This section of Data Science questions and answers covers the topic of "Data Transformation" within the broader chapter of "Data Collection and Cleaning." Data transformation involves the process of converting and manipulating raw data into a suitable format for analysis and modeling. The questions address various aspects of data transformation, including techniques such as normalization, smoothing, feature engineering, and handling missing values. The difficulty level is moderate, aiming to test a solid understanding of fundamental concepts related to transforming and preparing data for further analysis in the field of Data Science.
1. What is the primary goal of data transformation in the context of data collection and cleaning?
a) Normalize data
b) Enhance data visualization
c) Aggregate data
d) Impute missing values
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Answer: a
Answer Explanation: Data transformation aims to normalize data, making it suitable for analysis and modeling.
2. Which of the following is an example of data transformation technique?
a) Data sampling
b) One-hot encoding
c) Data imputation
d) Data aggregation
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Answer: b
Answer Explanation: One-hot encoding is a common technique in data transformation, especially for categorical variables.
3. In the context of data transformation, what does the term "smoothing" refer to?
a) Removing outliers
b) Reducing noise in data
c) Scaling data
d) Handling missing values
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Answer: b
Answer Explanation: Smoothing in data transformation involves reducing noise to reveal underlying patterns.
4. Which data transformation technique is suitable for handling skewed data distributions?
a) Z-score normalization
b) Log transformation
c) Min-Max scaling
d) Standardization
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Answer: b
Answer Explanation: Log transformation is often used to mitigate the effects of skewed data distributions.
5. What is the purpose of feature engineering in the context of data transformation?
a) Enhance model interpretability
b) Extract meaningful information from raw data
c) Handle missing values
d) Impute outliers
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Answer: b
Answer Explanation: Feature engineering involves creating new features to better represent the underlying patterns in the data.
6. Which of the following is an example of a non-linear data transformation?
a) Min-Max scaling
b) Z-score normalization
c) Power transformation
d) Standardization
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Answer: c
Answer Explanation: Power transformation is a non-linear technique used for handling skewed data distributions.
7. What role does the "fillna" function play in data transformation using pandas?
a) Scaling numerical data
b) Handling missing values
c) Normalizing data
d) Encoding categorical variables
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Answer: b
Answer Explanation: The "fillna" function is used to handle missing values in pandas during data transformation.
8. When is it appropriate to use data discretization as part of data transformation?
a) Handling outliers
b) Dealing with categorical variables
c) Reducing the dimensionality of data
d) Creating bins for numerical data
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Answer: d
Answer Explanation: Data discretization involves creating bins for numerical data, aiding in analysis and modeling.
9. Which statistical measure is commonly used in data transformation to scale data to a standard range?
a) Mean
b) Median
c) Variance
d) Z-score
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Answer: d
Answer Explanation: Z-score is a common measure used for standardizing data in data transformation.
10. In the context of data transformation, what does the term "dummy variables" refer to?
a) Variables with missing values
b) Variables with outliers
c) Binary variables representing categories
d) Variables with high variance
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Answer: c
Answer Explanation: Dummy variables are binary variables used to represent categories in data transformation.
