Top 25 Data Science Interview Questions and Answers

Did you know that jobs for data scientists are set to grow by 36 per cent from 2021 to 2031? That’s a massive opportunity waiting for you! But here’s the thing: to grab those promising data science jobs, you have to nail the interview. Now, interviews can be scary, but we have a solution for you. In this guide, we’ve put together a list of the top 25 data science interview questions and answers. It will help you prepare for interviews and land that dream job with ease.

Let’s begin!

Data Science Interview Questions for Freshers

Here are some common data science interview questions and answers for freshers:

1. What is data science, and why is it important?

Data Science is an interdisciplinary field that involves the use of statistical and computational methods to extract insights from data. It involves a combination of skills from mathematics, statistics, computer science, and domain expertise. Data Science is crucial because it helps organizations make data-driven decisions and gain a competitive edge.

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

Supervised learning is a type of machine learning where the algorithm learns from labelled data, while unsupervised learning is a type of machine learning where the algorithm learns from unlabeled data. Supervised learning is used for classification and regression problems, while unsupervised learning is used for clustering and dimensionality reduction.

3. Explain the bias-variance trade-off in machine learning.

The bias-variance trade-off is about finding the right balance between a model’s simplicity (bias) and its ability to fit the data (variance). It’s essential to prevent overfitting or underfitting.

4. What is feature engineering, and why is it crucial in data science?

Feature engineering involves creating new features or modifying existing ones to improve model performance. It’s crucial because better features can lead to more accurate predictions.

5. How does regularization help prevent overfitting in machine learning models?

Regularization techniques like L1 (Lasso) and L2 (Ridge) add penalties to the model’s coefficients, preventing them from becoming too large and reducing overfitting.

6. What’s the difference between a data analyst and a data scientist?

Data analysts focus on analyzing and visualizing data to provide insights, while data scientists go a step further by building predictive models and developing machine learning algorithms.

7. Explain the concept of cross-validation in machine learning.

Cross-validation is a technique used to assess a model’s performance by dividing the data into training and validation sets multiple times, helping to prevent overfitting and assess generalization.

8. What is the curse of dimensionality, and how does it impact data analysis?

The curse of dimensionality refers to the challenges and increased computational requirements that arise when dealing with high-dimensional data. It can lead to sparsity and increased complexity in data analysis.

Data Science Interview Questions for Experienced

Here are some common data science interview questions and answers for experienced:

9. What is the purpose of A/B testing in data science, and how does it work?

A/B testing is used to compare the performance of two versions (A and B) of a product or webpage. It helps determine which version performs better based on user behaviour.

10. Explain the ROC curve and AUC in the context of binary classification.

The ROC curve (Receiver Operating Characteristic) visually represents a model’s performance at different threshold values, while AUC (Area Under the Curve) quantifies the model’s overall performance.

11. What are some common data preprocessing techniques? 

Common data preprocessing techniques include data cleaning, data normalization, data transformation, and feature scaling. 

Data cleaning involves removing missing values and outliers, while data normalization involves scaling the data to a specific range. 

Data transformation involves converting the data into a more suitable format for analysis, while feature scaling involves scaling the features to a similar range.

12. Discuss the differences between bagging and boosting in ensemble learning.

Bagging (Bootstrap Aggregating) builds multiple independent models and averages their predictions while boosting combines multiple models sequentially, giving more weight to misclassified instances.

13. How do you handle missing data in a dataset, and what are the potential pitfalls?

Missing data can be handled by imputation, deletion, or modelling. However, imputation must be done carefully to avoid introducing bias into the data.

14. What is the purpose of principal component analysis (PCA), and how does it work?

PCA is used to reduce the dimensionality of data while preserving as much variance as possible. It does this by finding orthogonal axes, known as principal components, in the data.

15. Explain the concept of time series analysis in data science and its applications.

Time series analysis involves studying data points collected over time to make predictions or identify trends. It’s applied in various fields, including finance, weather forecasting, and sales forecasting.

Statistics Interview Questions for Data Science

Here are some common statistics data science interview questions and answers:

16. What is the difference between a parameter and a statistic? 

A parameter is a numerical characteristic of a population, while a statistic is a numerical characteristic of a sample.

17. What is the difference between a null hypothesis and an alternative hypothesis? 

A null hypothesis is a statement that there is no significant difference between two populations or variables, while an alternative hypothesis is a statement that there is a significant difference between two populations or variables.

18. What is a p-value? How do you interpret it? 

A p-value is the probability of observing a test statistic as extreme as or more extreme than the one observed, assuming that the null hypothesis is true. A p-value less than 0.05 indicates strong evidence against the null hypothesis and suggests that it should be rejected.

19. What is a confidence interval? How do you interpret it? 

A confidence interval provides a range of values within which the true population parameter is likely to fall with a certain degree of confidence. 

For example, if we construct a 95% confidence interval for the mean height of all people in the world, we can say that we are 95% confident that the true mean height falls within this interval.

20. What is the difference between Type I and Type II errors? 

Type I error occurs when we reject the null hypothesis when it is actually true, while Type II error occurs when we fail to reject the null hypothesis when it is actually false.

Python Interview Questions for Data Science

Here are some common data science programming interview questions and answers:

21. What is NumPy, and how is it used in data science?

NumPy is a Python library for numerical operations. It’s used for working with arrays and matrices, making it a fundamental tool in data science.

22. How do you remove duplicates from a list in Python? 

You can use the set() function to remove duplicates from a list. The set() function converts the list to a set, which automatically removes duplicates. You can then convert the set back to a list using the list() function.

23. What is a lambda function in Python, and where is it commonly used in data science?

A lambda function is an anonymous function in Python. It’s often used in data science for quick, simple operations on data, especially in functions like map and filtering.

24. How do you sort a dictionary by value in Python? 

You can use the sorted() function to sort a dictionary by value. The sorted() function takes a dictionary as input and returns a list of tuples sorted by value.

25. How do you create a scatter plot in Python using Matplotlib? 

You can use the scatter() function from the matplotlib.pyplot module to create a scatter plot. The scatter() function takes two arrays as input, one for the x-axis and one for the y-axis.

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

Company-Specific Interview Questions for a Data Scientist

Here are a few company-specific data science interview questions:

Deloitte Data Scientist Interview Questions

Here are some common Deloitte data scientist interview questions:

1. How do you merge a code to git? What is the difference between correlation and causation?
2. What is embedding?
3. Can you explain techniques to overcome imbalanced datasets?
4. Which is faster, joins or subqueries?
5. What is multicollinearity?

Infosys Data Scientist Interview Questions

Here are some common Infosys data scientist interview questions:

1. Explain Clustering. 
2. Explain the EDA process.  
3. Different libraries in NLP. 
4. Why do we need to scale continuous variables?
5. Explain Correlation and covariance.

TCS Data Scientist Interview Questions

Here are some common TCS data scientist interview questions:

1. What are the feature vectors?
2. What is root cause analysis?
3. Explain cross-validation.
4. What is the goal of A/B testing?
5. Explain star schema.

Microsoft Data Scientist Interview

Here are some common Microsoft data science interview questions:

1. Have you used normalization in your queries?
2. Give some use cases for Pandas and NumPy.
3. For data visualization, what would be the first step you would do?
4. How do you gauge the effectiveness of a machine-learning model?
5. How will you convert a string to an int in Python?

Google Data Scientist Interview Questions

Here are some common questions for data scientist Google interview:

1. How would you differentiate between K-mean and EM?
2. What are the applications of Feature Selection in AI?
3. How can you build estimators for medians?
4. How could you test if a metric has increased on a change you made in a Google app?
5. How can NoSQL databases be better than SQL databases?

PayPal Data Scientist Interview

Here are some common PayPal data scientist interview questions:

1. Explain the Naive Bayes algorithm.
2. Give us an example of the application of TensorFlow in a production system.
3. How would you find feature importance in a neural network?

Walmart Data Science Interview Questions

Here are some common Walmart data scientist interview questions:

1. Write the code to reverse a Linked list.
2. How will you fix multi-colinearity in a regression model?
3. What is the Law of Large Numbers?

EY Data Scientist Interview Questions

Here are some common EY data science questions:

1. What is your experience with data cleaning and preparation? 
2. What is your experience with big data technologies? 
3. What is your experience with statistical modelling? 

Data Science Interview Preparation Tips

Here are some tips to help you prepare for data science interview:

• Master data science basics
• Hone coding skills in Python
• Know ML algorithms
• Excel in data manipulation
• Learn data visualization
• Prepare for behavioural questions
• Conduct mock interviews
• Stay updated in the field
• Optimize your resume and portfolio

Conclusion

Understanding these top 25 data science interview questions and answers can help you land your dream data science job. Just remember, practice and preparation is the key.If you’re on the hunt for data science jobs, explore the best job opportunities on Hirist. Easily install the app, browse data science job listings, and conveniently apply for various positions right from your smartphone.