data-analysis

To find patterns and detect outliers in CSV data, use automated data analysis to calculate descriptive statistics and apply IQR or Z-score methods. This process identifies statistical anomalies and sequential trends within the dataset.

Generating visualizations from an Excel dataset involves loading the file into a data environment to create histograms, box plots, and correlation heatmaps. These visual plots translate raw spreadsheet data into actionable visual intelligence.

Yes, you need pandas and scipy to perform statistical analysis on JSON files. Pandas handles data manipulation and cleaning of JSON structures, while scipy provides the advanced statistical computations required for correlation analysis.

To identify correlations between variables in a dataset, perform a correlation analysis using statistical libraries like scipy. This calculates statistical relationships between different data columns and generates a correlation heatmap for visual confirmation.

Yes, you can clean missing values and duplicates before generating statistics. Data cleaning is an essential preparatory step that handles incomplete records and duplicate entries, ensuring resulting descriptive statistics and anomaly detection remain accurate.