数据与 AI技能

Reduce data with many features into fewer dimensions using UMAP. Create 2D or 3D views, prepare inputs for clustering, compare parameter settings, and project new samples while checking whether the results remain stable and meaningful.

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• Visualizing data with many features in 2D or 3D
• Preparing reduced features for HDBSCAN clustering
• Guiding dimensionality reduction with partial labels

Fit regression and time series models in Python, interpret coefficients, compare alternatives, and check model assumptions. Supports statistical diagnostics and forecasting, with confidence intervals and validation steps to assess uncertainty and predictive performance.

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• Fit regression models and interpret coefficients
• Compare models for binary outcomes or counts
• Forecast time series with prediction intervals

Use Hugging Face Transformers to load pretrained models, generate text, and classify text, images, or audio. Prepare model inputs, control generation settings, and fine-tune models on custom datasets with the Trainer API.

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• Prototype text, image, or audio classification
• Configure text generation and conversational responses
• Fine-tune a model on a custom dataset

Explain how input features contribute to machine-learning predictions using SHAP. Select an explanation method and reference data, validate contributions against model outputs, and create charts for individual predictions or patterns across samples, including models with multiple outputs.

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• Explaining the feature contributions behind one prediction
• Visualizing attribution patterns across evaluation samples
• Selecting and explaining a class in a multiclass model

Analyze experimental or survey data with guidance on choosing statistical tests, checking assumptions, estimating effect sizes and uncertainty, planning sample sizes, and writing results in APA style, a standard format for research reporting.

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• Comparing outcomes between experimental groups
• Examining relationships in survey data
• Planning the number of study participants

Build and test SimPy models of queues, production, logistics, and other event-driven systems. Set execution limits, monitor waiting times and resource use, and analyze repeated runs with recorded random seeds for reproducible results.

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• Comparing waiting times with different staffing levels
• Modeling shared equipment in production or logistics
• Testing priority queues and interrupted work

Build graph neural networks, models that learn from connected nodes, with PyTorch Geometric. Supports node and graph classification, link prediction, custom datasets, and training approaches for large graphs or graphs containing multiple types of nodes and connections.

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• Build node and graph classification models
• Predict links between nodes
• Train models on graphs with multiple node and connection types

Analyze large scientific tables with Vaex, including datasets that exceed available memory. Build calculated columns, filter rows, compute statistics, visualize aggregated data, and convert files while identifying operations that may still require substantial RAM.

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• Computing statistics on tables larger than available memory
• Creating calculated features without storing full columns
• Visualizing large datasets with aggregated grids

Build and evaluate single-agent reinforcement learning experiments, where an agent learns actions from rewards. Use Stable Baselines3 to choose algorithms, create environments, monitor training, and save models with the normalization data needed for consistent evaluation.

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• Set up a reproducible reinforcement learning experiment
• Create and validate a custom Gymnasium environment
• Monitor training and save checkpoints

Build and evaluate time-to-event models with scikit-survival, including records whose events remain unobserved when follow-up ends. Keep preprocessing separate from evaluation data, compare models, predict survival probabilities, and summarize censoring-aware metrics.

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• Validate event indicators and follow-up times in local CSV data
• Train survival models without leaking evaluation data
• Evaluate survival probabilities with censoring-aware metrics

Work with large scientific arrays by storing data in smaller blocks and reading selected portions. Guides compression, cloud storage, format migration, and NumPy, Dask, or Xarray integration while preserving dimensions, units, and other metadata.

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• Reading selected regions of large scientific arrays
• Choosing data blocks and compression settings
• Working with scientific arrays stored on S3 or GCS

Store genomic variant records in TileDB-VCF, add samples over time, and retrieve selected samples or genomic regions. Supports batch reading, allele statistics, quality checks, and VCF/BCF export, with workflows for local storage and TileDB Cloud.

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• Build a variant dataset and add samples incrementally
• Extract variants for selected samples and genomic regions
• Review allele statistics and sample quality

Create static statistical charts from pandas tables to explore distributions, relationships, and group differences. Supports scatter plots, regression displays, pair plots, and heatmaps, with explicit choices for aggregation, uncertainty, missing data, and visual styling.

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• Exploring distributions and relationships in tabular data
• Comparing group differences with box or violin plots
• Visualizing correlation matrices as heatmaps

Forecast sales and other time series with Google’s TimesFM without task-specific training. Prepare regularly spaced CSV data, export predicted values and uncertainty ranges, add explanatory variables, and evaluate forecasts against held-out observations.

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• Forecast monthly sales or revenue from CSV data
• Export predicted values with uncertainty ranges
• Forecast with additional explanatory variables

Estimate RNA velocity, the modeled direction of gene expression change, from single-cell spliced and unspliced counts. Fit models, inspect gene-level plots, visualize trajectories, and explore relative timing and associated genes while checking model limitations.

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• Explore hypotheses about directions of cell-state change
• Inspect gene-level RNA velocity model fits
• Estimate relative cell ordering with a dynamical model

Plan study sample sizes, estimate statistical power—the chance of detecting an assumed effect—and find the smallest detectable effect. Supports standard tests and simulations for complex designs, with sensitivity analyses and sample size justifications.

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• Estimating participant or sample counts before data collection
• Finding the smallest effect detectable with a fixed sample
• Preparing sample size justifications for grants or protocols

Fit probabilistic models to single-cell measurements for integration across experiments, cell annotation, combined RNA and protein or ATAC analysis, and gene expression comparisons. The skill covers input requirements, training checks, reference mapping, and interpretation of model uncertainty.

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• Integrating single-cell RNA data across experimental batches
• Assigning cell labels using partial annotations or a reference
• Analyzing RNA with protein or ATAC measurements

Retrieve U.S. federal financial data from the Treasury’s public API, including debt, revenue, spending, interest rates, and exchange rates. Filter dates and fields, retrieve multiple pages, and organize results into tables for analysis.

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• Tracking U.S. national debt over time
• Comparing monthly federal revenue and spending
• Retrieving Treasury reporting exchange rates

Work with Waypoint models to analyze microbiome samples, the communities of microbes in a sample. Convert taxonomic abundance tables, generate numerical sample representations, configure further training for prediction tasks, and evaluate models on the Compass benchmark.

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• Convert microbiome abundance tables into Waypoint inputs
• Generate sample vectors for clustering and visualization
• Configure further training with sample labels

Analyze gene-expression differences in bulk RNA-seq data using PyDESeq2. Validate count tables and experimental designs, test explicit comparisons, adjust for multiple testing, shrink effect estimates, and export statistical results with volcano and MA plots.

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• Compare gene expression between treatment and control groups
• Analyze expression differences while accounting for batch effects
• Validate count tables against sample metadata

Process mass spectrometry data using scripts for format conversion, signal cleanup, peak detection, and quantification across samples. Prepare protein and metabolite analyses with identification filtering, candidate annotations, data tables, and plots.

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• Inspect, convert, and preprocess mass spectrometry files
• Build quantitative tables across multiple samples
• Find metabolite candidates from accurate mass

Solve optimization problems in Python with one or several competing goals. Define constraints, compare search algorithms, visualize trade-offs, and assess candidate solutions before choosing an option that fits your priorities.

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• Comparing designs with competing cost and performance goals
• Searching for solutions under explicit constraints
• Benchmarking optimization algorithms

Build Bayesian models in PyMC to estimate effects and quantify uncertainty. Fit regression or hierarchical models, check sampling diagnostics, assess predictions against observed data, predict new groups, and compare models using predictive performance.

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• Estimate regression effects with uncertainty
• Model differences across patients or groups
• Inspect sampling diagnostics and predictive checks

Read, query, filter, and write genomic files with pysam in Python. Extract regions, summarize variants, and count aligned bases while handling coordinate conventions, file indexes, headers, and the reference sequences needed to decode CRAM files.

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• Inspect genomic file formats, headers, and indexes
• Filter alignment records by quality and flags
• Summarize variants and genotypes in a genomic region