Top 50 Most Asked Data Science Interview Questions

import numpy as np def haversine_km(lat1, lon1, lat2, lon2): lat1, lon1, lat2, lon2 = map(np.radians, [lat1, lon1, lat2, lon2]) dlat, dlon = lat2 - lat1, lon2 - lon1 a = np.sin(dlat / 2) ** 2 + np.cos(lat1) * np.cos(lat2) * np.sin(dlon / 2) ** 2 return 6371 * 2 * np.arcsin(np.sqrt(a)) df['dist_km'] = haversine_km(df.cust_lat, df.cust_lon, df.store_lat, df.store_lon)

WITH daily AS ( SELECT activity_date, COUNT(DISTINCT user_id) AS dau FROM events GROUP BY activity_date ) SELECT activity_date, dau, AVG(dau) OVER (ORDER BY activity_date ROWS BETWEEN 6 PRECEDING AND CURRENT ROW) AS dau_7d_avg FROM daily ORDER BY activity_date;

df.groupby('region').agg(total=('sales','sum'), avg=('sales','mean')) gives named aggregations per group in one pass.

import torch, torch.nn as nn from torchvision import models model = models.resnet18(weights=models.ResNet18_Weights.DEFAULT) for p in model.parameters(): p.requires_grad = False # freeze backbone model.fc = nn.Linear(model.fc.in_features, 5) opt = torch.optim.AdamW(model.fc.parameters(), lr=1e-3) loss_fn = nn.CrossEntropyLoss() # train head first, then unfreeze later layers with a lower lr

from statsmodels.stats.power import NormalIndPower from statsmodels.stats.proportion import proportion_effectsize baseline, target = 0.05, 0.055 effect = proportion_effectsize(target, baseline) # Cohen's h n = NormalIndPower().solve_power( effect_size=effect, alpha=0.05, power=0.8, ratio=1.0, alternative='two-sided' ) print(round(n)) # about 31,000 users per group

# process 1 million rows at a time import pandas as pd totals = {} for chunk in pd.read_csv('sales.csv', usecols=['city', 'amount'], chunksize=1_000_000): part = chunk.groupby('city')['amount'].sum() for city, amt in part.items(): totals[city] = totals.get(city, 0) + amt result = pd.Series(totals).sort_values(ascending=False)

(df.assign(rev=df.price * df.qty) .groupby(['region', 'product'])['rev'].sum() .reset_index() .sort_values('rev', ascending=False) .groupby('region').head(3))

SELECT customer_id, MIN(order_date) AS first_order, MAX(order_date) AS last_order, MAX(order_date) - MIN(order_date) AS days_between, COUNT(*) AS orders FROM orders GROUP BY customer_id ORDER BY days_between DESC; -- PostgreSQL: DATE minus DATE gives days; use DATEDIFF in other engines

import numpy as np from scipy import stats ctrl = df.loc[df.variant == 'control', 'revenue'].to_numpy() treat = df.loc[df.variant == 'treatment', 'revenue'].to_numpy() t, p = stats.ttest_ind(treat, ctrl, equal_var=False) # Welch diff = treat.mean() - ctrl.mean() se = np.sqrt(treat.var(ddof=1) / len(treat) + ctrl.var(ddof=1) / len(ctrl)) print(f'diff={diff:.2f} 95% CI=({diff - 1.96*se:.2f}, {diff + 1.96*se:.2f}) p={p:.4f}')

import numpy as np def psi(expected, actual, bins=10): edges = np.quantile(expected, np.linspace(0, 1, bins + 1)) edges[0], edges[-1] = -np.inf, np.inf e = np.histogram(expected, edges)[0] / len(expected) a = np.histogram(actual, edges)[0] / len(actual) e, a = np.clip(e, 1e-6, None), np.clip(a, 1e-6, None) return float(np.sum((a - e) * np.log(a / e))) # rule of thumb: <0.1 stable, 0.1-0.25 watch, >0.25 investigate

from pydantic import BaseModel, Field, ValidationError class LoanRequest(BaseModel): age: int = Field(ge=18, le=100) income: float = Field(gt=0) city: str = Field(min_length=2) def predict(payload: dict): try: req = LoanRequest(**payload) except ValidationError as e: return {'error': e.errors()} return {'score': float(model.predict_proba([[req.age, req.income]])[0, 1])}

from sklearn.metrics import confusion_matrix, precision_score, recall_score proba = model.predict_proba(X_test)[:, 1] pred = (proba >= 0.3).astype(int) print(confusion_matrix(y_test, pred)) print('precision', precision_score(y_test, pred)) print('recall', recall_score(y_test, pred))

from sklearn.compose import ColumnTransformer from sklearn.pipeline import make_pipeline from sklearn.impute import SimpleImputer from sklearn.preprocessing import StandardScaler, OneHotEncoder pre = ColumnTransformer([ ('num', make_pipeline(SimpleImputer(strategy='median'), StandardScaler()), num_cols), ('cat', make_pipeline(SimpleImputer(strategy='most_frequent'), OneHotEncoder(handle_unknown='ignore')), cat_cols) ]) model = make_pipeline(pre, LogisticRegression(max_iter=1000))

WITH monthly AS ( SELECT DATE_TRUNC('month', order_date) AS month, SUM(amount) AS revenue FROM orders GROUP BY 1 ) SELECT month, revenue, LAG(revenue) OVER (ORDER BY month) AS prev_revenue, ROUND(100.0 * (revenue - LAG(revenue) OVER (ORDER BY month)) / NULLIF(LAG(revenue) OVER (ORDER BY month), 0), 2) AS mom_pct FROM monthly;

import numpy as np def top_k(query_vec, doc_vecs, k=3): q = query_vec / np.linalg.norm(query_vec) d = doc_vecs / np.linalg.norm(doc_vecs, axis=1, keepdims=True) scores = d @ q idx = np.argsort(-scores)[:k] return list(zip(idx, scores[idx])) # doc_vecs shape: (n_docs, dim), query_vec shape: (dim,)

FROM python:3.11-slim WORKDIR /app COPY requirements.txt . RUN pip install --no-cache-dir -r requirements.txt COPY app/ ./app COPY model.joblib . EXPOSE 8000 CMD ["uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8000"]

import pandas as pd from features import days_since_last_purchase def test_basic_case(): df = pd.DataFrame({'customer_id': [1, 1], 'order_date': pd.to_datetime(['2026-01-01', '2026-01-10'])}) out = days_since_last_purchase(df, as_of='2026-01-15') assert out.loc[1] == 5 def test_no_future_orders_used(): df = pd.DataFrame({'customer_id': [1], 'order_date': pd.to_datetime(['2026-02-01'])}) out = days_since_last_purchase(df, as_of='2026-01-15') assert out.empty

from sklearn.ensemble import HistGradientBoostingClassifier from sklearn.calibration import CalibratedClassifierCV from sklearn.metrics import brier_score_loss base = HistGradientBoostingClassifier() cal = CalibratedClassifierCV(base, method='isotonic', cv=5) cal.fit(X_train, y_train) prob = cal.predict_proba(X_test)[:, 1] print('Brier:', brier_score_loss(y_test, prob))

# df has columns: store, date, sales df['date'] = pd.to_datetime(df['date']) df = df.sort_values(['store', 'date']) df['sales_7d_avg'] = ( df.groupby('store')['sales'] .transform(lambda s: s.rolling(7, min_periods=1).mean()) )