data-analysis

Dataset exploration, cleaning, statistical analysis, and visualization in Python or SQL. Python (Pandas) : SQL : - Supports CSV, JSON, and SQL data sources with pandas DataFrames and direct database queries - Covers the full analysis pipeline: data loading, missing value handling, outlier detection, grouping, correlation analysis, and pivot tables - Includes visualization templates for histograms, boxplots, heatmaps, and time series using matplotlib and seaborn - Generates structured markdown reports with dataset overview, key findings, statistical summaries, and actionable recommendations - Data exploration : Understand a new dataset - Report generation : Derive data-driven insights - Quality validation : Check data consistency - Decision support : Make data-driven recommendations - Understand the data first : Learn structure and meaning before analysis - Incremental analysis : Move from simple to complex analyses - Use visualization : Use a variety of charts to spot patterns - Validate assumptions : Always verify assumptions about the data - Reproducibility : Document analysis code and results - Preserve raw data (work on a copy) - Document the analysis process - Validate results - Do not expose sensitive personal data - Do not draw unsupported conclusions - Pandas Documentation - Matplotlib Gallery - Seaborn Tutorial