Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub ยท Productivity
Senior Data Scientist
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.
skill openclawclawhub Free
World-class data science skill for statistical modeling, experimentation, causal inference, and advanced analytics. Expertise in Python (NumPy, Pandas, Scikit-learn), R, SQL, statistical methods, A/B testing, time series, and business intelligence. Includes experiment design, feature engineering, model evaluation, and stakeholder communication. Use when designing experiments, building predictive models, performing causal analysis, or driving data-driven decisions.
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- Download the package from Yavira.
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Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md, references/experiment_design_frameworks.md, references/feature_engineering_patterns.md, references/statistical_methods_advanced.md, scripts/experiment_designer.py, scripts/feature_engineering_pipeline.py
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Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 2.1.1
Provenance
- Publisher
- alirezarezvani
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Documentation
Senior Data Scientist
World-class senior data scientist skill for production-grade AI/ML/Data systems.
1. Design an A/B Test
import numpy as np from scipy import stats def calculate_sample_size(baseline_rate, mde, alpha=0.05, power=0.8): """ Calculate required sample size per variant. baseline_rate: current conversion rate (e.g. 0.10) mde: minimum detectable effect (relative, e.g. 0.05 = 5% lift) """ p1 = baseline_rate p2 = baseline_rate * (1 + mde) effect_size = abs(p2 - p1) / np.sqrt((p1 * (1 - p1) + p2 * (1 - p2)) / 2) z_alpha = stats.norm.ppf(1 - alpha / 2) z_beta = stats.norm.ppf(power) n = ((z_alpha + z_beta) / effect_size) ** 2 return int(np.ceil(n)) def analyze_experiment(control, treatment, alpha=0.05): """ Run two-proportion z-test and return structured results. control/treatment: dicts with 'conversions' and 'visitors'. """ p_c = control["conversions"] / control["visitors"] p_t = treatment["conversions"] / treatment["visitors"] pooled = (control["conversions"] + treatment["conversions"]) / (control["visitors"] + treatment["visitors"]) se = np.sqrt(pooled * (1 - pooled) * (1 / control["visitors"] + 1 / treatment["visitors"])) z = (p_t - p_c) / se p_value = 2 * (1 - stats.norm.cdf(abs(z))) ci_low = (p_t - p_c) - stats.norm.ppf(1 - alpha / 2) * se ci_high = (p_t - p_c) + stats.norm.ppf(1 - alpha / 2) * se return { "lift": (p_t - p_c) / p_c, "p_value": p_value, "significant": p_value < alpha, "ci_95": (ci_low, ci_high), } # --- Experiment checklist --- # 1. Define ONE primary metric and pre-register secondary metrics. # 2. Calculate sample size BEFORE starting: calculate_sample_size(0.10, 0.05) # 3. Randomise at the user (not session) level to avoid leakage. # 4. Run for at least 1 full business cycle (typically 2 weeks). # 5. Check for sample ratio mismatch: abs(n_control - n_treatment) / expected < 0.01 # 6. Analyze with analyze_experiment() and report lift + CI, not just p-value. # 7. Apply Bonferroni correction if testing multiple metrics: alpha / n_metrics
2. Build a Feature Engineering Pipeline
import pandas as pd import numpy as np from sklearn.pipeline import Pipeline from sklearn.preprocessing import StandardScaler, OneHotEncoder from sklearn.impute import SimpleImputer from sklearn.compose import ColumnTransformer def build_feature_pipeline(numeric_cols, categorical_cols, date_cols=None): """ Returns a fitted-ready ColumnTransformer for structured tabular data. """ numeric_pipeline = Pipeline([ ("impute", SimpleImputer(strategy="median")), ("scale", StandardScaler()), ]) categorical_pipeline = Pipeline([ ("impute", SimpleImputer(strategy="most_frequent")), ("encode", OneHotEncoder(handle_unknown="ignore", sparse_output=False)), ]) transformers = [ ("num", numeric_pipeline, numeric_cols), ("cat", categorical_pipeline, categorical_cols), ] return ColumnTransformer(transformers, remainder="drop") def add_time_features(df, date_col): """Extract cyclical and lag features from a datetime column.""" df = df.copy() df[date_col] = pd.to_datetime(df[date_col]) df["dow_sin"] = np.sin(2 * np.pi * df[date_col].dt.dayofweek / 7) df["dow_cos"] = np.cos(2 * np.pi * df[date_col].dt.dayofweek / 7) df["month_sin"] = np.sin(2 * np.pi * df[date_col].dt.month / 12) df["month_cos"] = np.cos(2 * np.pi * df[date_col].dt.month / 12) df["is_weekend"] = (df[date_col].dt.dayofweek >= 5).astype(int) return df # --- Feature engineering checklist --- # 1. Never fit transformers on the full dataset โ fit on train, transform test. # 2. Log-transform right-skewed numeric features before scaling. # 3. For high-cardinality categoricals (>50 levels), use target encoding or embeddings. # 4. Generate lag/rolling features BEFORE the train/test split to avoid leakage. # 5. Document each feature's business meaning alongside its code.
3. Train, Evaluate, and Select a Prediction Model
from sklearn.model_selection import StratifiedKFold, cross_validate from sklearn.metrics import make_scorer, roc_auc_score, average_precision_score import xgboost as xgb import mlflow SCORERS = { "roc_auc": make_scorer(roc_auc_score, needs_proba=True), "avg_prec": make_scorer(average_precision_score, needs_proba=True), } def evaluate_model(model, X, y, cv=5): """ Cross-validate and return mean ยฑ std for each scorer. Use StratifiedKFold for classification to preserve class balance. """ cv_results = cross_validate( model, X, y, cv=StratifiedKFold(n_splits=cv, shuffle=True, random_state=42), scoring=SCORERS, return_train_score=True, ) summary = {} for metric in SCORERS: test_scores = cv_results[f"test_{metric}"] summary[metric] = {"mean": test_scores.mean(), "std": test_scores.std()} # Flag overfitting: large gap between train and test score train_mean = cv_results[f"train_{metric}"].mean() summary[metric]["overfit_gap"] = train_mean - test_scores.mean() return summary def train_and_log(model, X_train, y_train, X_test, y_test, run_name): """Train model and log all artefacts to MLflow.""" with mlflow.start_run(run_name=run_name): model.fit(X_train, y_train) proba = model.predict_proba(X_test)[:, 1] metrics = { "roc_auc": roc_auc_score(y_test, proba), "avg_prec": average_precision_score(y_test, proba), } mlflow.log_params(model.get_params()) mlflow.log_metrics(metrics) mlflow.sklearn.log_model(model, "model") return metrics # --- Model evaluation checklist --- # 1. Always report AUC-PR alongside AUC-ROC for imbalanced datasets. # 2. Check overfit_gap > 0.05 as a warning sign of overfitting. # 3. Calibrate probabilities (Platt scaling / isotonic) before production use. # 4. Compute SHAP values to validate feature importance makes business sense. # 5. Run a baseline (e.g. DummyClassifier) and verify the model beats it. # 6. Log every run to MLflow โ never rely on notebook output for comparison.
4. Causal Inference: Difference-in-Differences
import statsmodels.formula.api as smf def diff_in_diff(df, outcome, treatment_col, post_col, controls=None): """ Estimate ATT via OLS DiD with optional covariates. df must have: outcome, treatment_col (0/1), post_col (0/1). Returns the interaction coefficient (treatment ร post) and its p-value. """ covariates = " + ".join(controls) if controls else "" formula = ( f"{outcome} ~ {treatment_col} * {post_col}" + (f" + {covariates}" if covariates else "") ) result = smf.ols(formula, data=df).fit(cov_type="HC3") interaction = f"{treatment_col}:{post_col}" return { "att": result.params[interaction], "p_value": result.pvalues[interaction], "ci_95": result.conf_int().loc[interaction].tolist(), "summary": result.summary(), } # --- Causal inference checklist --- # 1. Validate parallel trends in pre-period before trusting DiD estimates. # 2. Use HC3 robust standard errors to handle heteroskedasticity. # 3. For panel data, cluster SEs at the unit level (add groups= param to fit). # 4. Consider propensity score matching if groups differ at baseline. # 5. Report the ATT with confidence interval, not just statistical significance.
Reference Documentation
Statistical Methods: references/statistical_methods_advanced.md Experiment Design Frameworks: references/experiment_design_frameworks.md Feature Engineering Patterns: references/feature_engineering_patterns.md
Common Commands
# Testing & linting python -m pytest tests/ -v --cov=src/ python -m black src/ && python -m pylint src/ # Training & evaluation python scripts/train.py --config prod.yaml python scripts/evaluate.py --model best.pth # Deployment docker build -t service:v1 . kubectl apply -f k8s/ helm upgrade service ./charts/ # Monitoring & health kubectl logs -f deployment/service python scripts/health_check.py
Category context
Workflow acceleration for inboxes, docs, calendars, planning, and execution loops.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
4 Docs2 Scripts
- SKILL.md
- references/experiment_design_frameworks.md
- references/feature_engineering_patterns.md
- references/statistical_methods_advanced.md
- scripts/experiment_designer.py
- scripts/feature_engineering_pipeline.py