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Data Science Interview Questions for Analytics Companies

Q: Which analytics companies are covered?

This page links to 11 analytics companies, including EXL, Mu Sigma, Fractal Analytics, LatentView Analytics, Tiger Analytics, Tredence, Sigmoid, Quantiphi. Each has its own page with rounds and a separate practice set.

Q: What rounds should I expect?

Common rounds at these companies include: HR round; Technical interview; Case or guesstimate round; Technical round 1; Technical round 2 or case round. The exact process varies by company and role.

40 questions weighted to the interview style of analytics companies — 8 easy, 26 medium, 6 hard.

How analytics companies interview for Data Science

Analytics and data-services firms test practical skill hard: SQL, Excel or Python tasks, statistics, and business cases where you turn data into a recommendation. Many run a take-home or live case round in addition to technical interviews.

This set focuses on Deep Learning and NLP (9), Feature Engineering (5), Statistics and Probability (4), Python for Data Science (4).

HR round Technical interview Case or guesstimate round Technical round 1 Technical round 2 or case round

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Correlation vs covariance?

Statistics and ProbabilityEasy

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Covariance shows the direction of a relationship but depends on units. Correlation is covariance scaled to −1…+1, so it shows direction and strength and is unit-free.

What are type hints in Python and why do data teams use them?

Python for Data ScienceEasy

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Type hints annotate expected types, for example def clean(df: pd.DataFrame, cols: list[str]) -> pd.DataFrame. Python does not enforce them at runtime, but tools like mypy and IDEs use them to catch mistakes early and give better autocomplete. In data teams they make shared pipeline code easier to read and review. They matter most in production code and libraries, less in quick exploratory notebooks.

Write Python code to compute a 95% bootstrap confidence interval for the mean.

Statistics and ProbabilityMedium

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import numpy as np def bootstrap_ci(x, n_boot=10000, seed=42): rng = np.random.default_rng(seed) x = np.asarray(x) means = [rng.choice(x, size=len(x), replace=True).mean() for _ in range(n_boot)] return np.percentile(means, [2.5, 97.5]) print(bootstrap_ci([12, 15, 9, 20, 14, 11, 18]))

Write Python code to evaluate a forecasting model with expanding-window time series cross-validation.

Time Series & ForecastingMedium

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import numpy as np from sklearn.model_selection import TimeSeriesSplit from sklearn.metrics import mean_absolute_error tscv = TimeSeriesSplit(n_splits=5, test_size=28) scores = [] for tr, te in tscv.split(X): model.fit(X.iloc[tr], y.iloc[tr]) pred = model.predict(X.iloc[te]) scores.append(mean_absolute_error(y.iloc[te], pred)) print(np.mean(scores), scores)

What is the difference between a set and a dictionary?

Python for Data ScienceEasy

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A set stores unique values with no keys; a dictionary stores key-value pairs with unique keys. Both use hashing, so lookups are O(1) on average.

What is named entity recognition?

Deep Learning and NLPEasy

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Named entity recognition finds and labels spans of text such as person names, organisations, locations, dates and amounts. For example, in an invoice email it can pull out the vendor, invoice number and total. It is usually framed as token classification with BIO tags. spaCy and fine-tuned transformer models are common tools. Domain-specific entities, like medicine names, usually need custom labelled training data.

Describe a project where you worked with very little labelled data.

Deep Learning and NLPHard

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Situation: we had to classify 50,000 support tickets into 12 categories with only 600 labelled. Task: reach usable accuracy in a month. Action: I used a pretrained sentence embedding model with logistic regression, then active learning to pick the most uncertain tickets for agents to label in short daily batches. Result: with 2,000 labels we reached 86 percent macro F1. I learned that smart labelling beats waiting for perfect data.

What is a confidence interval?

Statistics and ProbabilityMedium

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A range built from sample data that would contain the true parameter in a stated percentage (for example 95%) of repeated samples. It is not the probability that the parameter lies inside this one interval.

Difference between a shallow copy and a deep copy?

Python for Data ScienceMedium

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A shallow copy duplicates the outer object but shares nested objects. A deep copy (copy.deepcopy) recursively duplicates everything, so changes to nested items do not leak.

What is the difference between an epoch, a batch and an iteration?

Deep Learning and NLPMedium

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An epoch is one pass over the training data; a batch is the subset used for one update; an iteration is one weight update on a batch.

What is transfer learning?

Deep Learning and NLPMedium

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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.

Write SQL to compute users, conversions and conversion rate for each experiment variant.

A/B Testing & ExperimentationEasy

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SELECT a.variant, COUNT(DISTINCT a.user_id) AS users, COUNT(DISTINCT o.user_id) AS converters, ROUND(COUNT(DISTINCT o.user_id) * 1.0 / COUNT(DISTINCT a.user_id), 4) AS conv_rate FROM assignments a LEFT JOIN orders o ON o.user_id = a.user_id AND o.created_at >= a.assigned_at WHERE a.experiment_id = 'checkout_v2' GROUP BY a.variant;

What is the difference between layer normalisation and batch normalisation?

Deep Learning and NLPMedium

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Batch normalisation normalises each feature using the mean and variance across the batch, so it depends on batch size and behaves differently in training and inference. Layer normalisation normalises across the features of each single example, so it works with any batch size and with variable-length sequences. That is why transformers use layer norm, often in the pre-norm position, while CNNs traditionally use batch norm.

L1 vs L2 regularisation?

Machine LearningMedium

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L1 (Lasso) adds absolute weights and can shrink some to zero, giving feature selection. L2 (Ridge) adds squared weights and shrinks them smoothly without removing features.

Your neural network's training loss suddenly becomes NaN. What do you check?

Deep Learning and NLPMedium

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Common causes are a learning rate that is too high, exploding gradients, bad input values, and unstable maths like log of zero. I would check the input data for NaN or infinite values, lower the learning rate, add gradient clipping, and make sure the loss uses the stable built-in version with logits. With mixed precision, I would check the loss scaler. Logging gradient norms per step helps find the exact point it breaks.

What is the difference between a subquery and a correlated subquery?

SQL for Data ScienceMedium

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A normal subquery runs once on its own and its result is used by the outer query, for example WHERE price > (SELECT AVG(price) FROM products). A correlated subquery refers to columns of the outer query, so it is logically evaluated once per outer row, for example comparing each product with its own category average. Correlated subqueries can be slow; joins or window functions are usually faster alternatives.

How do you use groupby with multiple aggregations?

Python for Data ScienceMedium

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

What are hybrid search and reranking in retrieval systems?

Generative AI and LLMsHard

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Hybrid search combines keyword search, like BM25, with vector search. Keywords catch exact matches such as product codes and names, while vectors catch meaning and paraphrases. Results are merged, often with reciprocal rank fusion. Reranking then takes the top 20 to 50 candidates and scores each one against the query with a cross-encoder model, which is slower but more accurate. Together they usually improve RAG answer quality noticeably.

What is an AI agent in the context of LLMs?

Generative AI and LLMsMedium

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An AI agent is an LLM that works in a loop: it plans a step, calls a tool, reads the result and decides the next step until the goal is met. For example, it might query a database, run Python on the result and write a summary. Agents handle multi-step tasks but are less predictable, slower and costlier than a single call. Good designs limit tools and steps, log every action and keep a human check for risky actions.

What is the Augmented Dickey-Fuller test?

Time Series & ForecastingMedium

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The ADF test checks whether a series has a unit root, which means it is non-stationary. The null hypothesis is that a unit root exists. A small p-value, say below 0.05, lets you reject the null and treat the series as stationary. It is often paired with the KPSS test, whose null is the opposite, stationarity. Using both gives a more reliable conclusion before choosing how much to difference.

What is DBSCAN and when is it better than K-means?

Machine LearningMedium

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DBSCAN is a density-based clustering algorithm. It groups points that have at least min_samples neighbours within distance eps, and marks isolated points as noise. Unlike K-means, it does not need the number of clusters, finds clusters of any shape, and handles outliers. It struggles when clusters have very different densities and in high dimensions. It suits location data, like finding delivery hotspots from GPS points.

Your model's most important feature is customer_id. What is wrong?

Feature EngineeringMedium

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An ID has no real meaning, so high importance means the model is memorising individual customers. It often happens when the same customers appear in both train and test, or when IDs were assigned in time order and act as a proxy for date or signup cohort. I would drop the ID, use a group-based split by customer, and if timing matters, add an explicit feature like account age instead.

Your LLM app takes 8 seconds to respond and users complain. What do you do?

Generative AI and LLMsMedium

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Break down where the time goes: retrieval, model time to first token and generation length. Stream tokens so users see output immediately. Cut prompt and output length, run retrieval and other calls in parallel, and cache frequent answers. Use a smaller or faster model for simple requests, and avoid chains of several LLM calls where one would do. Set a latency budget per step and monitor p95, not just averages.

Your SQL result does not match the number on the official dashboard. How do you resolve it?

SQL for Data ScienceMedium

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I would first align definitions: date range, time zone, filters like test users or cancelled orders, and whether the metric counts users or events. Then compare at a smaller grain, by day or by city, to find where the gap starts. I would read the dashboard's underlying query and check data refresh times. Once found, I would document the agreed definition so the same mismatch does not repeat.

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

Machine LearningMedium

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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.

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

Deep Learning and NLPMedium

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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.

Write OpenCV code to read an image, convert it to grayscale, resize it and detect edges.

Computer VisionEasy

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import cv2 img = cv2.imread('part.jpg') # loaded as BGR if img is None: raise FileNotFoundError('part.jpg') gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) small = cv2.resize(gray, (224, 224), interpolation=cv2.INTER_AREA) blur = cv2.GaussianBlur(small, (5, 5), 0) edges = cv2.Canny(blur, 50, 150) cv2.imwrite('edges.png', edges)

What are trend, seasonality and noise in a time series?

Time Series & ForecastingEasy

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Trend is the long-term direction of the series, such as steady growth in monthly users. Seasonality is a pattern that repeats at a fixed period, like higher sales every weekend or every festive season. Noise is the random variation left after removing trend and seasonality. Separating these parts, through decomposition, helps you understand the data and pick a model that can capture each component properly.

Write Pandas code to encode hour of day as a cyclical feature.

Feature EngineeringEasy

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import numpy as np # hour 23 and hour 0 should be close, so use sin and cos df['hour_sin'] = np.sin(2 * np.pi * df['hour'] / 24) df['hour_cos'] = np.cos(2 * np.pi * df['hour'] / 24)

Explain the Law of Large Numbers.

Statistics and ProbabilityMedium

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As the number of independent trials grows, the sample average converges to the true expected value, which is why larger samples give more stable estimates.

How do you turn text columns into features for a tabular model?

Feature EngineeringMedium

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Simple options are length, word count and keyword flags. TF-IDF vectors capture important words and work well with linear models. For meaning, use sentence embeddings from a pretrained model and optionally reduce them with PCA. You can also train a separate text classifier and feed its predicted score as one feature. Fit any vocabulary or encoder on training data only to avoid leakage.

Your deployed model gives different predictions than the same model in your notebook. Why?

MLOps and Model DeploymentMedium

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Usually preprocessing differs between training and serving. Check that the same pipeline object was saved, not just the estimator, and that category order, missing value handling and scaling match. Compare library versions, feature types like strings versus integers, column order, and time zones in date features. Send the same few rows through both paths and compare features step by step to find the first mismatch.

Tell me about a time you had to explain a deep learning model's mistake to a client or stakeholder.

Deep Learning and NLPMedium

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Situation: our document classifier mislabelled several legal notices, and the client was upset. Task: explain clearly and restore confidence. Action: I showed the misclassified examples, explained that they used a format absent from training data, and avoided jargon. I proposed adding those samples, a confidence threshold for human review and weekly error reports. Result: errors on that format fell sharply and the client extended the project.

What are shadow, canary and A/B deployments for models?

MLOps and Model DeploymentHard

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In shadow deployment the new model receives real traffic and logs predictions, but users still get the old model's output; it is safe for checking latency and agreement. A canary sends a small share of traffic, like 5 percent, to the new model and expands if metrics stay healthy. An A/B test splits traffic randomly to measure business impact statistically. Teams often go shadow, then canary, then full rollout.

You target-encoded a city column and validation AUC jumped from 0.72 to 0.95. What do you suspect?

Feature EngineeringMedium

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Target encoding replaces each category with the average target value for that category, for example the churn rate per city. It handles high-cardinality columns compactly. The main risk is leakage: if a row's own target is included in its encoding, the model sees the answer and overfits. Prevent it with out-of-fold encoding, smoothing toward the global mean for rare categories, and fitting only on training data.

Users say your internal chatbot gives outdated policy answers. What is the likely cause and fix?

Generative AI and LLMsMedium

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The knowledge index probably still holds old document versions, or the model is answering from its training data instead of retrieved context. Fix the ingestion pipeline to re-index on document change and remove superseded versions. Store effective dates in metadata and filter or boost the latest. Instruct the model to answer only from provided context and show the source with its date, so users can verify it.

What is interference or a network effect in experiments, and why does it break standard A/B tests?

A/B Testing & ExperimentationHard

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Standard A/B analysis assumes one user's outcome does not depend on another user's assignment, the SUTVA assumption. In social or messaging products, a treated user's new feature affects their untreated friends, so control is contaminated and the effect is biased. Solutions include cluster randomisation, where connected groups of users are assigned together, ego-network designs and comparing results across different cluster sizes to estimate the spillover.

Write NumPy code for non-maximum suppression on a set of boxes and scores.

Computer VisionHard

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import numpy as np def nms(boxes, scores, thr=0.5): x1, y1, x2, y2 = boxes.T areas = (x2 - x1) * (y2 - y1) order = scores.argsort()[::-1] keep = [] while order.size: i = order[0] keep.append(i) w = np.maximum(0, np.minimum(x2[i], x2[order[1:]]) - np.maximum(x1[i], x1[order[1:]])) h = np.maximum(0, np.minimum(y2[i], y2[order[1:]]) - np.maximum(y1[i], y1[order[1:]])) inter = w * h iou = inter / (areas[i] + areas[order[1:]] - inter) order = order[1:][iou < thr] return keep

What are interaction features and when do they help?

Feature EngineeringMedium

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Interaction features combine two or more variables, such as price times quantity, or income divided by number of dependents. They help linear models capture effects where one variable changes the impact of another. Tree models can learn some interactions themselves, but explicit ratios often still help. Too many automatic interactions explode the feature count and overfit, so build them from domain logic and validate each one.

What is positional encoding in transformers and why is it needed?

Deep Learning and NLPHard

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Self-attention treats input tokens as a set, so without extra information it cannot tell word order: dog bites man and man bites dog look the same. Positional encoding adds position information to each token embedding. The original transformer used fixed sine and cosine patterns; BERT learned position embeddings; many modern LLMs use rotary embeddings, which encode relative positions and extend better to longer contexts.