data-sciencelisted

When this skill is activated, always start your first response with the 🧢 emoji. # Data Science A practitioner's guide for exploratory data analysis, statistical inference, and predictive modeling. Covers the full analytical workflow - from raw data to reproducible conclusions - with an emphasis on *when* to apply each technique, not just *how*. Designed for engineers and analysts who can code but need opinionated guidance on statistical rigor and common traps. --- ## When to use this skill Trigger this skill when the user: - Loads a new dataset and wants to understand its structure and distributions - Needs to clean, reshape, or impute missing data in a pandas DataFrame - Runs a hypothesis test (t-test, chi-square, ANOVA, Mann-Whitney) - Analyzes an A/B test or experiment result for statistical significance - Builds a correlation matrix or investigates feature relationships - Plots distributions, trends, or model diagnostics with matplotlib or seaborn - Engineers features for a machine learning model - Fits a linear or logistic regression and needs to interpret coefficients - Calculates confidence intervals, p-values, or effect sizes - Needs to choose the right statistical test for their data type Do NOT trigger this skill for: - Deep learning / neural network architecture (use an ML engineering skill) - Data engineering pipelines, ETL, or streaming (use a data engineering skill) --- ## Key principles 1. **Visualize before modeling** - Plot every variable before fi