A data scientist is working on a dataset with multiple categories and subcategories. What data visualization techniques can be used to ensure the readability and aesthetics of the data presentation?
- Box plot, because it shows the range and outliers
- Parallel coordinates, because it can represent multiple dimensions
- Scatter plot, because it shows relationships between variables
- Stacked bar chart or treemap, because they can show hierarchical data
Stacked bar charts or treemaps are suitable for visualizing data with multiple categories and subcategories (hierarchical data). These graphs allow the viewers to see the total size of each main category and the size of each subcategory within the main ones.
Suppose you are visualizing survey data where the responses are highly skewed towards one particular option. How can you accurately depict this bias in your visualization?
- Use a pie chart with equal slices for each response
- Use a bar graph with the y-axis starting at the lowest response value
- Use a bar graph with the y-axis starting at zero
- Present the data in a table, because graphs can't show this
If the responses to a survey question are highly skewed towards one option, a bar graph with the y-axis starting at zero can accurately depict this bias. This type of graph clearly shows the difference in the number of responses for each option, allowing viewers to see the skewness.
What are the key steps involved in an EDA process?
- Clean, Transform, Visualize, Model
- Gather, Analyze, Report
- Plan, Perform, Evaluate
- Question, Wrangle, Explore, Conclude, Communicate
The key steps in EDA are: Question (identifying the questions you want to answer), Wrangle (collecting the necessary data and cleaning/preprocessing it), Explore (investigating the data, looking for patterns and relationships, often through visualizations), Conclude (interpreting the analysis, answering the questions), and Communicate (presenting your findings effectively to others). This iterative process can offer a robust approach to understanding the data's features and underlying structures.
In a scenario where you need to produce a quick-and-dirty plot with minimal coding, which Python library would be the most appropriate?
- Bokeh
- Matplotlib
- Plotly
- Seaborn
Seaborn is a high-level interface based on Matplotlib that provides a high-level interface for drawing attractive statistical graphics with fewer lines of code. This makes it more suitable for quickly producing plots with minimal coding.
How does improper handling of missing data impact the precision-recall trade-off in a model?
- Degrades both precision and recall.
- Degrades precision but improves recall.
- Improves both precision and recall.
- Improves precision but degrades recall.
Incorrectly handling missing data can lead to incorrect learning and misclassification, degrading both the precision (incorrectly identified positives) and recall (missed true positives) of the model.
Why is the standard deviation a useful measure of dispersion?
- It is the same as variance
- It's a measure of average dispersion
- It's the most complex measure of dispersion
- It's unaffected by outliers
The "Standard Deviation" is a useful measure of dispersion because it is a "Measure of average dispersion". It tells us how much, on average, each value in the data set deviates from the mean.
How can one interpret the colors in a heatmap?
- Colors have no significance in a heatmap
- Colors represent different categories of data
- Colors represent the magnitude of the data
- Darker colors always mean higher values
In a heatmap, colors represent the magnitude of the data. Usually, a color scale is provided for reference, where darker colors often correspond to higher values and lighter colors to lower values. However, the color scheme can vary.
In what situations is it more appropriate to use the interquartile range instead of the standard deviation to measure dispersion?
- When the data has no outliers
- When the data is normally distributed
- When the data is perfectly symmetrical
- When the data is skewed or has outliers
The Interquartile Range (IQR) is a more appropriate measure of dispersion when the data is "Skewed or has outliers" as it is not affected by extreme values.
Incorrect handling of missing data can lead to a(n) ________ in model performance.
- amplification
- boost
- degradation
- improvement
Incorrectly handling missing data can distort the data, thereby negatively affecting the model's ability to learn accurately from it and leading to a degradation in the model's performance.
The ________ correlation coefficient is based on the ranks of data rather than the actual values.
- Covariance
- Kendall's Tau
- Pearson's
- Spearman's
The Spearman's correlation coefficient is based on the ranks of data rather than the actual values. This makes it suitable for use with ordinal variables and resistant to outliers.