Data analysis (dataset)

--- allowed-tools: Task, Read, Write, Bash, Grep, Glob argument-hint: [dataset] [analysis_type] description: Perform comprehensive data analysis on specified dataset --- # Data Analysis Command Execute data analysis on dataset $1 with analysis type $2 using the data-explorer subagent. ## Context - Dataset location: @data_storage/$1 - Analysis type: $2 (exploratory, statistical, predictive, complete) - Current working directory: !pwd - Available visualization libraries: matplotlib, seaborn, plotly - Python data science stack: pandas, numpy, scipy ## Your Task Use the data-explorer subagent to perform comprehensive data analysis: ### 1. Data Assessment - Load and inspect the dataset structure - Check data types, missing values, and duplicates - Generate initial summary statistics - Identify data quality issues ### 2. Statistical Analysis - Perform descriptive statistics analysis - Calculate correlations between variables - Identify outliers and anomalies - Conduct appropriate statistical tests ### 3. Pattern Discovery - Identify trends and patterns in the data - Discover relationships between variables - Detect seasonal patterns or cycles - Find clusters or segments in the data ### 4. Generate Insights - Extract key findings from the analysis - Identify actionable insights - Suggest areas for deeper investigation - Recommend visualization approaches ## Analysis Types ### Exploratory Analysis - Basic data understanding - Summary statistics - Data quality assessment - Initial pattern identification ### Statistical Analysis - Advanced statistical testing - Correlation and regression analysis - Hypothesis testing - Confidence intervals ### Predictive Analysis - Feature importance analysis - Predictive modeling preparation - Variable relationships - Model recommendation ### Complete Analysis - All of the above plus - Comprehensive report generation - Visualization recommendations - Next steps planning ## Expected Output ### Analysis Report Create a comprehensive analysis report with: - Executive Summary: Key findings in plain language - Data Overview: Dataset characteristics and quality - Statistical Findings: Detailed statistical analysis - Key Insights: Actionable discoveries - Recommendations: Next steps for deeper analysis - Limitations: Data and method constraints ### File Outputs - analysis_reports/analysis_summary_$1.md - Detailed analysis report - analysis_reports/statistical_summary_$1.csv - Statistical summary table - analysis_reports/data_quality_$1.json - Data quality assessment ## Quality Assurance - Validate all statistical calculations - Cross-check important findings - Document all assumptions and limitations - Ensure reproducible analysis ## Example Usage ``bash /analyze user_behavior.csv exploratory /analyze sales_data.csv statistical /analyze customer_data.csv predictive /analyze financial_data.csv complete `` ## Notes - Dataset should be located in the datastorage/ directory - Analysis results will be saved to analysisreports/ directory - Use Task tool to delegate to data-explorer subagent - Consider following up with /visualize command for charts