Use this skill when performing exploratory data analysis, statistical testing, data visualization, or building predictive models. Triggers on EDA, pandas, matplotlib, seaborn, hypothesis testing, A/B test analysis, correlation, regression, feature engineering, and any task requiring data analysis or statistical inference.
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Use this skill when performing exploratory data analysis, statistical testing, data visualization, or building predictive models. Triggers on EDA, pandas, matplotlib, seaborn, hypothesis testing, A/B test analysis, correlation, regression, feature engineering, and any task requiring data analysis or statistical inference.
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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
Visualize before modeling - Plot every variable before fitting anything.
Distributions, outliers, and relationships invisible in summary statistics leap
out in charts. A histogram takes 2 seconds; debugging a model trained on bad
assumptions takes days.
Check your assumptions - Every statistical test has assumptions (normality,
equal variance, independence). Violating them silently produces misleading results.
Run the assumption check first, then choose the test.
Correlation is not causation - A strong correlation between X and Y might
mean X causes Y, Y causes X, a third variable Z causes both, or pure coincidence.
Never state causation from observational data without a causal framework.
Validate on holdout data - Any model evaluated on the same data it was trained
on is measuring memorization, not learning. Always split before fitting; never
peek at the test set to tune parameters.
Reproducible notebooks - Set random seeds (np.random.seed, ),
pin library versions, and document every data transformation in order. A result
you cannot reproduce is not a result.
random_state
Core concepts
Distributions describe how values are spread: normal (bell curve), skewed,
bimodal, uniform. Knowing the shape tells you which statistics are meaningful
(mean vs. median) and which tests are valid.
Central Limit Theorem - the mean of a large enough sample is approximately
normally distributed regardless of the population distribution. This is why t-tests
work on non-normal data with n > 30.
p-values measure the probability of observing your data (or more extreme) if
the null hypothesis were true. They do NOT measure the probability the null is true,
the effect size, or practical significance. A p-value < 0.05 is a threshold, not a
truth detector.
Confidence intervals give the range of plausible values for a parameter. A 95%
CI means: if you repeated the experiment 100 times, ~95 intervals would contain the
true value. Always report CIs alongside p-values - a significant result with a CI
spanning near-zero means the effect is tiny.
Bias-variance tradeoff - underfitting (high bias) means the model is too simple
to capture the signal; overfitting (high variance) means it captures noise too.
Cross-validation is the primary tool for diagnosing which problem you have.
Common tasks
EDA workflow
Load data and profile it systematically before any analysis:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
df = pd.read_csv("data.csv")
# Shape, types, missing valuesprint(df.shape)
print(df.dtypes)
print(df.isnull().sum().sort_values(ascending=False))
# Numeric summaryprint(df.describe())
# Categorical value countsfor col in df.select_dtypes("object"):
print(f"\n{col}:\n{df[col].value_counts().head(10)}")
# Distribution of each numeric feature
df.hist(bins=30, figsize=(14, 10))
plt.tight_layout()
plt.show()
# Correlation heatmap
plt.figure(figsize=(10, 8))
sns.heatmap(
df.select_dtypes("number").corr(),
annot=True, fmt=".2f", cmap="coolwarm", center=0
)
plt.show()
Always check df.duplicated().sum() and df.dtypes - columns that should be
numeric but are object type signal parsing issues or mixed data.
Data cleaning pipeline
Build a repeatable cleaning function rather than inline mutations:
Never peek at results mid-experiment to decide whether to stop. This inflates
the false positive rate. Use sequential testing (e.g., alpha spending) if you
need early stopping.
Visualization best practices
import matplotlib.pyplot as plt
import seaborn as sns
# Set a consistent style once at the top of the notebook
sns.set_theme(style="whitegrid", palette="muted", font_scale=1.1)
# Distribution comparison - violin > box when showing distribution shape
fig, axes = plt.subplots(1, 2, figsize=(12, 5))
sns.violinplot(data=df, x="group", y="value", ax=axes[0])
axes[0].set_title("Distribution by Group")
# Scatter with regression line - always show the uncertainty band
sns.regplot(data=df, x="feature", y="target", scatter_kws={"alpha": 0.3}, ax=axes[1])
axes[1].set_title("Feature vs Target")
plt.tight_layout()
# Time series - always label axes and use ISO date format
fig, ax = plt.subplots(figsize=(12, 4))
ax.plot(df["date"], df["metric"], color="steelblue", linewidth=1.5)
ax.fill_between(df["date"], df["lower_ci"], df["upper_ci"], alpha=0.2)
ax.set_xlabel("Date")
ax.set_ylabel("Metric")
ax.set_title("Metric Over Time with 95% CI")
plt.xticks(rotation=45)
plt.tight_layout()
Use alpha=0.3 on scatter plots when n > 1000 - overplotting hides the real
density. For very large datasets use sns.kdeplot or hexbin instead.
Feature engineering
from sklearn.preprocessing import StandardScaler, LabelEncoder
from sklearn.model_selection import train_test_split
# 1. Split first - to prevent leakage
X = df.drop("target", axis=1)
y = df["target"]
X_train, X_test, y_train, y_test = train_test_split(
X, y, test_size=0.2, random_state=42, stratify=y
)
# 2. Numeric features - fit scaler on train, transform both
scaler = StandardScaler()
num_cols = X_train.select_dtypes("number").columns
X_train[num_cols] = scaler.fit_transform(X_train[num_cols])
X_test[num_cols] = scaler.transform(X_test[num_cols]) # transform only, no fit# 3. Date features
df["hour"] = pd.to_datetime(df["timestamp"]).dt.hour
df["day_of_week"] = pd.to_datetime(df["timestamp"]).dt.dayofweek
df["is_weekend"] = df["day_of_week"].isin([5, 6]).astype(int)
# 4. Interaction features (only when domain knowledge suggests it)
df["price_per_sqft"] = df["price"] / df["sqft"].replace(0, np.nan)
# 5. Target encoding (use cross-val folds to prevent leakage)from category_encoders import TargetEncoder
encoder = TargetEncoder(smoothing=10)
X_train["cat_encoded"] = encoder.fit_transform(X_train["category"], y_train)
X_test["cat_encoded"] = encoder.transform(X_test["category"])
Feature leakage - fitting a scaler or encoder on the full dataset before
splitting - is the single most common modeling mistake. Always split first.
Linear and logistic regression
from sklearn.linear_model import LinearRegression, LogisticRegression
from sklearn.metrics import (
mean_squared_error, r2_score,
classification_report, roc_auc_score
)
import statsmodels.api as sm
# Linear regression with statistical output (p-values, CIs)
X_with_const = sm.add_constant(X_train[["feature_1", "feature_2"]])
ols_model = sm.OLS(y_train, X_with_const).fit()
print(ols_model.summary()) # Shows coefficients, p-values, R-squared# Sklearn for prediction pipeline
lr = LinearRegression()
lr.fit(X_train[num_cols], y_train)
y_pred = lr.predict(X_test[num_cols])
print(f"RMSE: {mean_squared_error(y_test, y_pred, squared=False):.4f}")
print(f"R2: {r2_score(y_test, y_pred):.4f}")
# Logistic regression
clf = LogisticRegression(max_iter=1000, random_state=42)
clf.fit(X_train[num_cols], y_train)
y_prob = clf.predict_proba(X_test[num_cols])[:, 1]
print(classification_report(y_test, clf.predict(X_test[num_cols])))
print(f"ROC-AUC: {roc_auc_score(y_test, y_prob):.4f}")
Use statsmodels when you need p-values and confidence intervals for
coefficients (inference). Use sklearn when you need prediction pipelines,
cross-validation, and integration with other estimators.
Anti-patterns / common mistakes
Mistake
Why it's wrong
What to do instead
Analyzing the test set before the experiment is over
Inflates false positive rate (p-hacking)
Pre-register sample size, run full duration, analyze once
Fitting scaler/encoder on full dataset before splitting
Test set leaks into training, inflates evaluation metrics
Always train_test_split first, then fit_transform train only
Reporting p-value without effect size
A tiny effect with huge n produces p < 0.05; means nothing practical
Always report Cohen's d, odds ratio, or relative lift alongside p
Using mean on skewed distributions
Mean is pulled by outliers; misrepresents the typical value
Report median and IQR for skewed data; log-transform for modeling
Imputing after splitting
Future information leaks from test to train set
Split first, impute train separately, apply same transform to test
Dropping all rows with missing data
Loses information, can introduce bias if not MCAR
Use median/mode imputation or model-based imputation (IterativeImputer)
Gotchas
Feature leakage from fitting transformers before splitting - Fitting a StandardScaler, LabelEncoder, or imputer on the full dataset before train_test_split leaks test set statistics into training. The model then appears to generalize well but fails in production. Always split first, then fit_transform on train only, and transform on test.
Peeking at results mid-experiment inflates false positive rate - Running a significance test daily and stopping as soon as p < 0.05 is reached is p-hacking. The actual false positive rate can reach 30%+ instead of the nominal 5%. Pre-register your sample size, run the full duration, and analyze once. Use sequential testing (alpha spending) if early stopping is a genuine business requirement.
Shapiro-Wilk normality test unreliable above n=5000 - With large samples, Shapiro-Wilk becomes so sensitive it rejects normality for trivially small deviations that don't matter practically. For n > 5000, use visual diagnostics (Q-Q plot, histogram) instead of the test, and prefer non-parametric tests (Mann-Whitney U) or rely on the Central Limit Theorem for means.
df.copy() omission causes silent SettingWithCopyWarning mutations - Chained indexing on a pandas slice (df[mask]["col"] = value) silently fails to modify the original DataFrame. Always call .copy() when creating a subset DataFrame you intend to modify. Pandas 2.0+ converts this from a warning to an error, so existing code that worked may break on upgrade.
Outlier removal before splitting contaminates the test set - Applying IQR outlier removal to the full dataset before splitting removes some test set rows based on information from the training distribution. This is a subtle form of data leakage. Apply outlier handling only within the training fold during cross-validation or after splitting.
References
For deeper guidance on specific topics, load the relevant references file:
references/statistical-tests.md - decision tree for choosing the right test,
assumption checks, and non-parametric alternatives
Only load references files when the current task requires them - they are detailed
and will consume context.
Companion check
On first activation of this skill in a conversation: check which companion skills are installed by running ls ~/.claude/skills/ ~/.agent/skills/ ~/.agents/skills/ .claude/skills/ .agent/skills/ .agents/skills/ 2>/dev/null. Compare the results against the recommended_skills field in this file's frontmatter. For any that are missing, mention them once and offer to install:
