Total 53,999 skills, Data Processing has 2767 skills
Showing 12 of 2767 skills
Build professional financial services data packs from various sources including CIMs, offering memorandums, SEC filings, web search, or MCP servers. Extract, normalize, and standardize financial data into investment committee-ready Excel workbooks with consistent structure, proper formatting, and documented assumptions. Use for M&A due diligence, private equity analysis, investment committee materials, and standardizing financial reporting across portfolio companies. Do not use for simple financial calculations or working with already-completed data packs.
Best practices for developing advanced, interactive, and publication-quality data visualizations using HoloViz HoloViews
Codified expertise for demand forecasting, safety stock optimisation, replenishment planning, and promotional lift estimation at multi-location retailers.
Portfolio risk analysis including Value at Risk (parametric, historical, Monte Carlo), Conditional VaR, stress testing, drawdown analysis, and factor exposure assessment.
Python data analysis with pandas, numpy, and analytics libraries
Parse and explain HL7 v2.5 IHE PAM (Patient Administration Management) messages. Identifies message type, extracts segments (MSH, EVN, PID, PV1, PV2), validates structure, and provides detailed explanations of ADT messages for patient administration workflows.
Expert-level Looker BI, LookML, explores, dimensions, measures, dashboards, and data modeling
The market analysis function of Gate Exchange — liquidity, momentum, liquidation, funding arbitrage, basis, manipulation risk, order book explainer, slippage simulation. Use when the user asks about liquidity, depth, slippage, buy/sell pressure, liquidation, funding rate arbitrage, basis/premium, manipulation risk, order book explanation, or slippage simulation (e.g. market buy $X slippage). Trigger phrases: liquidity, depth, slippage, momentum, buy/sell pressure, liquidation, squeeze, funding rate, arbitrage, basis, premium, manipulation, order book, spread, slippage simulation.
Compute technical indicators like RSI, MACD, Bollinger Bands, SMA, EMA for a stock. Use when user asks about technical analysis, indicators, RSI, MACD, moving averages, overbought/oversold, or chart analysis.
Scrapes Amazon product data from ASINs using browseract.com automation API and performs surgical competitive analysis. Compares specifications, pricing, review quality, and visual strategies to identify competitor moats and vulnerabilities.
Perform RFM (Recency, Frequency, Monetary) customer segmentation analysis on e-commerce data. Use when you need to analyze customer value, identify VIP customers, or create marketing segments. Automatically cleans data, calculates RFM metrics, applies K-means clustering, and generates visualization reports with Chinese language support.
Production-ready single-cell and expression matrix analysis using scanpy, anndata, and scipy. Performs scRNA-seq QC, normalization, PCA, UMAP, Leiden/Louvain clustering, differential expression (Wilcoxon, t-test, DESeq2), cell type annotation, per-cell-type statistical analysis, gene-expression correlation, batch correction (Harmony), trajectory inference, and cell-cell communication analysis. NEW: Analyzes ligand-receptor interactions between cell types using OmniPath (CellPhoneDB, CellChatDB), scores communication strength, identifies signaling cascades, and handles multi-subunit receptor complexes. Integrates with ToolUniverse gene annotation tools (HPA, Ensembl, MyGene, UniProt) and enrichment tools (gseapy, PANTHER, STRING). Supports h5ad, 10X, CSV/TSV count matrices, and pre-annotated datasets. Use when analyzing single-cell RNA-seq data, studying cell-cell interactions, performing cell type differential expression, computing gene-expression correlations by cell type, analyzing tumor-immune communication, or answering questions about scRNA-seq datasets.