Data Science continues to be one of the most in-demand and evolving career fields in 2025. With industries relying more and more on data-driven decisions, the demand for skilled Data Scientists and Analysts is only rising.
But with that comes tough competition. To stand out and crack interviews, you must go beyond basics and prepare smartly for what recruiters really ask.
Here’s a handpicked list of the Top 10 Data Science Interview Questions (based on 2025 hiring trends) — along with tips and sample answers to help you shine.
1. What is the difference between supervised and unsupervised learning?
Why it’s asked: To test your basic ML understanding.
Answer:
- Supervised learning uses labeled data — you train the model on input-output pairs (e.g., predicting house prices).
- Unsupervised learning uses unlabeled data — the goal is to find hidden patterns (e.g., customer segmentation using clustering).
Tip: Always mention a real-life use case when answering.
2. How do you handle missing data in a dataset?
Why it’s asked: It checks your data preprocessing and decision-making skills.
Answer:
- Remove rows/columns (if too much missing)
- Impute using mean/median/mode (for numerical data)
- Use techniques like KNN imputation or interpolation
- For categorical data, use most frequent or “Unknown” category
Pro Tip: Say “I decide based on data size, importance of the variable, and domain knowledge.”
3. What is the difference between variance and bias?
Why it’s asked: To see if you understand model performance and tuning.
Answer:
- Bias: Error due to wrong assumptions in the model (underfitting)
- Variance: Error due to sensitivity to small fluctuations (overfitting)
Tip: Also mention the Bias-Variance Tradeoff and how regularization (like L2) helps balance it.
4. How do you evaluate the performance of a classification model?
Why it’s asked: This is a must-know for any ML project discussion.
Answer:
- Accuracy (when classes are balanced)
- Precision, Recall, F1 Score (for imbalance)
- ROC-AUC Score
- Confusion Matrix
- Cross-validation scores
“I don’t rely only on accuracy — I always analyze precision-recall tradeoff based on business needs.”
5. Explain overfitting and how to prevent it.
Why it’s asked: Common challenge in real-world modeling.
Answer:
Overfitting = Model performs well on training data but poorly on new data.
Prevention techniques:
- Regularization (L1, L2)
- Cross-validation
- Pruning (in decision trees)
- Reducing model complexity
- Early stopping (in neural networks)
6. What is p-value in statistics?
Why it’s asked: Core concept in hypothesis testing, often asked in analytics roles.
Answer:
P-value tells the probability that the observed results occurred by chance.
If p-value < 0.05, we reject the null hypothesis.
Tip: Don’t just define — explain how you’d use p-values in A/B testing or feature significance.
7. What is the difference between inner join and left join in SQL?
Why it’s asked: SQL is a must for any Data Science job.
Answer:
- Inner Join: Returns only matching rows between two tables
- Left Join: Returns all rows from the left table, and matched rows from the right (if any)
Tip: Practice JOINs with real datasets (like sales and customer tables) before interviews.
8. What steps do you follow in a typical Data Science project?
Why it’s asked: To understand your project workflow and thinking process.
Answer:
- Problem understanding
- Data collection
- Data cleaning and EDA
- Feature selection/engineering
- Model building and tuning
- Evaluation
- Deployment (if applicable)
- Interpretation and reporting
“At ONLEI Technologies, we worked on a real time project where we applied this full pipeline for a real-world dataset.”
9. What’s the difference between bagging and boosting?
Why it’s asked: To test your understanding of ensemble methods.
Answer:
- Bagging (Bootstrap Aggregating): Trains models in parallel, reduces variance (e.g., Random Forest)
- Boosting: Trains models sequentially, focuses on errors, reduces bias (e.g., XGBoost, AdaBoost)
“I used XGBoost in one of my ONLEI Technologies projects to improve prediction accuracy for a customer churn dataset.”
10. Tell us about a Data Science project you’ve worked on.
Why it’s asked: To check your application of skills and end-to-end understanding.
Answer (Structure):
- Brief problem statement
- Tools used (Python, Pandas, etc.)
- What you did (EDA, modeling, results)
- What you learned or would improve
Example: “In my ONLEI Technologies internship, I worked on predicting house prices using Linear Regression and optimized the model using feature selection techniques.”
Final Thoughts
The key to cracking Data Science interviews is not just knowing concepts, but knowing how to apply them. Build a few strong projects, revise your basics, and practice mock interviews.
If you’re looking for structured guidance, ONLEI Technologies offers practical, hands-on Data Science programs with interview preparation, real datasets, and mentor support.
Prepare smart. Practice real. Crack your dream job.