Question 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

Question 2

Which of the following is a common tool used for data visualization?

Matplotlib NumPy Pandas Scikit-learn

Question 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

Question 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

Question 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

Question 6

Which Python library is commonly used for creating static, animated, and interactive visualizations?

Seaborn Matplotlib Plotly Bokeh

Question 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

Question 8

Which visualization is best suited for showing the distribution of a single continuous variable?

Bar Chart Histogram Scatter Plot Line Chart

Question 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

Question 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

Data Preparation and Visualization