Top 40 Data Science Interview Questions

Do you have a data science interview coming up? Not sure of what to expect? Don’t worry, we’ve got you covered! 

In this blog, we will guide you through the steps to prepare for data science interviews and present you with the top 40 data science interview questions that are frequently asked. This blog summarizes everything for you to skim through the day before and ace the interview with ease. Let’s get started!

Quick Answer

The top data science interview questions usually focus on statistics, machine learning, Python, SQL, and data cleaning. They test concepts like supervised vs unsupervised learning, feature engineering, model evaluation, and probability. You’ll also be asked about handling messy data and real business problems. Overall, these questions help employers see how you think and how well you apply data science in practical situations.

Steps to Prepare for an Interview

First things first, let’s jump into the steps to prepare for interviews. To attend any interview, the first thing you need is confidence. This confidence comes from the fact that you are well prepared and ready to answer any kind of question. By validating your skillset, you can build your confidence. Let’s look into the skills and how you can increase your confidence for the interview. 

Brush Up Your Basic Skills

The initial step of preparing for your data science interview is to brush up on your basic skills. The basic skills required for a data science role include Python, mathematics, statistics, data analysis, data processing, machine learning algorithms, and evaluation metrics. Having a decent understanding of the important concepts in each skill will help you boost your confidence in attending the interview. You can find the important concepts of each skill below.

• Python: Variable declarations, functions, classes, objects, and other pillars of object-oriented programming.
• Mathematics:  It includes linear algebra, calculus, and probability theory.
• Statistics: Hypothesis testing, regression analysis, and data distribution.
• Data Analysis: This involves data cleaning, exploration, processing, model building, and visualization using Python libraries such as numpy, pandas, and scikit-learn.
• Machine Learning Algorithms: Supervised(trained on labeled dataset), unsupervised(trained on unlabeled dataset), reinforcement learning, classification, regression, clustering, and recommendation systems.
• Evaluation Metrics: Accuracy, Precision, F1-score, Confusion matrix, Mean Absolute Error(MAE), Root Mean Squared Error (RMSE), and Mean Squared Error (MSE).

Understand the Job Description and Prepare for Specific Topics

Once you brush up on your basic skills required for data science, read the job description carefully and understand what they are expecting from the candidate. For example, let’s consider HCL Guvi’s job description for a data scientist role.

This is a Data Science Mentor job description at HCL Guvi. If you look closely at the skills, tools, and responsibilities section, you can find the most important things they look for in a candidate. They need a person with good communication skills, who knows teaching, tools such as SQL, MongoDB, ML, NLP, and a capstone project to showcase their expertise.

For this job description, the new skills include NLP, MongoDB, and SQL. You can skim through the new technologies they listed that you don’t know to boost your confidence further.

Attend Mock Interviews

To increase your confidence further and avoid being nervous during the interview, you can practice mock interviews with your friends or mentors. Practicing makes the man perfect. It takes a lot of practice to avoid the nervousness and the anxiety we feel during the interview. 
Check out our blog on How to Crack a Data Science Interview in 6 Steps to learn more about the skills and resources for data science roles. If you want to learn the necessary skills required for a data science starting from scratch to advance from India’s top Industry Instructors, consider enrolling in HCL GUVI’s Zen class course “Become a Data Science Professional with IIT-M Pravarta” that not only teaches you everything about data science, but also provides you with hands-on project experience an industry-grade certificate!

Top 40 Data Science Interview Questions With Answers

In this section, we will look into the top 30 data science interview questions that are frequently asked in interviews. These questions cover concepts from the basic to the advanced level. During the interview, you may be asked to answer behavioural questions. There is less possibility of behavioural questions; here, we will cover all the conceptual questions related to data science. Let’s jump into it without further delay.

Basic Data Science Knowledge

1. What is Data Science?

By the name itself, we can say that it is a science about data. Data science is the study of data to extract meaningful information and insights. Data science is used to provide a data-driven solution to real-world problems. It includes maths, statistics, machine learning(ML), and artificial intelligence(AI).

2. What are the steps involved in the data science lifecycle?

Building a data science project involves various steps that include:

• Define the Problem: As a first step, identify the business question or objective you want to address with your data science project. 
• Data Collection: The second step is to gather relevant data from various sources, including databases or web scraping, depending on the project’s needs. 
• Data Cleaning: Next, clean and pre-process the data by handling missing values, outliers, inconsistencies, and formatting issues. 
• Exploratory Data Analysis (EDA): This step is to visualize and analyze the data to understand its characteristics, patterns, and relationships between variables using techniques like histograms, scatterplots, and correlation matrices. 
• Feature Engineering: In this step, you will create new features from existing data that might be more predictive for your model.
• Model Building: For building a predictive model, select appropriate machine learning algorithms based on your problem and train models on the prepared data. 
• Model Evaluation: After building the model, assess the performance of the model using metrics like accuracy, precision, recall, and F1-score on a test dataset.
• Deployment: The last step is to integrate the best-performing model into a production environment where it can be used to make predictions on new data.

3. What is the difference between supervised and unsupervised learning?

Supervised Learning: It is a machine learning algorithm that is trained on labeled data (i.e., input-output pairs). The goal of supervised learning is to map the input data to the output labels. Example supervised learning algorithms include classification and regression algorithms. Example problem statements are spam detection and cancer detection using classification algorithms.

Unsupervised Learning: It is a machine learning algorithm that is trained on an unlabeled dataset (i.e., no pairs). The main goal of unsupervised learning is to find the hidden patterns or structures in the data and predict the output based on them. Clustering algorithms and Dimensionality reduction algorithms are examples of unsupervised learning algorithms. Example problem statements are video recognition and product recommendations.

4. What’s the difference between the concepts of overfitting and underfitting in machine learning?

Overfitting: Overfitting occurs in a model when the model learns the noise or random fluctuations in the training dataset rather than the actual underlying patterns. It results in poor performance on new or unseen data. To resolve this problem, you can use regularization (L1/L2) and cross-validation methods.

Underfitting: Underfitting occurs in a model when it is too simple to capture the underlying trends in the data. It can lead to wrong predictions on unseen data. It can be solved by using complex models, increasing the number of features or decreasing the regularizations.

5. What is A/B testing in Data science?

A/B Testing is a statistical method to compare two versions (A and B) of a webpage, app, or other digital asset to determine which one performs better. It involves splitting the audience into two groups and analyzing the impact of changes on a specific metric.

6. What are some common techniques used for handling missing data?

Techniques include:

• Deletion or removal of the specific row or column
• Replacing the missing values with estimated values using statistical methods such as the mean, median.
• Using forward and backward fill methods that use the values from previous or next observations to fill the missing values.

7. What is the Curse of Dimensionality?

The Curse of Dimensionality refers to the phenomenon where the feature space becomes increasingly sparse as the number of features grows, making it harder for models to generalize. It can lead to overfitting and requires techniques like dimensionality reduction to manage effectively.

8. What are bias and variance? How do they affect model performance?

Bias: It is an error that occurs due to overly simplistic assumptions in the model, which can cause underfitting of the model.

Variance: It is also an error due to the model’s sensitivity to small fluctuations in the training data, which can lead to overfitting.

The model should maintain a balance between bias and variance to minimize total error.

9. What is feature engineering?

Feature engineering involves creating new features or modifying existing features to improve the model’s performance. The best feature engineering can significantly increase the accuracy of the predictive model by providing more relevant information to learn from the dataset.

10. What is a confusion matrix?

A confusion matrix is a table used to evaluate classification models, showing the actual versus predicted class labels. It includes the following:

• True Positive (TP): The model correctly predicted a positive outcome
• True Negative (TN): The model correctly predicted a negative outcome
• False Positive (FP): The model incorrectly predicted a positive outcome. Also known as Type I error.
• False Negative (FN): The model incorrectly predicted a negative outcome. Also known as a Type II error.

Machine learning algorithms and statistics-based Questions

11. Can you explain the working of the Random Forest Algorithm?

Random forest is an ensemble learning algorithm that builds multiple decision trees. Each tree is trained on a random subset of the dataset and features and then aggregates its predictions for a final result that improves accuracy and stability.

12. What are the common ensemble methods?

Ensemble methods are machine learning techniques that combine multiple models to improve the accuracy of the prediction model. It is mostly used in supervised learning algorithms such as classification and regression. The popular ensemble methods are:

• Bagging: It is also known as aggregating. It is used to train multiple models independently on different subsets of the data. Example: Random forest
• Boosting: This method trains the model sequentially, so that each model learns from the mistakes of the previous model. Example: Gradient Boosting and AdaBoost
• Stacking: This method combines the predictions of multiple base models by training a meta-model to make a final prediction more accurate. Example: Combining predictions from different models

13. What is a Time Series analysis?

Time Series Analysis involves analyzing data points collected or recorded at specific time intervals. It includes methods for modeling and forecasting future data points based on historical data. Common techniques include ARIMA and Exponential Smoothing.

14. What is the difference between K-Means and K-Nearest Neighbors (KNN)?

K-Means is a clustering algorithm that partitions data into K clusters based on the similarity of data points.

K-Nearest Neighbors (KNN) is a classification algorithm that assigns a class to a data point based on the classes of its K nearest neighbors.

15. What is a Neural Network?

A Neural Network is a series of algorithms that mimic the operations of a human brain to recognize patterns. It consists of layers of interconnected nodes (neurons) that process data in a way that allows the network to learn complex patterns and make predictions.

15.1 What are Activation Functions in Neural Networks? (Follow-up questions)

Activation Functions determine whether a neuron should be activated or not. They introduce non-linearity into the network, allowing it to learn complex patterns. Common activation functions include ReLU (Rectified Linear Unit), Sigmoid, and Tanh.

16. What is Naive Bayes, and when do you use it?

Naive Bayes is a probabilistic classifier model based on Bayes’ theorem. It assumes that all the features are conditionally independent. It is effective for large datasets and text classification problems such as spam detection and sentiment analysis.

17. What is a Z-score?

A z-score is a statistical measure that measures how many standard deviations a data point is from the mean of the distribution. Z-scores are usually used to identify outliers or to compare values from different distributions.

18. What is a Decision Tree?

A decision tree is a supervised learning algorithm that uses a tree-like structure to make decisions by evaluating features and following a series of rules in the nodes of the tree. It is similar to a flowchart structure, where each node represents a condition or test, and each branch represents an outcome of the test, which leads to the final prediction.

19. What is a Support Vector Machine (SVM)?

SVM is also a supervised learning algorithm used for both classification and regression tasks. It is particularly useful in finding an optimal boundary (hyperplane) that separates data points of different classes, maximizing the margin between them. SVMs are suitable for applications that include image and text classification, and bioinformatics.

20. What is a p-value in Hypothesis Testing?

A p-value is a measure of the evidence against a null hypothesis. A low p-value (typically < 0.05) indicates strong evidence against the null hypothesis, suggesting that the observed effect is statistically significant.

Programming and Tools-based Questions

21. What is the difference between SQL and NoSQL databases? When would you use it?

22. Write a Python program to calculate the mean, median, and mode of a given list of numbers

23. Write a Python function to split data into training and testing sets.

24. How would you detect outliers in a dataset?

Outliers can be detected using several methods, some of which are:

• Visualization: Methods such as box plots and scatter plots are used to detect the outliers. Outliers can be spotted visually by looking for points that are far away from the rest of the points in the dataset.
• Statistical methods: Methods such as Z-score and Interquartile Range (IQR) are used to detect outliers. A Z-score greater than 3 or less than -3 indicates an outlier. In IQR, values outside the range Q1 -1.5 x IQR and Q3 + 1.5 x IQR are considered outliers. 

25. What are the main steps in exploratory data analysis (EDA)?

The main steps in Exploratory Data Analysis (EDA) are:

1. Data Collection: Gather data from different sources, including structured, semi-structured, and unstructured data.
2. Data Cleaning: Handle missing values, remove duplicates, correct inconsistencies, and fix errors.
3. Data Transformation: Convert data types, normalize/scale data, and perform feature engineering.
4. Descriptive Statistics: Calculate measures like mean, median, standard deviation, and correlations.
5. Visualization: Create visualizations such as histograms, box plots, scatter plots, and heatmaps to identify patterns and relationships.
6. Hypothesis Testing: Perform statistical tests to validate assumptions and draw inferences.

26. How can you merge two data frames in Python?

In pandas, you can merge two DataFrames using the merge() function, which allows you to combine DataFrames based on common columns or indices, similar to SQL joins.

27. How can you handle missing data in pandas?

Pandas have several methods for handling missing data. Some of them are:

• Identifying missing data: The isna() and isnull() functions in pandas are used to check for missing values in a dataset, which provides the number of null values present.
• Dropping missing values: To drop the missing values from the dataset, the dropna() function is used to drop the rows or columns.
• Filling missing values: fillna() is used to replace the missing values with a specific value or method

28. What are the differences between R and Python in Data Science?

• R is particularly strong in statistical analysis and visualization, with a rich set of packages for these purposes.
• Python is more versatile and widely used for general-purpose programming and machine learning, with libraries like Pandas, NumPy, and Scikit-learn.

29. What is a ROC Curve?

A Receiver Operating Characteristic (ROC) Curve is a graphical representation of a classification model’s performance. It plots the True Positive Rate (TPR) against the False Positive Rate (FPR) at various threshold settings, helping to evaluate the trade-offs between sensitivity and specificity.

30. Implement a function to find the most frequent element in a list

31. If your model’s accuracy suddenly drops overnight, what are the top 3 things you would investigate first?

I would investigate:

1. Data Drift – The incoming data distribution may have changed (e.g., new user behavior, new product features).
2. Feature Pipeline Breakage – A feature may have stopped updating or changed format, causing unexpected NaNs or incorrect values.
3. Concept Drift – The real-world relationship between features and target may have changed due to seasonal shifts or market events.

32. Explain a machine learning model to a 5-year-old using a real-world analogy.

A model is like a friend who learns from your past choices. If you always pick chocolate ice cream, your friend will guess “You want chocolate!” even before you say it. Similarly, a machine learning model learns patterns from past data to make predictions. The more it sees, the better it gets, and it can adapt to new situations over time.

33. You have imbalanced data: 98% class A, 2% class B. Accuracy is 98%. Why is this model useless?

Because the model can predict class A every time and still get 98% accuracy, but it never learns to identify class B, which is typically the business-critical minority class such as fraud, disease detection, or churn prediction. Accuracy hides imbalance; metrics like F1-score, precision/recall, and ROC-AUC matter more.

34. You can only choose ONE metric for a binary classification problem. Which metric would you pick and why?

I would choose F1 score because it balances precision and recall and avoids the trap of misleading accuracy when classes are imbalanced or when the cost of false positives and false negatives both matter. If I had to choose a threshold-independent metric, ROC-AUC would be my next pick because it evaluates performance across all possible decision thresholds.

35. Your boss says: “Let’s use a deep learning model.” How would you decide if that’s a good idea?

I would check whether we have enough labeled data, whether the problem actually requires deep feature learning, if latency or compute constraints allow heavy models, and whether explainability is required. Deep learning is powerful but expensive, slower, and harder to interpret.

36. You notice high variance in your model. What do you do?

High variance means the model is overfitting. I would apply regularization (L1, L2, dropout), simplify the model (fewer features or shallower architecture), gather more data, or use cross-validation to improve generalization.

37. If you had to reduce model training time by 10×, what approaches would you try?

I’d reduce feature dimensionality using PCA or feature selection, downsample the dataset for preliminary experiments, use smaller or faster model architectures, switch to GPU or distributed training, and replace grid search with randomized or Bayesian optimization. Also, I would cache preprocessing to avoid repeated computation.

38. Your clustering algorithm forms strange, meaningless clusters. What could be wrong?

The number of clusters might be wrong, feature scaling may be inconsistent (allowing one feature to dominate), the distance metric may be unsuitable for the data, or the dataset may not contain natural clusters, causing the algorithm to force patterns that don’t exist.

39. Explain “feature importance” to someone non-technical.

Feature importance is like ingredients in a recipe. If removing an ingredient ruins the dish, it has high importance. If nothing changes, it’s unimportant. It shows which features have the strongest influence on the model’s decisions.

40. If you could only ask one question to evaluate a dataset before modeling, what would it be?

If I can ask only one question, then I would ask, “What is the business question we are trying to answer?”
This defines the target, the evaluation metric, and the required data, and ensures the entire modeling pipeline aligns with the business goal instead of producing something irrelevant.

Tips to Prepare for Data Science Interviews

Preparing well for a data science interview means practicing the right way and not just reading. Here are practical steps you can start using immediately to improve your chances of success:

• Revise the Basics: Spend 20–30 minutes daily revising statistics, ML concepts, and SQL queries using simple notes or flashcards.
• Practice Coding Daily: Solve 2–3 Python or SQL problems on platforms like LeetCode or HackerRank to build speed and confidence.
• Build Mini Projects: Create small projects such as predicting house prices or analysing sales data to show real-world applications.
• Explain Your Thoughts Out Loud: Practice explaining a project or solution to a friend to improve clarity and communication.
• Use a Case Study Framework: Follow a simple approach – understand the problem, identify data needed, choose a method, and define success metrics.
• Review Latest Tools: Spend 10 minutes checking updates on libraries like pandas, scikit-learn, or new AI news to stay relevant.
• Record Mock Interviews: Do short mock interviews and record yourself to find gaps in explanation or confidence.

Conclusion

To ace your upcoming data science interview, you need to have a strong understanding of basic concepts in programming, maths, statistics, machine learning and data processing. By mastering these concepts, preparing the skills mentioned in the job description and frequently asked questions mentioned above, you can easily ace the interview. Happy Interviewing!

FAQs

1. What should I include in my data science portfolio?

Include a mix of projects that showcase your skills in data analysis, machine learning, and visualization. Highlight any end-to-end projects, from data cleaning to model deployment, and include links to your GitHub or personal website.

2. How can I demonstrate my problem-solving skills during an interview?

Use the STAR method (Situation, Task, Action, Result) to structure your responses to behavioral questions. For technical questions, clearly explain your thought process, the steps you took, and the reasoning behind your choices.

3. What should I do if I get stuck on a coding problem during the interview?

Stay calm and communicate your thought process. Explain what you’re trying to do, what assumptions you’re making, and where you think the issue might be. Interviewers appreciate seeing how you approach problem-solving.

4. What kind of questions should I ask the interviewer?

Ask about the team structure, the types of projects you’d be working on, the company’s data culture, opportunities for growth, and the tools and technologies the team uses. This shows your interest and helps you gauge if the role is a good fit.