Data Science Basics Interview Online Test
Data Science Basics technical interview questions and answers are crucial for freshers and job seekers aiming to enter data analytics, AI, ML, and data-driven job roles. Companies like TCS, Infosys, Wipro, Accenture, Cognizant, and Capgemini frequently test candidates on foundational concepts such as statistics, probability, EDA, data visualization, ML basics, Python fundamentals, data cleaning, and real-world problem-solving. Interviewers evaluate both conceptual understanding and practical application skills, making strong fundamentals essential.
This guide contains the most important questions designed to help you understand essential data science concepts and perform confidently during technical rounds. Practicing these interview questions enables you to explain models clearly, interpret data logically, and demonstrate your analytical thinking. Whether you’re preparing for campus placements or entry-level roles, these technical interview Q&A will help you build a strong data science foundation and succeed in your interviews.
Data science aspirants must strengthen their foundation in machine learning algorithms and Python programming for advanced analytics roles
1. Describe the difference between supervised and unsupervised learning in data science
Answer: Supervised learning involves training a model on labeled data, where the outcome is known. Unsupervised learning deals with unlabeled data, where the model tries to find patterns or groupings without predefined outcomes.
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2. What is overfitting in machine learning and how can it be prevented
Answer: Overfitting occurs when a model learns the details and noise in the training data to the extent that it negatively impacts the performance on new data. It can be prevented by using techniques such as cross-validation, regularization, and pruning.
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3. Explain the concept of cross-validation in machine learning
Answer: Cross-validation is a technique used to evaluate the performance of a model by dividing the data into multiple folds. The model is trained on some folds and tested on the remaining fold, and this process is repeated multiple times.
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4. What is the purpose of feature scaling and how is it performed
Answer: Feature scaling standardizes the range of independent variables or features of data. It is performed using techniques like normalization (scaling between 0 and 1) or standardization (scaling to have zero mean and unit variance).
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5. Describe the difference between precision and recall in classification models
Answer: Precision measures the proportion of true positive results in all positive predictions, while recall measures the proportion of true positive results in all actual positive cases. Precision focuses on the quality of positive predictions, and recall focuses on capturing all positive cases.
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6. What is a confusion matrix and what are its key components
Answer: A confusion matrix is a table used to evaluate the performance of a classification model. It includes True Positives (TP), True Negatives (TN), False Positives (FP), and False Negatives (FN). These components help in calculating metrics like accuracy, precision, recall, and F1 score.
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7. Explain the bias-variance tradeoff in machine learning
Answer: The bias-variance tradeoff refers to the balance between model complexity and performance. High bias can lead to underfitting, where the model is too simple, while high variance can lead to overfitting, where the model is too complex and sensitive to fluctuations in the training data.
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8. What are ensemble methods and give examples
Answer: Ensemble methods combine the predictions of multiple models to improve overall performance. Examples include bagging (e.g., Random Forest), boosting (e.g., Gradient Boosting Machines), and stacking.
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9. Describe the purpose and method of dimensionality reduction in data science
Answer: Dimensionality reduction aims to reduce the number of features in a dataset while preserving important information. Methods include Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE).
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10. What is the role of the ROC curve and AUC in evaluating classification models
Answer: The ROC curve (Receiver Operating Characteristic curve) plots the True Positive Rate against the False Positive Rate at various threshold settings. The AUC (Area Under the ROC Curve) measures the model’s ability to distinguish between classes, with higher values indicating better performance.
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11. Explain the difference between parametric and non-parametric models
Answer: Parametric models make assumptions about the form of the data distribution (e.g., linear regression). Non-parametric models do not make such assumptions and can model data more flexibly (e.g., k-Nearest Neighbors).
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12. What is the purpose of regularization in machine learning
Answer: Regularization is used to prevent overfitting by adding a penalty to the model’s complexity. Techniques such as L1 (Lasso) and L2 (Ridge) regularization add constraints to the model parameters.
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13. Describe what a hyperparameter is and how it differs from a model parameter
Answer: Hyperparameters are external configurations set before the learning process begins (e.g., learning rate, number of trees). Model parameters are learned from the training data and define the model’s behavior.
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14. What are outliers and how can they impact a data analysis
Answer: Outliers are data points that differ significantly from other observations. They can skew and mislead the interpretation of the data, affecting statistical analyses and model performance. Methods for handling outliers include removal or transformation.
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15. Explain the use of clustering in unsupervised learning
Answer: Clustering is a technique used to group similar data points together based on their features. Common algorithms include k-Means, Hierarchical Clustering, and DBSCAN.
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16. What is the purpose of feature engineering in machine learning
Answer: Feature engineering involves creating new features or modifying existing ones to improve model performance. This process includes techniques like normalization, encoding categorical variables, and extracting new features from existing data.
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17. Describe the difference between a linear regression model and a logistic regression model
Answer: Linear regression predicts a continuous outcome variable, while logistic regression predicts a categorical outcome variable, typically used for binary classification tasks.
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18. What is the significance of the p-value in hypothesis testing
Answer: The p-value measures the probability of observing the data, or something more extreme, if the null hypothesis is true. It helps determine whether to reject the null hypothesis based on a significance level.
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19. How do you handle missing data in a dataset
Answer: Missing data can be handled through imputation (e.g., mean, median, or mode imputation), deletion (removing rows or columns with missing values), or by using algorithms that can handle missing values directly.
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20. What is cross-validation and how does it improve model evaluation
Answer: Cross-validation splits the data into training and testing subsets multiple times to ensure that the model’s performance is consistent and not dependent on a particular data split, providing a more robust evaluation.
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21. Explain the concept of time series analysis and its applications
Answer: Time series analysis involves analyzing data points collected or recorded at specific time intervals to identify trends, seasonal patterns, and other time-dependent behaviors. Applications include stock market analysis and economic forecasting.
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22. What is the purpose of data normalization and standardization
Answer: Data normalization scales features to a range, typically 0 to 1, while standardization scales features to have zero mean and unit variance. Both techniques improve model performance and convergence.
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23. Describe the difference between a decision tree and a random forest
Answer: A decision tree splits the data based on feature values to make predictions, while a random forest is an ensemble of multiple decision trees that combines their predictions to improve accuracy and reduce overfitting.
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24. What are some common metrics for evaluating regression models
Answer: Common metrics for evaluating regression models include Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared.
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25. How do you select important features for a machine learning model
Answer: Feature selection can be done using methods such as Recursive Feature Elimination (RFE), feature importance from models (e.g., Random Forest), and statistical techniques (e.g., correlation analysis).
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26. Explain the concept of ensemble learning and its benefits
Answer: Ensemble learning combines multiple models to improve overall performance by reducing the risk of overfitting and increasing robustness. Techniques include bagging, boosting, and stacking.
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