Total 30,780 skills, Data Processing has 1471 skills
Showing 12 of 1471 skills
Assemble multi-panel scientific figures with panel labels (A, B, C) at publication quality (300 DPI) using R. Use when combining individual plots into journal-ready figures.
Analyze relationship between profitability and market share. Use for competitive advantage assessment, scale economies analysis, and strategy validation.
Design KPI dashboards for executive monitoring. Use for performance tracking, strategic initiatives, and management reporting.
Visualize relationships between two variables. Use for correlation analysis and pattern identification.
Model free cash flow to evaluate project or business value. Use for investment decisions, valuation, and understanding cash dynamics.
Create multi-criteria comparison charts using traffic lights or Harvey balls. Use for option evaluation, competitive comparison, and executive dashboards.
Decompose Return on Equity into component ratios to identify performance drivers. Use for financial analysis, performance benchmarking, and identifying improvement opportunities.
Analyze cost advantages from scale. Use for understanding cost structure and competitive positioning.
Portfolio management. Display of held securities, trade records, structural analysis. Input data foundation for stress testing.
Tushare is a financial data interface package with rich data content, including market data such as stocks, funds, futures, digital currencies, and fundamental data such as corporate finance and fund managers. This skill is designed for proxy access scenarios, and the interface calling syntax is consistent with the official Tushare Pro.
Use this skill when implementing data validation, data quality monitoring, data lineage tracking, data contracts, or Great Expectations test suites. Triggers on schema validation, data profiling, freshness checks, row-count anomalies, column drift, expectation suites, contract testing between producers and consumers, lineage graphs, data observability, and any task requiring data integrity enforcement across pipelines.
Use this skill when performing exploratory data analysis, statistical testing, data visualization, or building predictive models. Triggers on EDA, pandas, matplotlib, seaborn, hypothesis testing, A/B test analysis, correlation, regression, feature engineering, and any task requiring data analysis or statistical inference.