data-science

name	data-science
description	Data science methodology — EDA, feature engineering, model selection, evaluation metrics, and production ML patterns.

Data Science Skill

Exploratory Data Analysis (EDA) Checklist

Every dataset should go through:

1. Shape & Types: rows, columns, dtypes — flag unexpected types
2. Missing Values: count, percentage, pattern (MCAR / MAR / MNAR)
3. Distributions: histogram for numeric, bar chart for categorical
4. Outliers: IQR method (Q1 - 1.5×IQR, Q3 + 1.5×IQR), Z-score > 3
5. Correlations: Pearson for numeric, Cramér's V for categorical
6. Target Leakage: features that could not be known at prediction time

Feature Engineering Patterns

• Numeric: log-transform skewed features, standardise (μ=0, σ=1), bin into quantiles
• Categorical: one-hot encode (low cardinality), target encode (high cardinality), embeddings
• Temporal: extract hour/day/month/year, encode cyclically (sin/cos for hour, day-of-week)
• Text: TF-IDF for sparse, sentence embeddings for semantic tasks
• Interactions: multiply correlated features, polynomial features for nonlinear boundaries

Model Selection Guide

Problem Type	Try First	Also Consider
Classification	XGBoost / LightGBM	Logistic Regression, RF
Regression	XGBoost / LightGBM	Ridge, ElasticNet, SVR
Time Series	Prophet, ARIMA	LSTM, N-BEATS, TFT
Clustering	K-Means	DBSCAN, Hierarchical
NLP	Fine-tuned LLM	TF-IDF + LR, FastText
Recommendation	Matrix Factorisation	Neural CF, Two-Tower

Evaluation Metrics — Which One to Use

• Classification (balanced): F1-Score, AUC-ROC
• Classification (imbalanced): Precision-Recall AUC, F1
• Regression: RMSE (penalises large errors), MAE (robust to outliers), MAPE
• Ranking: NDCG, MAP, MRR
• Clustering: Silhouette Score, Davies-Bouldin

Production ML Checklist

Before shipping a model:

• Train/validation/test split is time-based for temporal data
• No data leakage between folds
• Baseline model established (random, majority class, mean predictor)
• Calibration check for probability outputs
• Feature importance documented
• Monitoring plan: data drift (PSI), concept drift, latency SLA
• Rollback strategy defined

Common Pitfalls to Flag

• Data leakage: using future information during training
• Class imbalance not addressed
• Feature scaling applied before train/test split (causes leakage)
• Evaluating on training set only
• Ignoring class weights for imbalanced targets
• Not baseline benchmarking before complex models