R Analystv1.0.1

Business analysts can describe a CSV dataset's structure, compute descriptive statistics, and detect missing values or outliers without needing to install R. This enables rapid initial assessment of new data files.

Researchers can generate ASCII histograms and correlation matrices for experimental data in CSV format. This facilitates quick visualization and relationship detection without complex software setup.

Quality engineers can detect outliers in sensor or measurement data using the clean command, which applies the 3σ rule. This helps identify anomalous records before further analysis.

Financial analysts can compute Pearson correlation matrices on CSV files containing asset returns. This aids in portfolio diversification assessment and risk management.

Market researchers can use the clean command to identify missing values and outliers in survey or sales data. This ensures data quality before feeding into more advanced models.

Offer basic stats/describe free, while advanced features like correlation matrices and outlier detection require a subscription. This drives adoption among casual users.

Integrate these commands into CI/CD data pipelines as a service for enterprises that need automated data quality checks. Charge per pipeline execution or monthly retainer.

License the skill package to universities and bootcamps for teaching data analysis without R dependency. Provide discounts for bulk academic licenses.