless of the data distribution.
When should outlier removal be approached with caution?
When should outlier removal be approached with caution?
What is the purpose of data validation after the data cleaning process?
What is the purpose of data validation after the data cleaning process?
Which of the following is NOT a typical task in data transformation?
Which of the following is NOT a typical task in data transformation?
Data transformation is solely about converting data from one file format to another.
Data transformation is solely about converting data from one file format to another.
_________ is a data transformation technique used to scale numerical data to a standard range, such as between 0 and 1.
_________ is a data transformation technique used to scale numerical data to a standard range, such as between 0 and 1.
Name two key considerations when choosing a data aggregation method.
Name two key considerations when choosing a data aggregation method.
What is the purpose of feature engineering in data preprocessing?
What is the purpose of feature engineering in data preprocessing?
Feature engineering is only useful for complex machine learning algorithms and not for simpler statistical analyses.
Feature engineering is only useful for complex machine learning algorithms and not for simpler statistical analyses.
Which of the following is a common technique for handling categorical variables in machine learning?
Which of the following is a common technique for handling categorical variables in machine learning?
Describe a scenario where binning a numerical feature might be useful prior to modeling.
Describe a scenario where binning a numerical feature might be useful prior to modeling.
________ scaling is a feature scaling technique that transforms the values to have a mean of 0 and a standard deviation of 1.
________ scaling is a feature scaling technique that transforms the values to have a mean of 0 and a standard deviation of 1.
How can the effectiveness of different data preprocessing techniques be evaluated?
How can the effectiveness of different data preprocessing techniques be evaluated?
If a machine learning model performs poorly after applying a data preprocessing technique, it always indicates that the technique was implemented incorrectly.
If a machine learning model performs poorly after applying a data preprocessing technique, it always indicates that the technique was implemented incorrectly.
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