Top 40 Data Science Interview Questions for Freshers [2025]

Are you a recent graduate aiming to become a Data Scientist? Did you know that data science interview questions for freshers require thorough preparation as the field is expected to grow by 36% over the next decade. 

Considered the most lucrative job of the 21st century, data science combines statistics, computer science, machine learning, and data analysis to extract valuable insights from data. If you’re preparing for your first data science role, understanding core concepts like supervised vs. unsupervised learning, overfitting, and model evaluation metrics is essential.

This guide covers the top 40 data science interview questions for freshers entering the field in 2025. From basic concepts to advanced techniques, we’ve organized these questions to help with your data science interview preparation. Let’s begin!

Most Commonly Asked Data Science Interview Questions for Freshers

In data science interviews, hiring managers typically assess your understanding of fundamental concepts first. Mastering these core questions will build a strong foundation for your interview preparation. Let’s explore the most commonly asked questions that every fresher should be ready to answer.

1. What is Data Science?

Data science is an interdisciplinary field that combines mathematics, statistics, computer science, and domain expertise to extract meaningful insights from data. It involves collecting, processing, analyzing, and interpreting large datasets to solve complex problems. Data science uses scientific methods, algorithms, and systems to extract knowledge from both structured and unstructured data. This field has evolved significantly since the 1990s when it was first recognized as a distinct discipline at academic conferences.

2. What is the difference between Data Science and Data Analytics?

While often used interchangeably, data science and data analytics are distinct concepts. Data science serves as an umbrella term covering various tasks performed on large datasets, including developing algorithms and creating AI applications. Data analytics, meanwhile, focuses on examining existing datasets to answer specific questions.

Data scientists design new processes for data modeling using algorithms and predictive models, whereas data analysts examine datasets to identify trends and create visual presentations for business decisions.

3. What is the difference between Supervised and Unsupervised Learning?

The primary distinction between these machine learning approaches is the use of labeled data:

• Supervised learning uses labeled datasets to train algorithms, enabling them to classify data or predict outcomes. The algorithm learns from input-output pairs and adjusts until it can accurately predict results for new data.
• Unsupervised learning works with unlabeled data, discovering hidden patterns without human intervention. These algorithms analyze data structure without predefined categories.

Furthermore, supervised learning is ideal for classification and regression problems, while unsupervised learning excels at clustering and association tasks.

4. What is Overfitting and Underfitting?

Overfitting occurs when a model learns the training data too well, including noise and random fluctuations, causing poor performance on new data. An overfit model shows low training error but significantly higher testing error.

Underfitting, conversely, happens when a model is too simple to capture underlying patterns, resulting in poor performance on both training and testing data. The goal is finding the right balance—a model that generalizes well to new data without memorizing the training set.

5. What is the curse of dimensionality?

The curse of dimensionality refers to problems that arise when analyzing data in high-dimensional spaces. As the number of features increases, the volume of the space grows exponentially, making data points sparse and models less effective. It can lead to overfitting, increased computational cost, and degraded model performance. Dimensionality reduction techniques like PCA or feature selection help mitigate this issue.

6. What is the role of p-value in hypothesis testing?

The p-value measures the probability of obtaining observed results assuming the null hypothesis is true. It helps determine whether to reject the null hypothesis based on statistical significance:

• A smaller p-value indicates stronger evidence against the null hypothesis
• Typically, a p-value below 0.05 is considered statistically significant
• The p-value approach allows researchers to report the statistical significance level, letting readers interpret results themselves

7. What is the difference between classification and regression?

Both classification and regression are supervised learning techniques, but they serve different purposes:

• Classification predicts discrete categories or labels (e.g., spam/not spam)
• Regression predicts continuous numerical values (e.g., house prices)

The key difference lies in their output: classification maps input to discrete classes, while regression maps input to continuous quantities.

8. What is the Central Limit Theorem?

The Central Limit Theorem states that with a sufficiently large sample size, the sampling distribution of the mean will be normally distributed regardless of the population’s original distribution. This fundamental statistical concept allows for reliable statistical inference even when working with non-normal data. By convention, a sample size of 30 or more is generally considered sufficient for the theorem to apply.

Basic Data Science Interview Questions for Freshers

After mastering the common interview questions, let’s explore the basic concepts that form the foundation of data science. These questions assess your fundamental knowledge and are essential for freshers preparing for data science interviews.

9. What are KPI, Lift, and DOE?

KPI (Key Performance Indicator) is a measurable metric used to evaluate how effectively a company is achieving its business objectives. For instance, error rate can be a KPI for model performance.

Lift measures how well your targeting model performs compared to a random choice model. Essentially, lift tells you how much better your predictive model is than if you had no model at all.

DOE (Design of Experiments) is a systematic approach to determine the relationship between factors affecting a process and the output of that process. It helps predict outcomes based on changes in independent variables.

10. What are sampling techniques and why are they used?

Sampling allows researchers to draw conclusions about populations by examining only a portion of data, saving time and resources. The two main categories are:

• Probability sampling: Methods like simple random, systematic, stratified, and cluster sampling where each element has a known chance of selection.
• Non-probability sampling: Techniques like convenience, quota, purposive, and snowball sampling where selection isn’t random.

Sampling is particularly valuable when dealing with large datasets or when collecting data from every individual is impractical.

11. What is the difference between long and wide format data?

Wide format data contains values that don’t repeat in the first column, with each individual entity occupying a single row and each variable in a separate column. This format is people-friendly and typically used for analysis.

Long format data allows multiple rows for each entity, recording new attributes or observations as new rows. It’s machine-friendly and preferred for visualization tools and certain analyzes like time-series.

The choice between formats depends on your specific needs—wide for human readability and analysis, long for visualization and handling multi-dimensional data.

12. What is a bias-variance tradeoff?

The bias-variance tradeoff describes the relationship between a model’s complexity, prediction accuracy, and ability to generalize to new data.

Bias is the error from oversimplified models that miss relevant patterns (underfitting). Variance is the error from sensitivity to small fluctuations in training data (overfitting).

As model complexity increases, bias typically decreases while variance increases. The challenge is finding the optimal balance that minimizes total error without overfitting or underfitting the data.

13. What is a confusion matrix and how is it used?

A confusion matrix is a table that visualizes classification model performance by showing:

• True Positives (TP): Correctly predicted positive cases
• True Negatives (TN): Correctly predicted negative cases
• False Positives (FP): Incorrectly predicted positive cases (Type I error)
• False Negatives (FN): Incorrectly predicted negative cases (Type II error)

From these values, you can calculate metrics like accuracy, precision, recall, and F1-score to evaluate model performance thoroughly.

14. What is the difference between mean, median, and mode?

Mean is the average of all values, calculated by summing all numbers and dividing by the count. It’s sensitive to outliers.

Median is the middle value when data is arranged in order. It’s less affected by outliers and better for skewed distributions.

Mode is the most frequently occurring value, primarily used for categorical data.

The choice among these depends on your data distribution—mean for normal distributions, median for skewed data, and mode for categorical variables.

15. What is a null hypothesis?

A null hypothesis (H₀) is a statistical assumption suggesting that observed patterns or differences in data are due to chance alone. It’s the default position that there is no relationship between variables or no effect from a treatment.

In hypothesis testing, you attempt to reject the null hypothesis based on evidence from your data. For example, if testing whether a new algorithm improves performance, the null hypothesis might be “there is no improvement.”

16. What is the use of cross-validation?

Cross-validation is a technique to evaluate model performance on unseen data by:

• Splitting data into multiple subsets or “folds”
• Training the model on some folds and testing on others
• Rotating through different combinations of training and testing sets

This approach prevents overfitting, provides more robust performance estimates than a single train-test split, and is particularly valuable when working with limited data. The most common method is k-fold cross-validation, typically with k=5 or k=10.

Intermediate Data Science Interview Questions for Freshers

Moving beyond basics, intermediate data science concepts often appear in technical rounds of interviews. These questions test your analytical skills and understanding of machine learning fundamentals.

17. What is logistic regression and where is it used?

Logistic regression is a supervised machine learning algorithm used for binary classification problems. Unlike linear regression, it predicts the probability of a categorical outcome using the sigmoid function to map values between 0 and 1. It’s commonly used in fraud detection, disease prediction, and customer churn analysis. The model’s interpretability makes it valuable in fields like healthcare and finance where explaining decisions is crucial.

18. What is linear regression and its limitations?

Linear regression models the relationship between a dependent variable and one or more independent variables by fitting a linear equation. However, it has several limitations: it assumes a linear relationship between variables, is sensitive to outliers, and often oversimplifies complex real-world relationships. Additionally, it struggles with multicollinearity (when predictors are correlated) and cannot effectively model non-linear relationships.

19. What is the difference between correlation and covariance?

Covariance measures how two variables change together, while correlation measures both the strength and direction of their relationship. Key differences:

• Covariance ranges from -∞ to +∞ and depends on the scale of variables
• Correlation is standardized, ranging from -1 to +1, making it scale-independent
• Correlation = Covariance/(Standard Deviation of X × Standard Deviation of Y)

20. What is feature selection and why is it important?

Feature selection identifies the most relevant variables for model building. It improves model performance by reducing dimensionality, decreasing overfitting, and enhancing interpretability. Good feature selection leads to faster training times, lower computational costs, and often better accuracy by removing noise from irrelevant features.

21. What is the difference between bagging and boosting?

Both are ensemble learning techniques, yet they work differently:

• Bagging (Bootstrap Aggregating) trains models independently in parallel using random subsets of data, reducing variance and preventing overfitting
• Boosting trains models sequentially, with each model focusing on correcting errors made by previous ones, reducing bias and improving predictive accuracy

22. What is the use of regularization in machine learning?

Regularization prevents overfitting by adding a penalty term to the loss function during model training. This discourages learning overly complex models by constraining parameter values. Common techniques include L1 (Lasso) regularization, which can shrink coefficients to zero (enabling feature selection), and L2 (Ridge) regularization, which reduces the magnitude of all coefficients.

23. What is the difference between precision and recall?

Precision measures the accuracy of positive predictions (TP/(TP+FP)), focusing on minimizing false positives. Recall (sensitivity) measures the ability to find all positive instances (TP/(TP+FN)), focusing on minimizing false negatives. In fraud detection, precision may be prioritized, while in disease diagnosis, recall is often more critical.

24. What is the ROC curve?

The Receiver Operating Characteristic (ROC) curve plots the true positive rate against the false positive rate at various classification thresholds. It visualizes the trade-off between sensitivity and specificity. The Area Under the Curve (AUC) measures the model’s ability to distinguish between classes—a perfect model has an AUC of 1.0, while random guessing yields 0.5.

Advanced Data Science Interview Questions for Freshers

Advanced concepts often distinguish exceptional candidates in data science interviews. Mastering these topics demonstrates a deeper understanding of the field and its cutting-edge techniques.

25. What are eigenvalues and eigenvectors?

Eigenvalues and eigenvectors are fundamental concepts in linear algebra that help analyze matrices. An eigenvector is a non-zero vector that, when multiplied by a matrix, changes only in scale (not direction). The eigenvalue is the scaling factor applied to the eigenvector. In data science, they’re crucial for dimensionality reduction techniques and understanding data variance. They help extract key features from datasets and understand the structure inherent in data.

26. What is PCA and how does it help in dimensionality reduction?

Principal Component Analysis (PCA) transforms correlated variables into a smaller set of uncorrelated variables called principal components. It works by finding directions (eigenvectors) with maximum variance in the data. PCA helps by:

• Reducing computational complexity
• Minimizing multicollinearity and overfitting
• Improving visualization by projecting high-dimensional data into lower dimensions
• Preserving as much information as possible while reducing dimensions

27. What are autoencoders?

Autoencoders are neural networks that learn efficient data encodings in an unsupervised manner. They consist of two main parts: an encoder that compresses input data into a latent-space representation, and a decoder that reconstructs the original input from this compressed form. Autoencoders excel at dimensionality reduction, feature learning, anomaly detection, and image denoising. Unlike PCA, autoencoders can capture complex non-linear correlations in data.

28. What is a generative adversarial network (GAN)?

A GAN consists of two competing neural networks: a generator that creates new data instances and a discriminator that evaluates their authenticity. The generator tries to produce realistic data while the discriminator attempts to distinguish fake from real data. Throughout training, both networks improve until the generator creates data so realistic that the discriminator cannot differentiate it from authentic samples. GANs are used for generating realistic images, data augmentation, and completing missing information.

29. What are exploding and vanishing gradients?

These are problems encountered when training deep neural networks:

• Vanishing gradients occur when gradients become extremely small during backpropagation, making earlier layers learn very slowly or not at all
• Exploding gradients happen when gradients grow exponentially, causing unstable updates that lead to NaN values

Both issues make training difficult, especially in deep networks and RNNs. Solutions include gradient clipping, proper weight initialization, and using architectures like LSTM.

30. What is the difference between RNN and CNN?

RNNs (Recurrent Neural Networks) and CNNs (Convolutional Neural Networks) serve different purposes:

• RNNs handle sequential data by maintaining memory of previous inputs, making them ideal for text, time series, and speech
• CNNs excel at spatial data like images by using filters to detect patterns regardless of their position

Their architectures differ fundamentally: RNNs have feedback loops, while CNNs use convolutional layers with filters and pooling operations.

31. What is the use of LSTM in time series?

Long Short-Term Memory (LSTM) networks are specialized RNNs designed to overcome the vanishing gradient problem. In time series analysis, LSTMs are valuable because they:

• Remember information over long sequences through their unique memory cell structure
• Capture both short and long-term dependencies in temporal data
• Control information flow through input, forget, and output gates
• Perform well on tasks like time series prediction, anomaly detection, and sequence classification

32. What is the difference between batch and stochastic gradient descent?

These gradient descent variants differ in how they process data:

• Batch gradient descent uses the entire dataset to compute gradients, providing accurate estimates but requiring significant computation
• Stochastic gradient descent (SGD) uses a single training example per iteration, converging faster but with noisier updates

Batch GD works well for smooth error surfaces but struggles with large datasets. SGD is faster and can escape local minima, though it’s less stable. A compromise between the two is mini-batch gradient descent, which uses small random subsets of data.

Scenario-Based and Practical Data Science Interview Questions for Freshers

Real-world data science problems rarely present themselves as neatly as theoretical concepts. Scenario-based questions test your ability to apply knowledge to practical situations, a crucial skill for freshers entering the field.

33. How would you handle missing values in a dataset?

Initially, analyze the pattern of missingness—whether it’s random or systematic. For small amounts of missing data, consider removing affected rows. For larger gaps, use imputation methods like mean/median for numerical data or mode for categorical values. More advanced approaches include KNN imputation or using algorithms that handle missing values internally.

34. How would you deal with imbalanced data?

Imbalanced datasets occur when one class significantly outnumbers others. Address this through:

• Resampling: Oversample minority class or undersample majority class
• Synthetic generation: Create new minority samples using techniques like SMOTE
• Algorithmic approaches: Use algorithms less sensitive to imbalance or adjust class weights

35. How would you train a model on a large dataset with limited RAM?

Tackle RAM limitations through batch processing, where you train on smaller chunks of data sequentially. Alternatively, use incremental learning algorithms that update gradually with new data. Indeed, tools like Dask provide pandas-like functionality with out-of-memory computation. Consider dimensionality reduction or feature selection to decrease dataset size.

36. How would you evaluate a model’s performance?

Avoid relying solely on accuracy, as it can be misleading with imbalanced data. Instead, choose appropriate metrics: precision/recall for classification problems with imbalanced classes, RMSE for regression, or AUC-ROC for ranking performance. Importantly, use cross-validation to ensure reliable evaluation.

37. How would you choose the right algorithm for a problem?

Select algorithms based on problem type (classification, regression, clustering), data characteristics, interpretability needs, and computational constraints. Start with simpler models as baselines before testing more complex approaches.

38. How would you explain a model to a non-technical stakeholder?

Focus on business outcomes rather than technical details. Use clear visualizations, concrete examples, and relatable analogies. Connect model predictions to business metrics that stakeholders care about, such as revenue impact or cost savings.

39. How would you improve a model that is underperforming?

Analyze error patterns to identify where the model struggles. Check for data quality issues, try feature engineering, tune hyperparameters, or consider a different algorithm class altogether. Cross-validation can help prevent overfitting during improvement efforts.

40. How would you handle outliers in your data?

First visualize data to detect outliers using methods like box plots or Z-scores. Then decide whether to remove them (if they’re errors), cap them at thresholds, transform the data, or use robust algorithms less sensitive to outliers.

If you’re aiming to ace your data science interviews and build a strong foundation, GUVI’s Data Science Course is your ideal launchpad. With hands-on projects, mentorship from industry experts, and placement support, this course equips you with both the technical skills and interview readiness to land top data science roles.

Concluding Thoughts…

As we conclude, I’d like to highlight that preparing thoroughly for data science interviews significantly increases your chances of landing that dream job in 2025. Data science continues to evolve rapidly, therefore staying updated with core concepts remains essential for career growth. The questions covered throughout this guide provide a comprehensive foundation for freshers entering this competitive field.

Remember that technical knowledge alone won’t guarantee success. During interviews, you must demonstrate both theoretical understanding and practical problem-solving abilities. Consequently, practicing with real-world datasets and scenario-based problems should become part of your preparation routine.

With consistent practice and the comprehensive question set provided in this guide, you’ll undoubtedly be well-equipped to face your data science interviews confidently and successfully.