Data & Analytics

Data analysis, BI, visualization, datasets, statistics, and ML workflows

skills in category: 12,629
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Showing 12,361–12,384 of 12,629 skills

Ml Model TrainerATrain machine learning models with automated workflows. Analyzes datasets,datagobashVotes: 0GitHub stars: 21
Hyperparameter TunerAOptimize machine learning model hyperparameters using grid search, randomdatapythonbashVotes: 0GitHub stars: 21
Feature Engineering ToolkitACreate, select, and transform features to improve machine learning modeldatapythongoVotes: 0GitHub stars: 21
Experiment Tracking SetupASetup machine learning experiment tracking using MLflow or Weights &datapythonbashVotes: 0GitHub stars: 21
Deep Learning OptimizerAOptimize deep learning models using Adam, SGD, and learning rate schedulingdatabashperformanceVotes: 0GitHub stars: 21
Dataset SplitterASplit datasets into training, validation, and testing sets for ML modeldatapythonbashVotes: 0GitHub stars: 21
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Data Visualization CreatorAGenerate plots, charts, and graphs from data with automatic visualizationdatabashVotes: 0GitHub stars: 21
Data Preprocessing PipelineAAutomate data cleaning, transformation, and validation for ML tasks.datapythongoVotes: 0GitHub stars: 21
Clustering Algorithm RunnerAExecute clustering algorithms (K-means, DBSCAN, hierarchical) to identifydatapythongoVotes: 0GitHub stars: 21
Classification Model BuilderABuild and evaluate classification models for supervised learning tasksdatapythongoVotes: 0GitHub stars: 21
Automl Pipeline BuilderABuild automated machine learning pipelines with feature engineering, model selection, and hyperparameter tuning. Use when automating ML workflows from data preparation through model deployment. Trigger with phrases like "build automl pipeline", "automate ml workflow", or "create automated training pipeline".datapythongoVotes: 0GitHub stars: 21
Anomaly Detection SystemAIdentify anomalies and outliers in datasets using machine learning algorithms. Use when analyzing data for unusual patterns, outliers, or unexpected deviations from normal behavior. Trigger with phrases like "detect anomalies", "find outliers", or "identify unusual patterns".datapythongoVotes: 0GitHub stars: 21
Spark OptimizationAOptimize Apache Spark jobs with partitioning, caching, shuffle optimization, and memory tuning. Use when improving Spark performance, debugging slow jobs, or scaling data processing pipelines.datapythongoVotes: 0GitHub stars: 45,825
Similarity Search PatternsAImplement efficient similarity search with vector databases. Use when building semantic search, implementing nearest neighbor queries, or optimizing retrieval performance.datagodatabaseVotes: 0GitHub stars: 45,825
Ml EngineerABuild production ML systems with PyTorch 2.x, TensorFlow, anddatagonodeVotes: 0GitHub stars: 45,825
Dbt Transformation PatternsAMaster dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.datasqltestingVotes: 0GitHub stars: 45,825
Data StorytellingATransform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.datapythongoVotes: 0GitHub stars: 45,825
Data Quality FrameworksAImplement data quality validation with Great Expectations, dbt tests, and data contracts. Use when building data quality pipelines, implementing validation rules, or establishing data contracts.dataci/cdVotes: 0GitHub stars: 45,825
Backtesting FrameworksABuild robust backtesting systems for trading strategies with proper handling of look-ahead bias, survivorship bias, and transaction costs. Use when developing trading algorithms, validating strategies, or building backtesting infrastructure.datagotestingVotes: 0GitHub stars: 45,825
Zarr PythonAChunked N-D arrays for cloud storage. Compressed arrays, parallel I/O, S3/GCS integration, NumPy/Dask/Xarray compatible, for large-scale scientific computing pipelines.datapythongoVotes: 0GitHub stars: 40
VaexAUse this skill for processing and analyzing large tabular datasets (billions of rows) that exceed available RAM. Vaex excels at out-of-core DataFrame operations, lazy evaluation, fast aggregations, efficient visualization of big data, and machine learning on large datasets. Apply when users need to work with large CSV/HDF5/Arrow/Parquet files, perform fast statistics on massive datasets, create visualizations of big data, or build ML pipelines that do not fit in memory.datapythongoVotes: 0GitHub stars: 40
Umap LearnAUMAP dimensionality reduction. Fast nonlinear manifold learning for 2D/3D visualization, clustering preprocessing (HDBSCAN), supervised/parametric UMAP, for high-dimensional data.datapythongoVotes: 0GitHub stars: 40
TorchdrugAPyTorch-native graph neural networks for molecules and proteins. Use when building custom GNN architectures for drug discovery, protein modeling, or knowledge graph reasoning. Best for custom model development, protein property prediction, retrosynthesis. For pre-trained models and diverse featurizers use deepchem; for benchmark datasets use pytdc.datapythongoVotes: 0GitHub stars: 40
Torch GeometricAGraph Neural Networks (PyG). Node/graph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning.datapythongoVotes: 0GitHub stars: 40