Top 50+ Data Science Interview Questions and Answers

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This guide offers a list of 50+ most asked data science interview questions and answers for freshers and experienced professionals. You will also find an interview cheat sheet and helpful tips to prepare for data science interviews.

But before we move on to the frequently asked data science interview questions, let’s cover the basics.

Category	Details
Total Duration	2–4 weeks (from application to final offer)
Number of Rounds	3 to 6 rounds (varies by company)
Types of Rounds	– Resume Screening – Online Test (Coding/MCQs) – Technical Interview(s) – Case Study or Project Discussion – HR/Behavioral Interview
Question Types	– Python & SQL Coding – Statistics & Probability – Machine Learning – Business Scenarios – Communication Skills
Difficulty Level	Medium to High (varies by role and company)
Each Round Duration	30–90 minutes
Top Hiring Companies	Google, Amazon, Flipkart, TCS, Microsoft, Accenture, Capgemini
Most Common Tools Asked	Python, Pandas, NumPy, SQL, Scikit-learn, Excel, Tableau, Jupyter
Preparation Time Needed	2–3 weeks of focused preparation recommended

So, what is data science?

Data = Information

Science = Way of understanding things

Data Science = A smart way to understand information and find answers using it.

Data Science definition 

Data science is the field of using data, statistics, and programming to solve real-world problems. It helps businesses make smarter decisions and predict future outcomes. 

History and origin

The roots of data science go back to the 1960s, but the term gained real popularity in the early 2000s. William S. Cleveland is one of the key figures who helped shape it into a modern discipline.

Are data science jobs in high demand?

Today, data science is one of the most in-demand fields in the world. According to the Bureau of Labor Statistics, data science jobs are expected to grow by 36% between 2023 and 2033.

An estimated 20,800 data scientist job openings are projected each year on average. 

From healthcare to finance, every industry needs skilled data professionals. Common job titles include –

• Data analyst
• Data scientist
• Machine learning engineer
• AI specialist

If you are applying for any of these roles, here are some of the most commonly asked data science interview questions to help you prepare.

Note:

We have categorized the questions into six key sections – most asked, for freshers, for experienced professionals, technical (advanced), coding, and questions asked by top IT companies. This structure will help you focus on the areas that matter most for your interview preparation.

Also Read - Top 35+ Data Analyst Interview Questions and Answers

Most Asked Data Science Interview Questions

Here are some of the top data scientist interview questions and answers. These are the 10 most asked questions. Go through them to understand what interviewers expect.

1. What is the distinction between supervised and unsupervised learning?

In supervised learning, models train on labeled data. You know the correct output.

In unsupervised learning, the data has no labels. The model finds patterns on its own.

Type	Input Data	Goal	Examples
Supervised	Labeled	Prediction	Regression, Classification
Unsupervised	Unlabeled	Pattern Detection	Clustering, PCA

2. Describe how you would build a decision tree from scratch.

Start by selecting the best feature using Gini index or information gain.
Split data based on feature values. Repeat the process on child nodes.
Stop when nodes are pure or other stopping criteria are met.

3. Explain bias versus variance and how you would balance them.

Bias is error from incorrect assumptions. Variance is error from sensitivity to data. Too much bias causes underfitting. High variance causes overfitting. 

To balance them, I choose simpler models first, then tune complexity using cross-validation.

4. How do you treat data with over 30% missing values?

If the dataset is large, I remove the affected rows. If small, I use imputation – mean, mode, or even predictive models.

The method depends on the nature of the data and business need.

5. What are your methods for preventing overfitting?

I use cross-validation, early stopping, and regularization (like L1 or L2).
Simpler models also help. If needed, I increase data size through augmentation.

6. Walk me through the steps of deploying and maintaining a model.

First, I prepare a pipeline for training and prediction. After deployment, I monitor accuracy and retrain as needed. I also track concept drift and update data pipelines regularly.

7. How would you design an A/B test and interpret its outcome?

I divide users randomly into two groups. One sees the current version (A), the other sees the new version (B).

I track metrics like conversion rate and run a statistical test (e.g., t-test) to compare them.

If the p-value is < 0.05, I consider the difference significant.

8. Explain the ROC curve and AUC metric.

ROC curve plots True Positive Rate vs False Positive Rate.

AUC is the area under this curve.

AUC near 1.0 shows great performance. AUC near 0.5 means the model is guessing.

9. How do you evaluate a clustering model without ground truth?

I use internal metrics like Silhouette score, Davies-Bouldin index, or inertia. These don’t require true labels and still tell how well clusters are formed.

10. When and why would you use dimensionality reduction?

I use it when there are too many features. Too many dimensions can confuse the model or slow it down. PCA or t-SNE helps simplify the data while keeping useful information.

Data Science Interview Questions for Freshers

If you are a fresher or a recent graduate, these interview questions for a data scientist will help you get started.

11. What is Data Science?

Data science is the field of using data to solve problems. It combines statistics, programming, and domain knowledge to extract insights, make predictions, and support decisions.

12. What is the difference between data analytics and data science?

Data analytics focuses on examining past data to find patterns and trends. Data science goes further – it also builds predictive models and uses machine learning to forecast future outcomes or automate decisions.

13. What is SQL and why is it used in data science?

SQL stands for Structured Query Language. It is used to read and work with data in relational databases.

In data science, SQL helps extract, filter, and join data before analysis.
Without clean data, no model works well. So SQL is essential.

14. Name three key statistics: define and contrast them.

Term	Definition	Purpose
Mean	Average of numbers	Measures central tendency
Median	Middle value in sorted list	Handles skewed data better
Mode	Most frequent value	Useful for categories

Mean is sensitive to outliers. Median is more stable. Mode works for categorical features.

15. What is the role of a primary key?

A primary key uniquely identifies each record in a table.
It cannot be null or duplicate. It helps avoid data duplication.

16. How do you use GROUP BY versus WHERE?

WHERE filters rows before grouping. GROUP BY groups rows to apply functions like SUM() or COUNT().

Example:

SELECT city, COUNT(*) FROM customers  

WHERE status = ‘active’  

GROUP BY city;

17. What is logistic regression?

It is a model used to predict binary outcomes. It outputs probabilities using the sigmoid function.

For example, will a user click or not?

The image below shows the sigmoid curve, which maps input values to probabilities between 0 and 1. The curve has an S-shape and is steepest around zero – this is where small changes in input can flip predictions.

18. Describe K-means clustering steps.

• Choose K cluster centers randomly
• Assign data points to nearest cluster
• Update centers based on current members
• Repeat until centers stop moving

19. What is the difference between mean and median?

Mean adds all values and divides by count. Median is the middle value when sorted. In skewed data, median is often more reliable.

Note – Interview questions for data science fresher roles often include basic concepts, Python, statistics, and real-life problem-solving scenarios.

Also Read - Top 25+ SQL DBA Interview Questions and Answers

Data Science Interview Questions for Experienced Professionals

These interview questions in data science are often asked during technical rounds for experienced roles.

20. How have you handled a large messy dataset in past projects?

I once worked with millions of user logs from an e-commerce platform. The data had nulls, mixed formats, and duplicate rows. I wrote preprocessing scripts in Python using Pandas and Dask for faster performance. I validated entries using regex and business rules. Outliers were flagged separately for review.

21. Explain a time you communicated technical findings to a business audience.

In one project, we found that customer churn was strongly linked to delivery delays. Instead of showing model weights, I used visuals like bar charts and simple bullet points. I compared high-risk vs. low-risk customer behavior. This helped the operations team take action fast.

22. Describe a model that went off-track – what did you do?

A fraud detection model showed a sudden drop in precision.
On inspection, I noticed a change in transaction patterns due to a festival campaign.

I retrained the model with recent data and added time-based features. It recovered within two weeks of deployment.

23. How do you incorporate stakeholder feedback into model design?

During development, I run check-ins with product and business teams.
They share what decisions depend on the model. 

For example, one team wanted the model to flag risk even at lower probability. So, I adjusted the decision threshold and retrained it on more negative samples.

24. Share an experience where you improved a model’s performance.

In a marketing campaign model, recall was low. I added features from email interaction history and used SMOTE to balance the dataset. I also tuned hyperparameters using GridSearchCV. The model’s F1 score went up by 18%.

25. What’s the most important metric you tracked in production, and why?

Metric	Why It Matters
F1 Score	Balances precision and recall
Latency	Affects real-time prediction speed
Drift Score	Detects change in incoming data

For most cases, I track F1 score and drift score together. They help keep performance stable over time.

Data Science Technical Interview Questions (Advanced)

Here are advanced data scientist interview questions that test your technical expertise and real-world problem-solving skills.

26. What are the assumptions behind linear regression?

Linear regression is based on five key assumptions:

Assumption	Description
Linearity	Relationship between input and output is linear
Independence	Errors are not related across observations
Homoscedasticity	Constant variance in residuals
Normality	Residuals are normally distributed
No multicollinearity	Features are not strongly correlated

Violating these affects the model’s reliability.

27. Explain how backpropagation works in a neural network.

Backpropagation updates weights in a neural network by calculating gradients of the loss function. It uses the chain rule to move errors backward from the output layer. Each layer’s weights are adjusted to reduce the final error.

28. Describe the difference between bagging and boosting.

Both are ensemble methods, but they work differently.

Feature	Bagging	Boosting
Training	Parallel	Sequential
Goal	Reduce variance	Reduce bias
Example	Random Forest	XGBoost, AdaBoost

Bagging trains models independently. Boosting learns from previous mistakes.

29. How do you detect heteroscedasticity in regression?

I plot residuals vs predicted values. If the spread increases or forms a pattern, variance is not constant. Breusch-Pagan test can also be used to confirm this.

30. Walk through how to evaluate ARIMA model components.

ARIMA has three parts: AR (p), I (d), MA (q).

I use ACF and PACF plots to choose p and q.

d refers to the number of times the series must be differenced to become stationary.

Data Science Coding Interview Questions

Now let’s look at some coding-focused interview questions for a data scientist, including Python, SQL, and algorithms.

31. Write a Python function to compute a confidence interval.

import scipy.stats as stats

import numpy as np

def confidence_interval(data, confidence=0.95):

    n = len(data)

    mean = np.mean(data)

    std_err = stats.sem(data)

    margin = std_err * stats.t.ppf((1 + confidence) / 2, n – 1)

    return (mean – margin, mean + margin)

Use this when you have a sample and want to estimate the true mean.

32. Using SQL, how would you find the median of a numeric column?

SELECT AVG(salary) AS median_salary

FROM (

  SELECT salary,

         ROW_NUMBER() OVER (ORDER BY salary) AS rn,

         COUNT(*) OVER () AS total

  FROM employees

) sub

WHERE rn IN (FLOOR((total + 1) / 2), CEIL((total + 1) / 2));

Works in most SQL dialects with window functions.

33. Given Python lists A and B, write code to find items present in both.

A = [1, 2, 3, 4]

B = [3, 4, 5, 6]

common = list(set(A) & set(B))

print(common)  # Output: [3, 4]

Simple and fast using set intersection.

34. Write a function to sum odd-indexed elements in a list.

def sum_odd_indexed(lst):

    return sum(lst[i] for i in range(1, len(lst), 2))

# Example

print(sum_odd_indexed([10, 20, 30, 40]))  # Output: 60 (20 + 40)

Data Science MCQs

35. What does the sigmoid function output in logistic regression?

A. Any real number
B. Only 0 or 1
C. A value between 0 and 1
D. A binary class label

Answer: C. A value between 0 and 1

36. Which SQL clause is used to group records with the same values?

A. WHERE
B. ORDER BY
C. GROUP BY
D. HAVING

Answer: C. GROUP BY

37. Which metric is best when classes are imbalanced?

A. Accuracy
B. Recall
C. F1 Score
D. Mean Squared Error

Answer: C. F1 Score

38. In Python, which library is used for handling labeled datasets?

A. NumPy
B. Pandas
C. Matplotlib
D. TensorFlow

Answer: B. Pandas

39. What does p-value < 0.05 generally indicate in hypothesis testing?

A. Weak correlation
B. Strong probability
C. Statistically significant result
D. Model overfitting

Answer: C. Statistically significant result

40. What is the role of a primary key in SQL?

A. Creates foreign tables
B. Sorts the data
C. Identifies unique records
D. Deletes null values

Answer: C. Identifies unique records

Other Important Data Science Interview Questions

This section covers additional interview questions in data science that often appear across various company rounds.

Statistics Interview Questions for Data Scientists

Here are key data scientist questions based on statistics that are commonly asked during interviews.

1. Define Type I and Type II errors.
2. How is a p-value different from confidence interval?
3. What are z-test, t-test, and F-test used for?
4. What is a chi-squared distribution applied to?
5. How do you detect and correct skewness in data?

Data Scientist Python Interview Questions

1. How would you calculate Euclidean distance between two vectors? 
2. Show code to draw N samples from a normal distribution and plot histogram.
3. Write a function to compute rolling averages on a list of numbers.
4. How do you manipulate missing values using pandas?
5. Demonstrate one-hot encoding for categorical data in Python.

Data Science and Machine Learning Interview Questions

1. Compare decision trees and random forests.
2. What is the kernel trick in SVMs and when do you use it?
3. Explain gradient descent vs. stochastic gradient descent.
4. What is the difference between discriminative and generative models?
5. Describe transfer learning and its main benefit.

Also Read - Top 75+ Python Interview Questions and Answers

Data Science Interview Questions Asked by Top IT Companies

These are some of the most commonly asked data science questions in interviews at top tech companies.

Google Data Scientist Interview Questions

1. How would you predict user engagement for a new feature?
2. What is the difference between boosting and bagging? 
3. Explain the mechanics of a neural network and activation functions.
4. Describe a time you handled ambiguity in data with limited visibility.

Microsoft Data Scientist Interview Questions

Here’s what to expect in a typical Microsoft data science interview, including key topics and question patterns.

1. What assumptions underlie linear regression? 
2. How do you choose the number of clusters in K-means?
3. Write code for evaluating time-series stationarity.
4. Explain handling unbalanced datasets with ensemble models.

Apple Data Science Interview Questions

Here are some common topics and question types asked in an Apple data science interview.

1. How would you build a recommendation engine for the App Store?
2. What metrics would you track post-release for model health monitoring?
3. Write a SQL query to find users with anomalous usage patterns.
4. Describe a time you simplified a complex model for stakeholders.

Amazon Data Scientist Interview Questions

1. Design and evaluate an A/B test for Prime display strategies.
2. Explain how you’d forecast demand using historical sales data.
3. What’s your approach to feature selection at scale?
4. How do you detect concept drift post-deployment?

Accenture Data Science Interview Questions

Here are commonly asked questions from a typical Accenture data scientist interview to help you prepare.

1. Explain a data pipeline you’ve built end-to-end.
2. Describe a time when you optimized a machine learning model.
3. Write SQL to detect duplicate transactions.
4. How do you validate model results with business data?

Capgemini Data Scientist Interview Questions

1. How do you cleanse and normalize data in ETL processes?
2. Describe how you’d approach missing data imputation.
3. Write code to convert categorical features into numeric.
4. How do you track and document data lineage?

IBM Data Science Interview Questions

1. How would you deploy a model using IBM Cloud/Azure?
2. Explain the process of hyperparameter tuning.
3. Write code to evaluate a classifier’s F1 score.
4. Tell me about a time you refactored code for better performance.

TCS Data Scientist Interview Questions

These are commonly asked questions in a typical TCS data scientist interview, based on recent candidate experiences.

1. How do you integrate RDBMS and NoSQL data sources?
2. Explain designing a star schema in data warehousing.
3. Write a script to detect and flag outliers in Python.
4. Describe handling high-dimensional financial data.

Wipro Data Scientist Interview Questions

1. How would you forecast quarterly business KPIs?
2. Explain anomaly detection in streaming sensor data.
3. Write SQL to compute month-over-month growth.
4. How do you monitor model drift in production?

Cognizant Data Scientist Interview Questions

1. How do you profile and understand new datasets?
2. What steps do you take to scale a model for large data?
3. Write Python code to merge and aggregate datasets.
4. Tell me about a data solution you implemented in production.

JP Morgan Data Scientist Interview Questions

1. How do you model financial time-series volatility?
2. Explain risk prediction using logistic regression.
3. Write code to backtest a trading model.
4. How do you handle multivariate dependencies in finance?

NVIDIA Data Scientist Interview Questions

1. How would you build a computer vision model for defect detection?
2. Explain feature engineering for sensor data from GPUs.
3. Write code to evaluate model latency and throughput.
4. How do you manage model deployment on edge devices?

Citibank Data Scientist Interview Questions

1. How would you detect fraudulent transaction patterns?
2. Explain credit scoring model development process.
3. Write SQL to identify high-risk customer segments.
4. Describe improving existing risk models with new data.

Intuit Data Scientist Interview Questions

1. How would you forecast user activity over tax seasons?
2. Explain handling highly seasonal revenue data.
3. Write code to bucket customers by usage patterns.
4. How would you evaluate model fairness and bias?

L&T Data Scientist Interview Questions

1. How would you model predictive maintenance for machinery?
2. Explain time-series forecasting for equipment failure.
3. How do you integrate sensor and enterprise data?
4. Describe optimizing sensor-based anomaly alerts.

Flipkart Data Science Interview Questions

1. How would you recommend products to first-time users?
2. Explain metrics to evaluate recommendation performance.
3. Write code to label sessions as likely conversion vs bounce.
4. Describe a model you scaled for high-traffic shopping events.

Walmart Data Scientist Interview Questions

1. How would you forecast demand across multiple stores?
2. Explain inventory optimization using clustering.
3. Write SQL to find underperforming SKUs by region.
4. How do you detect sales anomalies in real-time?

Data Science Interview Questions Cheat Sheet 

If you are short on prep time, this quick data scientist interview cheat sheet covers the must-know topics in under 2 minutes.

Topic	Quick Recall Point
Supervised vs Unsupervised	Labeled vs unlabeled data. Used in prediction vs grouping tasks.
Overfitting	Model fits training data too closely. Use cross-validation, pruning, or regularization.
Bias vs Variance	Bias = error from assumptions. Variance = error from sensitivity to data. Balance both.
ROC & AUC	ROC plots TPR vs FPR. AUC shows model’s ability to classify — closer to 1 is better.
Logistic Regression	For binary output. Uses sigmoid function to return probabilities.
SQL GROUP BY vs WHERE	WHERE filters rows; GROUP BY aggregates them.
p, d, q in ARIMA	p = past values, d = differencing, q = error terms.
Feature Scaling	Use MinMaxScaler or StandardScaler before distance-based models.
Confusion Matrix	Shows TP, FP, TN, FN. Use it to derive precision and recall.
Clustering Evaluation	Use Silhouette Score. Closer to 1 means better-defined clusters.
Cross-Validation	Splits data into k folds to test model robustness.
Gradient Descent	Optimizer that updates weights to reduce loss.
Mean vs Median	Mean is sensitive to outliers. Median is not.
Primary Key	Uniquely identifies rows in a table.
Feature Selection	Pick top features using correlation, mutual info, or tree importance.
A/B Testing	Randomly split users. Compare two versions using metrics like conversion rate.
Dimensionality Reduction	Use PCA or t-SNE to reduce features without losing too much info.
Python Libraries	Pandas, NumPy, Scikit-learn, Matplotlib, Seaborn.
Statistics Must-Know	Mean, Median, Mode, Std Dev, P-value, Confidence Interval.
NLP Basics	Tokenization, stemming, TF-IDF, stopwords removal, word embeddings.

Pro Tips for Data Scientist Interview Preparation

Here are some practical tips to help with your data scientist interview preparation – beyond just revising theory and practicing code.

• Skim recent company blog posts to understand their data culture
• Practice explaining ML concepts like you’re talking to a non-tech friend
• Prepare 2–3 failure stories and what you learned
• Review your GitHub or portfolio projects – they may ask about them
• Rehearse writing clean code on a whiteboard or notepad

Wrapping Up

These 50+ data science interview questions cover everything from basics to advanced topics. Review them well, practice your answers, and follow the tips we have shared. 

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Also Read - How to Become a Data Scientist in 2025?

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