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Quiz 2: Data Preparation and Visualization
Test your knowledge on Data Visualization and Data Scaling concepts.
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1. What is the primary purpose of data visualization?
To store data efficiently
To represent data graphically for easier understanding
To clean and preprocess data
To perform statistical analysis
2. Which of the following is a common tool used for data visualization?
Matplotlib
NumPy
Pandas
Scikit-learn
3. What does data scaling aim to achieve?
To increase the size of the dataset
To normalize data to a specific range
To reduce the dimensionality of data
To remove outliers from the dataset
4. Which scaling technique transforms data to have a mean of 0 and a standard deviation of 1?
Min-Max Scaling
Standardization (Z-score normalization)
Robust Scaling
Log Transformation
5. In which scenario is Min-Max Scaling particularly useful?
When the data has outliers
When the data follows a normal distribution
When the algorithm assumes data is within a specific range
When the data has a large number of zero values
6. Which Python library is commonly used for creating static, animated, and interactive visualizations?
Seaborn
Matplotlib
Plotly
Bokeh
7. What is the effect of data scaling on distance-based algorithms like K-Nearest Neighbors?
It has no effect
It can improve the performance by ensuring all features contribute equally
It reduces the computational complexity
It increases the variance of the dataset
8. Which visualization is best suited for showing the distribution of a single continuous variable?
Bar Chart
Histogram
Scatter Plot
Line Chart
9. Why is it important to scale features before applying Principal Component Analysis (PCA)?
To increase the number of principal components
To ensure that each feature contributes equally to the analysis
To reduce the number of features
To improve the interpretability of the components
10. Which of the following is a potential downside of Min-Max Scaling?
It is sensitive to outliers
It does not change the distribution of the data
It is computationally expensive
It cannot be used with categorical data
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