Data Science Interview Questions in Pune

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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))

How do you evaluate clustering when you have no labels?

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Use internal metrics: the silhouette score compares how close a point is to its own cluster versus the nearest other cluster, and the Davies-Bouldin index measures cluster overlap. The elbow method looks at within-cluster variance. But the real test is usefulness: are clusters stable across random seeds and samples, can the business describe them, and do they behave differently on outcomes like spend or churn?

Write a scikit-learn ColumnTransformer that preprocesses numeric and categorical columns.

Feature EngineeringMedium

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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))

Pandas: find customers whose monthly spend increased for three consecutive months.

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m = (df.assign(month=df['date'].dt.to_period('M')) .groupby(['customer_id', 'month'])['amount'].sum() .reset_index() .sort_values(['customer_id', 'month'])) m['up'] = (m.groupby('customer_id')['amount'].diff() > 0).astype(int) # three rises in a row = three consecutive increases m['streak'] = (m.groupby('customer_id')['up'] .rolling(3).sum().reset_index(level=0, drop=True)) result = m.loc[m['streak'] == 3, 'customer_id'].unique()

What is transfer learning?

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Reusing a model pretrained on a large dataset and fine-tuning it on a smaller task-specific dataset, saving data, time and compute.

What is non-maximum suppression in object detection?

Computer VisionEasy

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A detector usually predicts many overlapping boxes for the same object. Non-maximum suppression keeps the box with the highest confidence, removes other boxes of the same class whose IoU with it exceeds a threshold such as 0.5, and repeats with the remaining boxes. A threshold that is too low can delete genuinely nearby objects in crowded scenes, so variants like soft-NMS lower scores instead of removing boxes.

What is sampling bias and how can you reduce it?

Statistics and ProbabilityMedium

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Bias occurs when the sample does not represent the population. Reduce it with random or stratified sampling, higher response rates and by checking sample demographics against the population.

Write torchvision transforms for training and validation of an image classifier using ImageNet normalisation.

Computer VisionEasy

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from torchvision import transforms as T mean, std = [0.485, 0.456, 0.406], [0.229, 0.224, 0.225] train_tf = T.Compose([ T.RandomResizedCrop(224), T.RandomHorizontalFlip(), T.ColorJitter(0.2, 0.2, 0.2), T.ToTensor(), T.Normalize(mean, std), ]) val_tf = T.Compose([ T.Resize(256), T.CenterCrop(224), T.ToTensor(), T.Normalize(mean, std), ])

How does the Gini impurity differ from entropy?

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Both measure node impurity; Gini is cheaper to compute and entropy is slightly more sensitive to class balance, with similar trees in practice.

What is NumPy broadcasting?

Python for Data ScienceEasy

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The rule that lets arrays of different shapes operate together by virtually stretching the smaller one, for example adding a scalar or a (3,) vector to a (4,3) matrix.

What should you log for a production prediction service?

MLOps and Model DeploymentMedium

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For each request, log a request ID, timestamp, model version, input features or a hash of them, the prediction and score, and latency. Log errors and fallback use separately. Do not log raw personal data unless needed and allowed; mask it. Aggregate these logs into dashboards for traffic, latency percentiles, error rate and score distribution. Joining logged predictions with later outcomes gives the data for monitoring and retraining.

Your GPU model-serving bill has doubled while traffic grew only 20 percent. How do you bring cost down?

MLOps and Model DeploymentHard

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Profile utilisation first; idle or under-filled GPUs are common. Enable dynamic batching so each GPU call handles several requests, right-size instance types and autoscale on queue depth rather than CPU. Reduce model cost with quantisation, distillation or a smaller model for easy requests. Cache repeated predictions, move non-urgent work to batch jobs on cheaper hardware, and track cost per thousand predictions as a standing metric.

How do you perform feature selection?

Feature EngineeringMedium

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Use filter methods (correlation, chi-square), wrapper methods (RFE), embedded methods (Lasso, tree importance) and domain knowledge, validating each choice with cross-validation.

Pandas: find the top 3 products by revenue in each region.

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

What is the Global Interpreter Lock (GIL)?

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A mutex in CPython that lets only one thread execute Python bytecode at a time. Use multiprocessing for CPU-bound work and threads or asyncio for I/O-bound work.

What is quantisation of LLMs?

Generative AI and LLMsMedium

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Quantisation stores model weights in lower precision, such as 8-bit or 4-bit integers instead of 16-bit floats. A 7-billion parameter model drops from about 14 GB to around 4 GB at 4-bit, so it fits on smaller GPUs or even laptops and runs faster. The cost is some loss in quality, usually small at 8-bit and noticeable on hard tasks at very low bits. Common formats include GPTQ, AWQ and GGUF.

Which activation functions do you know and when to use them?

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ReLU for hidden layers, sigmoid for binary outputs, softmax for multi-class outputs and tanh when zero-centred outputs help; Leaky ReLU avoids dead neurons.

A feature won its A/B test with a 4 percent lift, but after full launch the metric barely moved. Why might that happen?

A/B Testing & ExperimentationMedium

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Several reasons are common. Winner's curse: significant results from underpowered tests tend to overestimate the true effect. Novelty effect faded after launch. Seasonality or other launches changed the baseline, so a before-after comparison is misleading. The test population may differ from all users, for example only one platform. Use a holdout after launch to measure real impact, and run tests with adequate power and duration.

How does K-means clustering work and how do you choose k?

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It assigns points to the nearest centroid and updates centroids until stable. Choose k with the elbow method, silhouette score or business context.

What is the difference between iterators and iterables in Python?

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An iterable can produce an iterator (it has __iter__); an iterator has __next__ and remembers its position. A for-loop calls iter() on the iterable and then next() repeatedly.

What are Python's main built-in data types?

Python for Data ScienceEasy

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int, float, complex, bool, str, list, tuple, set, dict and NoneType. Mutable ones are list, set and dict; the others are immutable.

Write a prompt template for classifying customer support tickets.

Generative AI and LLMsMedium

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System: You classify support tickets for an e-commerce company. Categories: payment, delivery, refund, account, other. Rules: choose exactly one category; if unsure, choose other; do not invent details. Return JSON with fields category and reason, reason under 20 words. Then two short examples showing a ticket and the expected JSON. User: Ticket: {ticket_text}. Keep the template fixed and test it on 100 labelled tickets before rollout.

What is overfitting and how do you prevent it?

Machine LearningEasy

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The model memorises noise and fails on new data. Prevent it with more data, simpler models, regularisation, cross-validation, early stopping and dropout.

A deep network you built from scratch does not learn at all; the loss stays flat from the first epoch. Could weight initialisation be the reason?

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If weights start too large, activations and gradients explode; if too small, they vanish, and training stalls. If all weights are equal, every neuron learns the same thing. Good schemes keep the variance of activations stable across layers: Xavier or Glorot initialisation for tanh and sigmoid, and He initialisation for ReLU. Frameworks use sensible defaults, but it matters when building custom layers or very deep networks.

You must classify support messages into 40 categories, but some categories have only 20 examples. What do you do?

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Start with a pretrained sentence embedding model plus a simple classifier, or fine-tune a small transformer with class weights. For the rare categories, try few-shot approaches such as SetFit or an LLM with examples in the prompt, and generate or collect more examples. Merge categories that are rarely distinguishable. Report per-class F1 rather than overall accuracy, and route low-confidence predictions to a human queue.

Explain logistic regression.

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It models the log-odds of a binary outcome as a linear function and passes it through a sigmoid to give probabilities, trained by maximising likelihood.

After joining orders with a promotions table, total revenue doubled. What went wrong?

SQL for Data ScienceMedium

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The join created duplicate order rows, because some orders match more than one promotion row, so each amount is counted several times. Check by comparing COUNT(*) with COUNT(DISTINCT order_id) after the join. Fix it by aggregating the promotions table to one row per order first, picking one promotion with ROW_NUMBER, or summing revenue before the join. Always check row counts after every join.

What is a confusion matrix?

Machine LearningEasy

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A table of true positives, false positives, true negatives and false negatives from which accuracy, precision, recall and F1 are calculated.

Your dataset is highly imbalanced (2% positives). How do you handle it?

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Use stratified splits, class weights, resampling (SMOTE or undersampling) and threshold tuning, and judge the model with PR-AUC or F1 instead of accuracy.

Write Pandas code to create lag and rolling features for each customer.

Feature EngineeringMedium

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df = df.sort_values(['customer_id', 'month']) g = df.groupby('customer_id')['spend'] df['spend_lag1'] = g.shift(1) df['spend_lag3'] = g.shift(3) # shift first so the current month is not included df['spend_roll3_mean'] = g.transform(lambda s: s.shift(1).rolling(3).mean())

What are context managers in Python?

Python for Data ScienceEasy

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A context manager sets something up and guarantees cleanup when a block ends, even if an error is raised. You use it with the with statement, for example with open('data.csv') as f, which always closes the file. It works through __enter__ and __exit__ methods, or the contextlib.contextmanager decorator. In data work it is used for files, database connections, locks and timers, so resources are never left open.

Your detector finds large objects well but misses small objects like distant people. How do you improve it?

Computer VisionHard

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Small objects lose detail after downsampling. Train and infer at higher input resolution, or tile large images into overlapping crops. Use a backbone with a feature pyramid so detection happens on high-resolution maps. Tune anchors or assignment settings for small sizes, and oversample images with small objects. Check labelling quality for tiny boxes too. Measure mAP on small objects separately to confirm improvement.

What is SVM and the kernel trick?