Outliers
Charts
Data Visualization Techniques
Data Mining Techniques Comparison
Data Visualization
Data Visualization Techniques for Analysts
Data Clustering
Graphs
Data Cleaning Techniques for Analysis Projects 
Outliers Data points that deviate significantly from other observations
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Importance of Visualization visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends,
outliers, and patterns in data
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Charts Good for showing correlation Can be difficult to interpret with large datasets Can display
outliers effectively Requires careful scaling of axes 5
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Data Visualization Techniques visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends,
outliers, and patterns in data
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Data Mining Techniques Comparison forecasting, financial analysis Predictive modeling, continuous outcomes Assumes linearity, sensitive to
outliers Clustering Unsupervised Market segmentation, social network analysis Discovers hidden
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Data Visualization visual elements like charts, graphs, and maps, data visualization tools provide an accessible way to see and understand trends,
outliers, and patterns in data
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Data Visualization Techniques for Analysts Identifies trends and
outliers that may require further investigation
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Data Clustering Anomaly Detection: Clustering can be used to identify
outliers in data, which may indicate fraudulent activities or errors
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Graphs By transforming numerical data into visual formats, graphs help stakeholders to identify trends, patterns, and
outliers that might not be immediately evident from raw data
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Data Mining Techniques for Financial Compliance Anomaly Detection Anomaly detection techniques are essential for identifying
outliers that may indicate fraudulent activities or compliance violations
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