moai-domain-data-science
Production-grade data science specialist with TensorFlow 2.20.0, PyTorch 2.9.0, Scikit-learn 1.7.2 expertise. Master data processing, ML pipeline development, model deployment, and statistical analysis. Build end-to-end data science solutions with comprehensive experimentation and visualization.
npx skills add majiayu000/claude-skill-registry --skill moai-domain-data-science-dolsoon-my-awesome-project --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
What it does
Conveys a production-grade data science specialization, outlining capabilities in data processing, machine learning, visualization, statistics, big data, and experimentation. It includes concrete code samples and practical implementations for data preprocessing, pipeline construction, feature engineering, and statistical analysis.
How it works
The skill defines concrete classes and methods for:
- DataPreprocessor with methods analyze_data, detect_outliers, build_preprocessing_pipeline, transform_data, get_feature_importance, create_features. It uses Pandas, NumPy, scikit-learn, and SciPy to construct preprocessing pipelines, perform feature selection, and generate engineered features.
- StatisticalAnalyzer with methods descriptive_statistics, normality_tests, hypothesis_testing. It leverages SciPy, statsmodels, and Pingouin to compute descriptive stats, multiple normality assessments, and hypothesis tests. Code blocks show usage examples and integration points (e.g., creating pipelines, transforming data, extracting feature importances).
When to use it
Use when you need end-to-end data science workflows: data probing and cleaning, preprocessing pipelines, feature engineering, and rigorous statistics to support model development and evaluation. The skill emphasizes practical, executable patterns for preprocessing and analysis.
What it can touch
- Data: DataFrame inputs for preprocessing and analysis.
- Models and features: references to RandomForestClassifier for feature importance, and various feature engineering techniques.
- Libraries: Pandas, NumPy, scikit-learn, SciPy, statsmodels, Pingouin, etc.
Caveats
- The skill is described as production-grade and references a set of library versions; licensing is MIT. It uses abstracted components that may require adaptation for project-specific data schemas and environments.
- Some categorical handling uses a simplified approach (LabelEncoder or one-hot placeholder) within the pipeline construction, which may need adjustment for multi-category scenarios.
# Data Science & Analytics ## Level 1: Quick Reference ### Core Capabilities - **Data Processing**: Pandas 2.2.0, NumPy 1.26.0, Dask 2024.1.0 - **Machine Learning**: TensorFlow 2.20.0, PyTorch 2.9.0, Scikit-learn 1.7.2 - **Visualization**: Matplotlib 3.8.0, Seaborn 0.13.0, Plotly 5.17.0 - **Statistics**: SciPy 1.12.0, Statsmodels 0.14.0, Pingouin 0.8.0 - **Big Data**: Spark 3.5.0, Polars 0.20.0, Apache Arrow 14.0.0 - **Experimentation**: MLflow 2.9.0, Weights & Biases, Neptune ### Quick Setup Examples ```python # Data science workflow starter import pandas as pd import numpy as np import matplotlib.pyplot as plt import seaborn as sns from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier from sklearn.metrics import classification_report, confusion_matrix # Load and explore data df = pd.read_csv('data.csv') print(f"Dataset shape: {df.shape}") print(f"Missing values: {df.isnull().sum().sum()}") # Basic preprocessing X = df.drop('target', axis=1) y = df['target'] # Split data X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42) # Train model model = RandomForestClassifier(n_estimators=100, random_st
- Level 1: Quick Reference
- Core Capabilities
- Quick Setup Examples
- Level 2: Practical Implementation
- Data Processing & Analysis Pipeline
- Level 3: Advanced Integration
- Production ML Systems
- Related Skills
- Quick Start Checklist
- Data Science Best Practices
What does the moai-domain-data-science skill do?
Production-grade data science specialist with TensorFlow 2.20.0, PyTorch 2.9.0, Scikit-learn 1.7.2 expertise. Master data processing, ML pipeline development, model deployment, and statistical analysis. Build end-to-end data science solutions with comprehensive experimentation and visualization.
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill moai-domain-data-science-dolsoon-my-awesome-project --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.