01
How do you handle imbalanced classes?
Show answer
Use stratified splits, class weights, resampling such as SMOTE, threshold tuning and judge models with precision, recall, F1 or PR-AUC instead of accuracy.
Course5 Intelligence Data Science Interview Questions & Answers
Curated interview practice questions with answers. These are practice scenarios, not a verified list of past interview questions.
About Course5 Intelligence hiring for Data Science roles
Course5 Intelligence is a Mumbai-based analytics and AI company that offers market research, digital analytics, customer analytics and AI-driven insights to global brands. It works with clients in technology, retail, consumer goods, media and pharma. In India it hires data analysts, research analysts, data scientists and engineers who turn survey, digital and business data into insights.
For Data Science roles, this Course5 Intelligence practice set focuses most on Machine Learning (9), Feature Engineering (4), Deep Learning and NLP (4), SQL for Data Science (4). It has 4 easy, 32 medium, 4 hard questions, so you can start easy and work up to the harder ones.
- Company type
- Analytics
- Headquarters
- Mumbai, India
- Main India offices
- Mumbai, Bengaluru
Course5 Intelligence Data Science interview process
- Online assessment (aptitude + analytics basics)
- Technical interview
- Case or managerial round
- HR round
Typical pattern based on public candidate experiences. Rounds vary by role, team and year.
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02
You target-encoded a city column and validation AUC jumped from 0.72 to 0.95. What do you suspect?
Show answer
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.
03
How does linear regression work and what are its assumptions?
Show answer
It fits a line minimising squared error. Assumes linearity, independent errors, constant variance (homoscedasticity), normally distributed residuals and no strong multicollinearity.
04
How does a CNN work?
Show answer
Convolution filters slide over the input to detect local patterns, pooling reduces size, and deeper layers combine edges into shapes and objects. Used mainly for images.
05
What does CI/CD mean for machine learning projects?
Show answer
Continuous integration runs automatic checks on every code change: unit tests for feature code, data validation, linting and a quick training run on sample data. Continuous delivery packages the model and service, for example as a Docker image, and deploys it through staging to production after evaluation gates, such as the new model beating the current one on a holdout set. Some teams add continuous training, which retrains on new data automatically.
06
Write Python code to compute the haversine distance between customer and store locations.
Show answer
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)
07
Write SQL to find customers who ordered in January 2026 but not in February 2026.
Show answer
SELECT DISTINCT o.customer_id
FROM orders o
WHERE o.order_date >= '2026-01-01' AND o.order_date < '2026-02-01'
AND NOT EXISTS (
SELECT 1 FROM orders f
WHERE f.customer_id = o.customer_id
AND f.order_date >= '2026-02-01' AND f.order_date < '2026-03-01'
);
08
Tell me about a time you had to learn a new deep learning framework or tool quickly.
Show answer
Situation: my internship project used PyTorch, but I had only used Keras. Task: build a text classifier within two weeks. Action: I followed the official tutorials for two days, rebuilt a small Keras model in PyTorch to compare, and asked a senior to review my training loop. Result: I delivered the model on time with 88 percent F1. I learned that rebuilding something familiar is the fastest way into a new tool.
09
Write a basic PyTorch training loop for one epoch.
Show answer
model.train()
for X, y in train_loader:
X, y = X.to(device), y.to(device)
optimizer.zero_grad()
logits = model(X)
loss = loss_fn(logits, y)
loss.backward()
optimizer.step()
# evaluation
model.eval()
with torch.no_grad():
val_loss = sum(loss_fn(model(Xv.to(device)), yv.to(device)).item() for Xv, yv in val_loader)
10
Your logging system was down for four days, leaving gaps in a daily series. How do you handle them before modelling?
Show answer
First make the gaps explicit by reindexing to a full daily calendar so missing days appear as NaN instead of silently disappearing. For short gaps, linear or time-based interpolation is usually fine. If there is strong weekly seasonality, fill using the same weekday from neighbouring weeks or a seasonal decomposition. Add a flag column marking imputed days, and never treat missing as zero unless zero really happened.
11
How do you pull large data from a SQL database into Pandas efficiently?
Show answer
Push as much work as possible into SQL: filter, aggregate and select only needed columns before pulling. Use pd.read_sql with a SQLAlchemy engine and chunksize to stream large results. For very large extracts, export to Parquet from the warehouse and read that, which is much faster than row-by-row transfer. Set dtypes like category early, and never use SELECT star on a big table from a notebook.
12
The business asks you to pick a decision threshold for a churn model. How do you choose it?
Show answer
The default 0.5 is rarely right. I would get the cost of each outcome: the value of saving a churner, the cost of a retention offer, and the team's capacity to contact customers. Then I compute expected profit or precision and recall at many thresholds on validation data and pick the one that maximises value within capacity. I would also check that the chosen threshold stays stable over recent months.
13
Your LLM app takes 8 seconds to respond and users complain. What do you do?
Show answer
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.
14
What are Python's main built-in data types?
Show answer
int, float, complex, bool, str, list, tuple, set, dict and NoneType. Mutable ones are list, set and dict; the others are immutable.
15
Write SQL to compute a 7-day rolling average of daily active users.
Show answer
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;
16
What is the difference between hard voting and soft voting in ensembles?
Show answer
In hard voting each model predicts a class and the majority wins. In soft voting each model gives class probabilities, they are averaged, and the class with the highest average wins. Soft voting usually works better because it uses each model's confidence, but it needs calibrated probabilities from all models. Hard voting is simpler and works even with models that only output labels.
17
What is a CTE and what is a recursive CTE?
Show answer
A CTE, common table expression, is a named temporary result defined with WITH and used in the query that follows. It breaks complex SQL into readable steps and can be referenced more than once. A recursive CTE refers to itself: an anchor query starts the result and a recursive part keeps adding rows until none are added. It is used for hierarchies like employee-manager chains or generating date series.
18
What is a contrastive image-text model like CLIP, and how does it enable zero-shot classification?
Show answer
CLIP trains an image encoder and a text encoder together on hundreds of millions of image-caption pairs, pulling matching pairs close in a shared embedding space and pushing mismatches apart. For zero-shot classification you embed prompts like 'a photo of a damaged parcel' for each class and pick the class whose text embedding is most similar to the image embedding. It works without task-specific training but may need fine-tuning for niche domains.
19
Most of your products sell zero units on most days, with occasional sales. Which forecasting approach suits this?
Show answer
This is intermittent demand, and standard models like ARIMA forecast a small constant that is useless for stocking. Croston's method and its SBA variant forecast the size of non-zero demand and the interval between sales separately. Alternatives are aggregating to weekly level, or using a global gradient boosting model with Tweedie or Poisson loss. Evaluate with MAE or MASE, not MAPE, because zero actuals break percentage errors.
20
Your daily sales forecast badly misses the Diwali spike every year. How do you fix it?
Show answer
The model is treating Diwali as noise because the festival moves on the calendar and a fixed yearly seasonality does not align. Add explicit holiday features: a flag for Diwali and a few days before and after, since buying starts early. Include other regional festivals and sale events too. Models like Prophet or gradient boosting with holiday features handle this well. Then backtest specifically on past festive periods to confirm the fix.
21
Your random forest is accurate but too slow at prediction time for a real-time API. What do you do?
Show answer
First measure where the time goes. Then reduce trees and maximum depth and check the accuracy loss. Switch to a gradient boosting model, which often needs fewer, smaller trees. Compile the model with tools like ONNX Runtime or Treelite. Cache features or precompute scores for known users. If accuracy allows, a simpler logistic regression may meet the latency budget easily. Always compare options on the same validation set.
22
Your power analysis says you need 200,000 users per group, but the page gets only 5,000 users a week. What are your options?
Show answer
Waiting 80 weeks is unrealistic. Options: test a bolder change so the expected effect is larger; use a more sensitive metric closer to the change, like add-to-cart instead of purchase; apply CUPED to reduce variance; or move the test to a higher-traffic page. If none work, accept a larger MDE explicitly, or decide using qualitative research and a staged rollout with monitoring instead of a formal test.
23
What is a baseline model and why should you build one first?
Show answer
A baseline is the simplest reasonable prediction, such as predicting the most common class, last week's value or a basic logistic regression. It sets the minimum score any real model must beat. Without it, a 75 percent accuracy can look good when predicting the majority class already gives 80 percent. Baselines also catch pipeline bugs early and show whether extra complexity is worth it.
24
What is the randomisation unit in an experiment, and why does it matter?
Show answer
The randomisation unit is what you assign to variants: a user, a session, a device, a page view or even a city. Choosing user-level keeps each person's experience consistent and avoids contamination. The analysis unit should match the randomisation unit, because if you randomise by user but analyse by page view, observations are correlated and standard errors come out too small, producing false positives. Use the delta method or bootstrap when they differ.
25
You must classify support messages into 40 categories, but some categories have only 20 examples. What do you do?
Show answer
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.
26
How would you optimise a slow Pandas pipeline on a 5 GB file?
Show answer
Read in chunks or use usecols and dtypes, convert strings to category, avoid apply with row loops in favour of vectorised ops, and consider Polars, Dask or Parquet storage.
27
Pandas: merge vs join vs concat?
Show answer
merge joins on columns, SQL-style (how='inner', 'left', ...). join joins on the index. concat stacks DataFrames along rows or columns.
28
What is the receptive field of a neuron in a CNN?
Show answer
The receptive field is the region of the input image that can influence one activation. Early layers see small patches like edges, and deeper layers see larger regions, enabling recognition of whole objects. It grows with kernel size, depth, stride and pooling, and dilated convolutions increase it without extra parameters. If the receptive field is smaller than the objects you care about, the network cannot capture their full context.
29
Write Python code to compute the Population Stability Index for a feature to detect drift.
Show answer
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
30
What are quantile and power transforms such as Box-Cox and Yeo-Johnson, and when do you use them?
Show answer
Power transforms reshape a skewed feature to look more normal. Box-Cox needs strictly positive values, while Yeo-Johnson also handles zero and negative values; both learn a lambda parameter from training data. A quantile transform maps values to a uniform or normal distribution by rank, which is robust to outliers. They help linear models, distance-based methods and neural networks, but tree models rarely need them.
31
What are interaction features and when do they help?
Show answer
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.
32
How does the Gini impurity differ from entropy?
Show answer
Both measure node impurity; Gini is cheaper to compute and entropy is slightly more sensitive to class balance, with similar trees in practice.
33
L1 vs L2 regularisation?
Show answer
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.
34
What is experiment tracking and why use a tool like MLflow?
Show answer
Experiment tracking records each training run's parameters, code version, data version, metrics and output artefacts. Tools like MLflow or Weights and Biases store this in one place, so you can compare runs, reproduce the best model and see why something improved. Without it, results live in notebook outputs and memory. MLflow also offers a model registry to move approved models through staging and production.
35
What is statistical power and how can you increase it?
Show answer
Power is the probability of detecting a real effect. Increase it with a larger sample, a bigger effect size, lower noise or a higher significance level.
36
How would you explain a statistical concept to a non-technical manager?
Show answer
I start with the decision they care about, not the formula. Once a sales manager asked why a 3 percent lift was not significant. I explained that with only 200 customers, the result could easily happen by chance, like getting 6 heads in 10 coin tosses. I showed a simple range chart of the confidence interval and suggested running the test longer. He agreed, and the extended test gave a clear answer.
37
Your manager asks whether collecting more training data will improve the model. How do you answer using a learning curve?
Show answer
A learning curve plots training and validation scores against the size of the training set. If both scores are low and close, the model has high bias and needs more features or a more flexible model. If training is high but validation is much lower and the gap narrows as data grows, the model has high variance and more data will help. It tells you whether collecting more data is worth the cost.
38
You have 50,000 unlabelled images and budget to label only 3,000. How do you use the budget well?
Show answer
Do not pick 3,000 at random. Label a small diverse seed set, using embedding clustering to cover variety, and train a first model. Then use active learning: label the images the model is least confident about or that disagree across models. Use semi-supervised methods like pseudo-labelling high-confidence images, or self-supervised pretraining on all 50,000. Keep a randomly sampled test set so evaluation stays unbiased.
39
Your LLM gives different summaries for the same document on different runs, but auditors need consistent output. What do you do?
Show answer
Set temperature to zero or near zero and fix other sampling settings, and use a seed where the API supports it, though outputs may still vary slightly. Pin the exact model version. Use a structured output format with fixed fields, and reduce ambiguity in the prompt. Cache and store each generated output with its inputs and version, so the stored result, not a regeneration, is what auditors see.
40
How do you pivot data in Pandas?
Show answer
pd.pivot_table(df, index='region', columns='month', values='sales', aggfunc='sum') reshapes long data into a summary matrix; melt() does the reverse.
Tips to crack the Course5 Intelligence Data Science interview
- Revise Excel, SQL and a visualisation tool such as Power BI or Tableau, which are widely used in analytics roles.
- Learn basic market research concepts like surveys, sampling and segmentation if you apply for research and insights positions.
- Practise presenting a short insight story from a dataset, explaining what the numbers mean for a business decision.
- Keep a one-minute story ready for every project on your CV: problem, your part, tools, result.
- Practise the hard questions on this page aloud; Course5 Intelligence interviewers usually ask follow-ups on your first answer.
Frequently asked questions
How many rounds are there in the Course5 Intelligence Data Science interview?
Candidates usually go through 4 stages: Online assessment (aptitude + analytics basics); Technical interview; Case or managerial round; HR round. The exact rounds change with the role, team, location and your experience, so confirm the process with the recruiter.
Which topics should I prepare for the Course5 Intelligence Data Science interview?
This practice set leans on Machine Learning (9), Feature Engineering (4), Deep Learning and NLP (4), SQL for Data Science (4). Revise those first, then practise explaining one or two of your own projects end to end, because most Course5 Intelligence interviewers ask you to walk through real work.
Are these the exact questions asked at Course5 Intelligence?
No. These are curated practice questions that match the skills and difficulty usually tested in analytics interviews like Course5 Intelligence's. They are not a leaked or verified list of past questions.
Where does Course5 Intelligence hire Data Science professionals in India?
Course5 Intelligence has large teams in Mumbai, Bengaluru. Openings and locations change often, so check the Course5 Intelligence careers page and filter by city and experience.
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