數據科學
1726 skills in 數據與 AI > 數據科學
budget-creation
Complete budgeting and forecasting workflow covering annual budgets, departmental budgets, rolling forecasts, and variance analysis. Delivers documented budgets with approval trails.
using-finance-team
6 specialist financial agents for analysis, budgeting, modeling, treasury, accounting, and metrics. Dispatch when you need deep financial expertise.
database-performance-debugging
Debug database performance issues through query analysis, index optimization, and execution plan review. Identify and fix slow queries.
pricing-strategy
Pricing analysis skill for developing pricing models, competitive pricing analysis, and pricing recommendations for products and features.
code-metrics-analysis
Analyze code complexity, cyclomatic complexity, maintainability index, and code churn using metrics tools. Use when assessing code quality, identifying refactoring candidates, or monitoring technical debt.
pandas
Pandas library for data manipulation and analysis. Use for loading CSV files, data transformation, grouping, aggregation, and creating DataFrames for tabular data.
optimization-algorithms
Multi-objective optimization algorithms including Pareto front generation, dominance analysis, and constraint handling. Use for optimizing multiple competing objectives simultaneously.
scipy
Advanced scientific computing for portfolio optimization, statistical testing, and numerical methods. Use when minimizing portfolio variance, fitting distributions to returns data, performing correlation analysis, running hypothesis tests, or solving constrained optimization problems. Provides optimization algorithms (BFGS, SLSQP) and statistical distributions essential for risk modeling.
options-pricing
Options pricing models and derivatives valuation. Use for Black-Scholes pricing, Greeks calculation, implied volatility, and options strategy analysis.
matplotlib
Create data visualizations. Use for charts, plots, graphs, and saving figures to files.
signal-processing
Digital signal processing with NumPy/SciPy. Use when filtering signals, computing spectral analysis, performing convolution, detecting peaks, or processing time-series/frequency-domain data.
pyvista-visualization
Visualize 3D meshes, point clouds, and volumetric data using PyVista. Use for interactive plotting, mesh inspection, and creating publication-quality figures. Built on VTK with simplified Pythonic interface.
matplotlib
Plotting and visualization library. Use when creating charts, graphs, or visual representations of data.
data-transformation
Data transformation utilities for financial analysis. Use for currency conversions, percentage calculations, data normalization, and balance of payments accounting transformations.
rna-seq
Process RNA-seq data from raw reads through alignment and quantification to expression analysis.
itertools
Efficient iterator combinatorics and operations for memory-efficient looping. Provides combinations, permutations, cartesian products, and chainable iterators. Use when generating all possible currency pairs, exploring trading paths, creating sequences without loading into memory, grouping sorted data, computing running accumulations, or working with infinite sequences. Ideal for combinatorial analysis and lazy iteration.
numpy
High-performance numerical computing with multi-dimensional arrays and mathematical operations. Provides vectorized operations, linear algebra, statistical functions, and matrix manipulation. Use when performing bulk calculations on exchange rates, computing logarithmic transformations, calculating means/standard deviations, doing matrix operations, working with numerical data that needs fast computation, or requiring element-wise operations without loops. Essential for scientific computing and data preprocessing.
numpy-stl-processing
Read, write, and manipulate STL mesh files using numpy-stl library. Use for 3D printing preparation, mesh analysis, transformations, and combining multiple meshes. Fast numpy-based operations on triangular meshes.
data-aggregation
Aggregate and summarize data from multiple sources. Use when computing totals, averages, grouping data by categories, generating summary statistics, or combining data from multiple tables or files.
blockchain-analysis
Blockchain and cryptocurrency transaction analysis tools. Use for tracing transactions, analyzing wallet activity, and investigating cryptocurrency flows.