11 Essential Plots That Data Scientists Use 95% of the Time

Visualizations are powerful tools used to convey complex data patterns and relationships in a visually intuitive and comprehensible manner. They play a crucial role in data analysis, providing insights that are often difficult to discern from raw data or through traditional numerical presentations.

Here’s a detailed description of why visualizations are critical in understanding complex data patterns and relationships:

1. Simplification of Complexity: Complex data, such as large datasets or multi-dimensional data, can be overwhelming when presented in its raw form. Visualizations simplify this complexity by representing data points graphically, making it easier for individuals to grasp the underlying patterns and trends.
2. Pattern Recognition: Humans are highly visual creatures, and our brains are adept at recognizing patterns and trends in visual information. Visualizations leverage this innate ability, allowing us to identify trends, outliers, clusters, and anomalies in data more quickly and accurately.
3. Contextual Understanding: Visualizations provide context to data. They allow viewers to see the big picture and understand the relationships between various data points. For example, a scatter plot can show how two variables relate to each other, helping to establish causality or correlation.
4. Comparison and Contrast: Visualizations make it simple to compare and contrast different aspects of data. Bar charts, pie charts, and line graphs, for instance, enable quick comparisons between categories, proportions, and time series, respectively. This facilitates better decision-making.
5. Storytelling: Visualizations can tell a story by presenting data in a narrative form. When a series of visualizations is arranged in a logical sequence, they can convey a compelling narrative about the data, making it easier for audiences to follow and understand the information presented.
6. Interactive Exploration: Many modern data visualization tools and platforms offer interactive features that allow users to explore data in real time. This interactivity enhances understanding by enabling users to drill down into the data, zoom in on specific details, or change parameters to see how they affect the visualization.
7. Detecting Anomalies: Visualizations are instrumental in spotting anomalies or outliers in data. Deviations from expected patterns are often more apparent in visual representations, helping to identify errors or opportunities for further investigation.
8. Predictive Insights: Visualizations can also assist in making predictions based on historical data. By visualizing historical trends and relationships, analysts can better understand future possibilities and make informed forecasts.
9. Decision Support: Visualizations empower decision-makers by providing a clear view of the data. Whether in business, healthcare, or any other field, well-designed visualizations help individuals make informed decisions based on data-driven insights.
10. Communication and Collaboration: Visualizations serve as a universal language for data. They facilitate communication among different stakeholders, including those with varying levels of data literacy. Team members, clients, and partners can more easily collaborate and reach a common understanding through visual representations.

They offer a concise way to understand the:

• intricacies of statistical models
• validate model assumptions
• evaluate model performance and much more.

Thus, it is important to be aware of the most important and helpful plots in data science.

The visual below depicts the 11 most important and must-know plots in data science:

Today, let’s understand them briefly and how they are used.

KS Plot:

• It is used to assess the distributional differences.
• The core idea is to measure the maximum distance between the cumulative distribution functions (CDF) of two distributions.
• The lower the maximum distance, the more likely they belong to the same distribution.
• Thus, instead of a “plot”, it is mainly interpreted as a “statistical test” to determine distributional differences.

SHAP Plot:

• It summarizes feature importance to a model’s predictions by considering interactions/dependencies between them.
• It is useful in determining how different values (low or high) of a feature affect the overall output.

ROC Curve:

• It depicts the tradeoff between the true positive rate (good performance) and the false positive rate (bad performance) across different classification thresholds.
• The idea is to balance TPR (good performance) vs. FPR (bad performance).

Precision-Recall Curve:

• It depicts the tradeoff between Precision and Recall across different classification thresholds.

QQ Plot:

• It assesses the distributional similarity between observed data and theoretical distribution.
• It plots the quantiles of the two distributions against each other.
• Deviations from the straight line indicate a departure from the assumed distribution.

Cumulative Explained Variance Plot:

• It is useful in determining the number of dimensions we can reduce our data to while preserving max variance during PCA.

Elbow Curve:

• The plot helps identify the optimal number of clusters for the k-means algorithm.
• The point of the elbow depicts the ideal number of clusters.

Silhouette Curve:

• The Elbow curve is often ineffective when you have plenty of clusters.
• Silhouette Curve is a better alternative, as depicted above.

Gini-Impurity and Entropy:

• They are used to measure the impurity or disorder of a node or split in a decision tree.
• The plot compares Gini impurity and Entropy across different splits.
• This provides insights into the tradeoff between these measures.

Bias-Variance Tradeoff:

• It’s probably the most popular plot on this list.
• It is used to find the right balance between the bias and the variance of a model against complexity.

Partial Dependency Plots:

• Depicts the dependence between target and features.
• A plot between the target and one feature forms → 1-way PDP.
• A plot between the target and two feature forms → 2-way PDP.
• In the leftmost plot, an increase in temperature generally results in a higher target value.

Conclusion:

In conclusion, visualizations are indispensable tools in the world of data analysis and decision-making. They convert complex data into a visual language that is accessible, informative, and actionable. By leveraging our innate ability to understand visual information, they help us make sense of the intricate patterns and relationships hidden within the data, driving better insights and outcomes across various domains and industries.

Comment down your favourite visualisation!