Interviewing for a data scientist position can feel intimidating, but preparation is your secret weapon. After years of working in data science and guiding friends through their interviews, I’ve compiled this list of 20 questions. Each one is paired with practical answers and tips I’ve personally used or seen work.

1. What’s the difference between supervised and unsupervised learning?

Imagine you’re a teacher.

Supervised learning is when understudies have reading material and answers—they learn from labeled examples.

Unsupervised learning is like giving them perplexes with no arrangement guide—they figure out designs on their own.

When inquired this, I bring up real-world examples: predicting house costs (supervised) vs. client division (unsupervised).

2. What’s the bias-variance tradeoff?

I remember explaining this during a mock interview using a dartboard analogy:

High bias means the darts miss the board entirely but are close to each other (underfitting).

High variance means they scatter all over the board (overfitting).

Balancing bias and variance is about hitting that sweet spot in the middle—something I often validate using cross-validation.

3. How do you handle missing data?

Missing data can mess up any analysis. Here’s what I do:

Drop rows/columns if there’s little missing data.

Impute: Use the mean, median, or a model to fill gaps.

Leverage context: Once, I worked on a project for a retail client where missing sales data was estimated using nearby stores’ averages.

4. Explain p-value in simple terms.

I’ve found this explanation works wonders: The p-value tells you how likely your results are if the null hypothesis is true. A low p-value means it’s probably not random. I always stress this: a small p-value doesn’t mean the effect size is big—it just means it’s real.

5. What’s overfitting, and how can you avoid it?

Overfitting is like memorizing the answers to a test instead of understanding the material. The model works great on training data but flops on new data.

I prevent it by:

Regularizing models (Ridge or Lasso).

Cross-validating.

Keeping the model simple.

6. What’s the curse of dimensionality?

When you add too many features, data points spread thin, and meaningful patterns are harder to find. I often deal with this by using PCA or focusing on the most relevant features.

7. How do you evaluate a classification model?

Accuracy isn’t enough. For example, in fraud detection, false negatives are costly, so I prioritize metrics like:

Precision: Are my positive predictions accurate?

Recall: Am I catching all actual positives?

F1-score: A balance of precision and recall.

8. Can you explain A/B testing?

I love this one—it’s basically an experiment! You compare two groups: A (control) and B (variation). For instance, I once tested two email subject lines to see which improved click-through rates. It’s all about statistical significance.

9. What’s feature engineering?

This is where data science becomes art. For example, I once turned timestamps into “day of the week” and “hour of the day” features, which improved sales predictions significantly.

10. How do you handle multicollinearity?

When predictors are highly correlated, it can distort regression models. My fixes:

• Drop one variable.
• Use Ridge regression.
• Combine correlated features with PCA.

11. Explain the Central Limit Theorem (CLT).

The CLT says that if you take enough samples, their mean will follow a normal distribution, no matter the population’s shape. I’ve used this principle countless times in hypothesis testing.

12. What’s the difference between bagging and boosting?

Bagging is like a team working independently (e.g., Random Forest), while boosting is like a relay race, where each model corrects the last (e.g., Gradient Boosting).

13. What tools do you use for data visualization?

For Python, I use Matplotlib and Seaborn. For dashboards, I turn to Tableau or Power BI. I once built an interactive sales report for a client using Plotly—it blew them away!

14. What’s regularization?

Regularization adds penalties to a model to prevent overfitting. L1 (Lasso) can shrink some coefficients to zero, while L2 (Ridge) keeps them small.

15. What’s the lifecycle of a data science project?

Every project I’ve done follows this process:

• Define the problem.
• Collect and clean data.
• Explore patterns through EDA.
• Build and evaluate models.
• Deploy and monitor.

16. What’s your favorite data science project?

One of my favorites was predicting equipment failures for a factory. We reduced downtime by 25% by implementing a real-time monitoring system.

17. How do you deploy a machine learning model?

I usually use Flask or FastAPI for APIs and deploy on AWS or Azure. In one case, I set up monitoring tools to catch performance drifts after deployment.

18. What’s your approach to imbalanced datasets?

In fraud detection, I balanced the dataset by using:

• SMOTE (oversampling).
• Adjusted thresholds to favor recall over precision.

19. Why do you want to work here?

I always research the company beforehand. For example, I might say, “Your focus on innovation aligns with my passion for applying data science to solve impactful problems.”

20. What’s your approach to learning new tools or techniques?

I’d share a personal story. For instance, I once learned TensorFlow over a weekend for a project. My go-to resources are online courses, documentation, and experimentation.

Data science interviews can feel overwhelming, but preparation builds confidence. Use these questions to practice, and always tie your answers to real experiences. You’ve got this—best of luck!