Data science is an incredibly dynamic and rapidly evolving field, with growing opportunities in various industries. A data scientist earns a median salary of $108,020, according to the U.S. Bureau of Labor Statistics, making it one of the most sought-after and lucrative careers in tech today. As companies continue to leverage data to drive business decisions, the demand for skilled data scientists has never been higher. 

For freshers looking to break into this field, understanding how to stand out in a job interview is essential. Preparing thoroughly for data science interview questions for freshers will give you a strong edge. A solid grasp of core concepts, tools, and applications in data science is crucial to making a lasting impression and securing your spot in this growing field.

In this article, we will explore the top data science interview questions for freshers, providing you with the insights and knowledge needed to excel in your interviews and embark on a successful career in data science.​

What is Data Science?

Data Science is an interdisciplinary field that combines scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. It involves multiple steps, such as data collection, cleaning, analysis, and interpretation, with a focus on solving complex problems and generating actionable insights. 

Data science uses statistical analysis, machine learning, data mining, and big data technologies to provide data-driven solutions. Common tools used in Data Science include programming languages like Python and R, machine learning libraries, and big data platforms such as Hadoop and Spark. Data scientists work closely with business stakeholders to identify challenges, develop models, and optimise outcomes.

Basic Data Science Interview Questions for Freshers

For freshers, interview questions often focus on the core concepts of data science such as basic statistics, machine learning algorithms, and data cleaning techniques. This is the foundation upon which more advanced concepts are built.

1. What is the difference between deep learning and machine learning?

Why it's important: Understanding the difference between deep learning and machine learning is crucial for selecting the appropriate approach to solve specific problems in data science and AI. Both techniques are often used interchangeably, but they differ in their complexity, applications, and the kind of problems they are best suited to solve. 

Sample Answer:

Machine Learning (ML)

• Definition: Machine learning is a subset of AI where algorithms learn patterns from data by identifying relationships and making predictions based on that. It involves training a model on a dataset, where the model is designed to make decisions or predictions without explicit programming.

Deep Learning (DL)

• Definition: Deep learning is a more advanced subset of machine learning that mimics the way the human brain works. It uses artificial neural networks with many layers (hence the term "deep") to model complex patterns and make predictions. Deep learning does not require feature engineering because the model learns to identify important features directly from the data.

Key Differences:

• Data Requirements: Deep learning typically requires a large amount of labeled data to perform well, while machine learning can work with smaller datasets.
• Model Complexity: Deep learning models are more complex, consisting of multiple layers and requiring significant computational resources (e.g., GPUs), while machine learning models are generally simpler and faster to train.
• Feature Engineering: In machine learning, features need to be manually selected, whereas deep learning can automatically learn features from the data.

2. Define the terms KPI, Lift, Model Fitting, Robustness, and DOE.

Why it's important: Understanding these terms is crucial as they form the foundation of evaluating performance and improving data models in data science and machine learning. Each term plays a role in assessing how well a model works, how to improve it, and how it can be applied to real-world problems.

Sample Answer:

• KPI (Key Performance Indicator): A KPI is a measurable value that demonstrates how effectively a company is achieving a business objective. For example, in e-commerce, KPIs can include metrics like conversion rate, average order value, and customer retention rate. KPIs help organisations track their progress toward set goals.
• Lift: Lift refers to the improvement in the predictive power of a model when compared to random chance. It is often used in marketing and sales to measure how much more likely a customer is to take a certain action (e.g., purchase a product) after receiving a targeted offer, compared to without the offer.
• Model Fitting: Model fitting is the process of adjusting a machine learning model's parameters so that it best matches the observed data. Proper model fitting ensures that the model generalises well to unseen data and doesn’t overfit to the noise of the training set. For example, fitting a linear regression model involves determining the optimal values for the coefficients that minimise the error between the predicted and actual data points.
• Robustness: Robustness refers to the ability of a model or system to perform well under a variety of conditions, including in the presence of noise or outliers in the data. A robust model is less sensitive to small variations in the input data and can maintain performance even when data is slightly imperfect.
• DOE (Design of Experiments): DOE is a systematic method used to plan and analyse experiments that systematically vary one or more factors to observe their effect on a dependent variable. It is commonly used in industrial, agricultural, and scientific experiments to optimise processes or products. It helps identify causal relationships between variables.

3. What is the difference between data analytics and data science?

Why it's important: Understanding the distinction between data analytics and data science is important for knowing which approach or technique to apply depending on the task at hand. This knowledge helps businesses use the right methods for generating insights and solving problems.

Sample Answer:

Data analytics and data science are closely related but differ in their scope and methods.

• Data Analytics focuses on analysing existing data to generate actionable insights. It uses techniques like descriptive statistics, exploratory data analysis, and reporting. Data analysts typically use tools like Excel, SQL, and Tableau to summarise past performance and identify trends.
• Data Science, on the other hand, goes beyond just analysing data. It involves using advanced mathematical models, machine learning algorithms, and predictive analytics to uncover deeper insights, forecast future trends, and create data-driven solutions. Data scientists often work with big data technologies, programming languages like Python, and statistical methods to build complex models and perform simulations.

While data analytics is often used for decision-making and reporting, data science enables organisations to develop more sophisticated solutions to complex problems, especially in real-time or large-scale environments.

4. What are some of the techniques used for sampling? What is the main advantage of sampling?

Why it's important: Sampling is essential in data analysis because it allows researchers and analysts to make inferences about a large population without needing to process every single data point. Proper sampling techniques ensure that insights are accurate while saving time and resources.

Sample Answer:

Sampling is the process of selecting a subset of data from a larger population to make inferences about the whole group. Some common sampling techniques include:

• Simple Random Sampling: Every member of the population has an equal chance of being selected. It’s the most straightforward approach and helps prevent bias.
• Stratified Sampling: The population is divided into subgroups (strata) based on certain characteristics, and samples are taken from each subgroup. This ensures that important categories are represented.
• Systematic Sampling: A sample is selected at regular intervals from the population. For example, choosing every 10th person in a list.
• Cluster Sampling: The population is divided into clusters, and a random selection of these clusters is made. This method is useful when dealing with large, geographically dispersed populations.
• Convenience Sampling: A non-random sampling technique where samples are chosen based on their convenience or accessibility. While easy, it can introduce bias.

The main advantage of sampling is that it reduces the resources and time required for data collection and analysis. With proper sampling techniques, you can still make reliable estimates about the larger population without having to examine every data point.

5. List down the conditions for Overfitting and Underfitting.

Why it's important: Overfitting and underfitting are common issues in machine learning and statistical modeling. Understanding their causes is crucial for building models that generalise well to new data and avoid performance pitfalls.

Sample Answer:

Overfitting and underfitting are two common problems that occur when training machine learning models.

• Overfitting occurs when a model learns the details and noise in the training data to the extent that it negatively impacts its performance on new, unseen data. Conditions for overfitting include:
  
  • The model is too complex, with too many parameters or features compared to the size of the dataset.
  • The model fits the training data too closely, capturing random fluctuations and noise.
  • Lack of regularisation techniques (e.g., dropout or L1/L2 regularisation) to penalise overly complex models.
• Underfitting happens when a model is too simplistic and fails to capture the underlying patterns in the data. Conditions for underfitting include:
  
  • The model is too simple, with too few parameters or features.
  • The training process is stopped too early, and the model hasn't had enough time to learn from the data.
  • Insufficient model complexity or inadequate training data.

To strike the right balance between overfitting and underfitting, it’s important to choose the right model complexity, use techniques like cross-validation, and ensure proper regularization during model training.

6. Differentiate between the long and wide format data.

Why it's important: Understanding the difference between long and wide formats is crucial for data analysis, as the format impacts how data is processed, analyzed, and visualized. Using the wrong format can lead to misinterpretations of data and hinder the effectiveness of statistical models.

Sample Answer:

Long format and wide format are two ways of structuring data for analysis.

• Long Format: In the long format, each observation (row) represents a single measurement or record. Multiple variables are stored in separate columns, and each row corresponds to a specific instance of the measurement, with repeated entries for the same entity. This format is often used when there are multiple measurements per subject, such as in time series or repeated measures data.

Example: In a dataset of patient visits, if there are multiple visits for each patient, the data would be represented in long format with columns like Patient_ID, Visit_Date, and Measurement.

• Wide Format: In the wide format, each entity (such as a subject or observation) occupies one row, and multiple columns are used for the measurements of interest. This format is useful when you need to display multiple values for the same variable (e.g., the same measurement taken at different times) in a single row.

Example: In a similar patient dataset, instead of listing each visit in separate rows, the measurements from different visits for each patient would be placed in distinct columns (e.g., Visit1_Measurement, Visit2_Measurement).

While the wide format is easier for some types of analysis, the long format is more flexible and suitable for statistical methods such as mixed-effects models and time series analysis.

7. What are Eigenvectors and Eigenvalues?

Why it's important: Eigenvectors and eigenvalues are foundational concepts in linear algebra and are crucial for many machine learning algorithms, especially in principal component analysis (PCA) and dimensionality reduction. They help in understanding the variance in data and simplifying complex datasets.

Sample Answer:

Eigenvectors and eigenvalues are mathematical concepts used primarily in the analysis of linear transformations.

• Eigenvectors: An eigenvector is a non-zero vector that only changes by a scalar factor when a linear transformation is applied to it. In simpler terms, when you multiply a matrix by an eigenvector, the output is just a scaled version of the original vector. Eigenvectors represent the directions in which the data is most spread out or has the most variance.
• Eigenvalues: An eigenvalue is a scalar that describes the factor by which the eigenvector is stretched or compressed during the linear transformation. It provides information about the magnitude of variance along the eigenvector. The eigenvalue gives an indication of the importance or contribution of that eigenvector to the overall data structure.

For example, in PCA, the eigenvectors are the directions of maximum variance, and the eigenvalues tell us the amount of variance in the data along those directions. Larger eigenvalues correspond to directions where data variance is higher, and smaller eigenvalues correspond to less important dimensions.

8. What does it mean when the p-values are high and low?

Why it's important: P-values are critical in hypothesis testing as they help determine the strength of evidence against the null hypothesis. Understanding the significance of high and low p-values is essential for making informed decisions in statistical analysis.

Sample Answer:

A p-value is a measure used in hypothesis testing to determine whether the observed data is consistent with the null hypothesis.

• Low p-value (< 0.05): A low p-value indicates strong evidence against the null hypothesis, suggesting that the observed data is unlikely under the null hypothesis. Typically, if the p-value is less than 0.05 (the standard threshold), we reject the null hypothesis in favour of the alternative hypothesis. A p-value of 0.01, for example, suggests that there is only a 1% chance that the observed data would occur if the null hypothesis were true.
• High p-value (> 0.05): A high p-value indicates weak evidence against the null hypothesis, suggesting that the observed data is consistent with the null hypothesis. In this case, we do not have enough evidence to reject the null hypothesis. For instance, a p-value of 0.8 suggests a very high probability that the observed data could occur under the null hypothesis, so we fail to reject it.

Understanding the meaning of p-values helps in determining the significance of test results and in deciding whether the observed effect is meaningful or just due to random chance.

9. When is resampling done?

Why it's important: Resampling is a powerful technique used to address various challenges in data analysis, such as small sample sizes, imbalanced data, and model validation. It's essential for ensuring the robustness and generalizability of statistical models.

Sample Answer:

Resampling refers to the process of repeatedly drawing samples from the original dataset to estimate properties of the population or improve model performance.

• Cross-validation: One of the most common uses of resampling is in cross-validation, where the dataset is split into training and test sets multiple times to assess the model’s performance and reduce overfitting. This technique helps ensure that the model generalises well to new, unseen data.
• Bootstrapping: Bootstrapping is another resampling method that involves sampling with replacement from the dataset to create multiple subsets. This technique is often used to estimate the sampling distribution of a statistic and calculate confidence intervals.
• Addressing Imbalanced Data: Resampling is also used to handle imbalanced datasets, where one class is underrepresented. In such cases, oversampling the minority class or undersampling the majority class can help balance the data and improve model accuracy.

Resampling is used in various situations, such as model validation, performance assessment, and dealing with small or imbalanced datasets, ensuring that statistical results and model predictions are more reliable.

10. What do you understand by Imbalanced Data?

Why it's important: Imbalanced data is a common problem in machine learning, especially when dealing with classification tasks. Understanding this concept and knowing how to handle imbalanced data is crucial for building effective models and making accurate predictions.

Sample Answer:

Imbalanced data occurs when the distribution of classes in a dataset is not uniform, meaning that one class (or category) significantly outnumbers the other(s). This is common in tasks like fraud detection, where fraudulent transactions are much fewer than legitimate ones.

• Impact on Model Performance: When the data is imbalanced, machine learning algorithms tend to be biased toward the majority class, leading to poor performance for the minority class. For example, a model predicting whether a transaction is fraudulent might predict ‘no fraud’ for most transactions, but it won’t be effective in detecting actual fraud cases.
• Handling Imbalanced Data: There are several techniques to deal with imbalanced data:
  
  • Resampling: As mentioned earlier, you can either oversample the minority class or undersample the majority class to balance the data.
  • Synthetic Data Generation: Techniques like SMOTE (Synthetic Minority Over-sampling Technique) can be used to generate synthetic data points for the minority class to create a balanced dataset.
  • Algorithm Adjustment: Some machine learning algorithms, such as decision trees and random forests, have built-in parameters that allow you to adjust the decision threshold to handle imbalanced data better.

Want to impress in your Data Science interview? Practice mock interviews with real professionals at Topmate! Get personalised feedback on your responses and improve your interview performance.

‍

‍

With the basics covered, let’s move on to questions that delve deeper into the field and test your intermediate knowledge of data science.

Intermediate Data Science Interview Questions for Freshers

Intermediate data science interview questions are designed to test your understanding of more complex topics like model evaluation, feature engineering, and deeper statistical concepts. These questions assess how you can apply foundational knowledge in practical scenarios.

1. Are there any differences between the expected value and the mean value?

Why it's important: Understanding the difference between the expected value and mean value is fundamental to statistical analysis, especially in probability theory. While they are closely related, they are used in different contexts and can represent different concepts depending on the distribution of the data.

Sample Answer:

The expected value and the mean value are terms often used interchangeably in basic statistics, but they can have subtle differences based on the context.

• Mean Value: The mean (also called the arithmetic mean) is calculated by summing all values in a dataset and dividing by the number of data points. It’s a simple measure of central tendency, used to describe a typical value in a dataset. Example: For the dataset {2, 4, 6, 8}, the mean is (2 + 4 + 6 + 8) / 4 = 5.
• Expected Value: In probability theory, the expected value is a weighted average of all possible outcomes of a random variable, weighted by their respective probabilities. It represents the long-run average or the theoretical average of a random variable if an experiment is repeated many times. Example: If you roll a fair six-sided die, the expected value would be the sum of each face value multiplied by the probability (1/6 for each side), so (1+2+3+4+5+6)/6 = 3.5.

While the mean is often used to describe data, the expected value is used in the context of probabilistic scenarios and decision-making models.

2. What do you understand by Survivorship Bias?

Why it's important: Survivorship bias occurs when conclusions are drawn from data that excludes those who have failed or dropped out of a process. This bias can lead to inaccurate conclusions or overestimations of success rates, making it crucial to understand and mitigate in data analysis and decision-making.

Sample Answer:

Survivorship bias is a logical error that occurs when only the 'survivors' or successful cases are considered in an analysis, while failures or non-survivors are ignored. This bias leads to distorted conclusions because the sample used doesn’t reflect the true picture of the entire population.

• Example: A classic example of survivorship bias is the analysis of successful companies. If you only study successful companies, you might conclude that certain business practices lead to success. However, this ignores all the companies that failed despite following the same practices, leading to a biased understanding of what contributes to success.
• Impact: Survivorship bias can create overly optimistic views about success rates, especially in fields like investing, entrepreneurship, or healthcare. For example, if you study only the successful startups in Silicon Valley, you might miss the fact that thousands of startups fail each year, leading to an inaccurate assessment of the probability of success.

To avoid survivorship bias, it’s important to consider both the successful and unsuccessful cases in analysis to get a complete and realistic view.

3. What is a Gradient and Gradient Descent?

Why it's important: Gradients and gradient descent are fundamental concepts in machine learning and optimisation. Understanding how gradients are used to guide model optimisation and how gradient descent minimizes error is key to training efficient machine learning models.

Sample Answer:

Gradient and gradient descent are key concepts in optimisation and machine learning algorithms, especially when training models to minimise errors and improve predictions.

• Gradient: A gradient is a vector that points in the direction of the steepest slope of a function. It represents the rate of change of a function with respect to its parameters. In machine learning, gradients are used to indicate how much the model's output changes with respect to its parameters, guiding the optimization process.

Example: In linear regression, the gradient helps us understand how the model's predictions change as we adjust the weights (parameters) of the model.

• Gradient Descent: Gradient descent is an iterative optimisation algorithm used to minimise the cost (error) function by adjusting the parameters in the direction of the negative gradient. The algorithm updates the parameters in small steps, proportional to the gradient and a learning rate, to find the values that minimise the error. 

Example: In training a neural network, the gradient descent algorithm adjusts the weights of the network by moving in the direction that reduces the error, improving the accuracy of the model over time.

Gradient descent is crucial for efficiently optimising machine learning models, ensuring they learn from data and make accurate predictions.

4. Define confounding variables.

Why it's important: Confounding variables are variables that affect both the independent and dependent variables in a study, leading to a false or misleading interpretation of the relationship between them. Identifying and controlling for confounders is essential to ensure valid conclusions in research.

Sample Answer:

Confounding variables are external factors that influence both the independent variable (cause) and the dependent variable (effect), creating a false or misleading impression of the relationship between them.

• Example: Suppose you’re studying the relationship between exercise and weight loss, but you fail to account for diet. If people who exercise also tend to eat healthier, diet could be a confounder that distorts the true effect of exercise on weight loss.
• Impact: Confounding variables can introduce bias into research findings, leading to incorrect conclusions. For instance, failing to account for age, gender, or socioeconomic status in a health study could result in a misinterpretation of the factors affecting health outcomes.

To control for confounding variables, researchers often use statistical techniques like matching, stratification, or regression analysis to isolate the effect of the independent variable and ensure a more accurate relationship is observed.

5. Define and explain selection bias?

Why it's important: Selection bias occurs when the sample collected for a study or analysis is not representative of the larger population, leading to skewed or inaccurate results. Recognising and addressing selection bias is crucial to ensure the validity and generalizability of research conclusions.

Sample Answer:

Selection bias is a type of bias that occurs when the sample used in a study is not representative of the broader population, leading to skewed results.

• Example: A study that only includes data from college-educated participants may not be generalizable to the entire population, especially those without a college education. This introduces selection bias, as the sample does not accurately represent the diversity of the target population.
• Impact: Selection bias can lead to misleading conclusions and reduce the external validity of research findings. For instance, a survey on the effectiveness of a new medication conducted only with healthy individuals may not provide accurate results for patients with pre-existing conditions.

To address selection bias, researchers can use random sampling, ensure diversity in their sample population, or apply statistical adjustments to account for differences between the sample and the population.

6. Define bias-variance trade-off?

Why it's important: The bias-variance trade-off is a fundamental concept in machine learning that impacts the model’s ability to generalize. Understanding the trade-off helps you to strike a balance between underfitting and overfitting, improving the model's performance on unseen data.

Sample Answer:

The bias-variance trade-off refers to the balance between two sources of error in machine learning models: bias and variance.

• Bias: Bias is the error introduced by approximating a real-world problem with a simplified model. High bias leads to underfitting, where the model is too simple to capture the underlying patterns in the data. For instance, using a linear model to capture a complex relationship leads to high bias.
• Variance: Variance is the error introduced by the model's sensitivity to small fluctuations in the training data. High variance leads to overfitting, where the model learns noise and random fluctuations in the training data rather than the actual patterns. An example would be using a very complex model that fits the training data too well but fails to generalise on new data.

The key challenge in machine learning is to find the right balance where both bias and variance are minimised. Regularisation techniques, cross-validation, and selecting the right model complexity can help in addressing the trade-off.

7. Define the confusion matrix?

Why it's important: The confusion matrix is a fundamental tool for evaluating the performance of classification algorithms. It provides detailed insights into the types of errors the model is making, which is crucial for improving model accuracy and reliability.

Sample Answer:

A confusion matrix is a table used to evaluate the performance of a classification model by comparing the predicted values against the actual outcomes. The matrix contains four key components:

• True Positives (TP): The number of instances correctly classified as positive.
• True Negatives (TN): The number of instances correctly classified as negative.
• False Positives (FP): The number of instances incorrectly classified as positive when they are actually negative (Type I error).
• False Negatives (FN): The number of instances incorrectly classified as negative when they are actually positive (Type II error).

These four components are used to calculate key performance metrics, such as accuracy, precision, recall, and F1 score. By examining the confusion matrix, you can get a clear picture of where the model is making mistakes, helping you to focus on improving those areas, such as addressing false positives or false negatives.

8. What is a random forest? Explain its working.

Why it's important: Random Forest is one of the most widely used ensemble learning methods for classification and regression tasks. Understanding how it works and why it is powerful is crucial for selecting the right model in machine learning tasks.

Sample Answer:

Random Forest is an ensemble learning algorithm that builds multiple decision trees and merges their results to improve accuracy and reduce overfitting. Each tree in the forest is built using a random subset of the data and features, which introduces diversity and reduces the variance compared to a single decision tree. Here’s how Random Forest works:

• Training: The algorithm creates multiple decision trees by using bootstrapped samples of the dataset (random sampling with replacement). For each tree, a subset of features is chosen randomly at each split to reduce correlation between the trees.
• Prediction: When making a prediction, each tree in the forest provides a prediction. For classification tasks, the prediction is based on the majority vote across all trees, while for regression tasks, the average of the predictions is taken.
• Out-of-Bag Error: Random Forest also provides an internal method for estimating the model’s error using the "out-of-bag" data (samples not used in training a particular tree).

Example: A recent use case for Random Forest was in predicting customer satisfaction in an e-commerce store, where different customer characteristics (e.g., age, purchase history) were used as features to predict customer ratings. The Random Forest model was able to provide accurate predictions with a low risk of overfitting, compared to a single decision tree model.

9. How do you identify if a coin is biased?

Why it's important: Identifying whether a coin is biased is crucial in both theoretical probability and real-world applications like gaming, quality control, or decision-making processes. It ensures fairness and integrity in various scenarios where randomness is critical. Being able to detect bias can help make informed decisions, avoid unfair outcomes, and refine experimental methods.

Sample Answer:

To identify if a coin is biased, you would typically conduct a statistical test based on the results of multiple coin flips. Here's how you can approach it:

1. Perform the Experiment: Flip the coin a large number of times, say 1000 or more, to gather sufficient data. The more flips, the more reliable your test will be.
2. Record the Results: Track the outcomes (heads or tails) for each flip. Count how many heads and tails you get.
3. Calculate the Proportions: Determine the proportions of heads and tails. For a fair coin, the probability of getting heads or tails should each be approximately 50%. In the case of 1000 flips, you would expect about 500 heads and 500 tails.
4. Perform a Statistical Test: Apply a hypothesis test, such as a Chi-square test, to compare the observed outcomes with the expected outcomes. The null hypothesis (H0) assumes the coin is fair (i.e., a 50% chance for heads and 50% for tails). A p-value less than a significance level (usually 0.05) indicates that the results are statistically significant, suggesting that the coin is biased.
5. Interpret the Results: If the p-value is greater than 0.05, you fail to reject the null hypothesis, suggesting no significant bias. If the p-value is less than 0.05, you reject the null hypothesis and conclude that the coin may be biased.

In this way, you can identify whether a coin has a bias towards heads or tails. Statistically, the higher the number of flips, the more reliable your results will be. Consistent bias would show up as a significant deviation from 50%.

10. How is the grid search parameter different from the random search tuning strategy?

Why it's important: Hyperparameter tuning is a critical step in optimising machine learning models. Understanding the difference between grid search and random search helps in selecting the right method for efficient model optimisation. Choosing the right strategy can significantly reduce computation time and improve model performance, especially when dealing with large datasets and complex models.

Sample Answer:

Grid search and random search are both techniques for hyperparameter tuning, but they differ significantly in how they explore the hyperparameter space.

1. Grid Search:
   
   • Definition: Grid search is an exhaustive search method where you define a set of values for each hyperparameter, and the algorithm tests every possible combination of hyperparameters from that set.
   • Process: You specify a grid of hyperparameter values, and the search evaluates all combinations systematically. For example, if you're tuning two hyperparameters, alpha and beta, with three possible values for each, grid search will evaluate all 3 × 3 = 9 combinations.
   • Advantages: It ensures that you explore every possible combination in the defined search space, guaranteeing that no potentially optimal set of hyperparameters is overlooked.
2. Random Search:
   
   • Definition: Random search is a method where you randomly sample hyperparameter combinations within the predefined search space. Instead of testing every combination, it selects random configurations, often providing good results with fewer iterations.
   • Process: You specify the distribution of each hyperparameter, and random search samples random combinations of these hyperparameters. For example, it might randomly choose 5 or 10 combinations and evaluate their performance.

Advantages: It is generally more computationally efficient than grid search, as it does not require testing every single combination. For large search spaces, random search often finds a good solution faster than grid search.

Topmate can connect you with professionals at Google, Microsoft, Amazon, and more! Get referred to top Data Science roles faster and boost your job prospects today.

‍

‍

Now that we’ve tackled intermediate-level questions, let’s explore more challenging concepts to help you stand out in an advanced data science interview.

Advanced Data Science Interview Questions for Freshers

Advanced data science interview questions dive into sophisticated algorithms, deep learning models, and real-world applications. These questions are aimed at testing how well you can handle complex data science tasks and solve real-world business problems.

1. How are time series problems different from other regression problems?

Why it's important: Time series problems require a unique approach compared to other regression problems because the data points are sequential and depend on time. Identifying the differences between time series and general regression models helps in building more accurate models, as traditional methods might not capture temporal dependencies effectively.

Sample Answer: 

Time series problems differ from other regression problems primarily because of the time-dependent nature of the data. In traditional regression, the data points are independent of one another. However, in time series analysis, the value of a variable at a particular time point often depends on its previous values. This sequential dependence introduces additional challenges such as seasonality, trends, and autocorrelation that need to be addressed for accurate forecasting.

Key differences include:

• Temporal Dependence: Unlike typical regression, where each observation is treated independently, time series models account for the relationship between observations at different time intervals.
• Stationarity: Many time series models assume stationarity (the property that statistical properties like mean and variance do not change over time), which is not a concern in standard regression problems.
• Modeling Components: Time series models, such as ARIMA, decompose data into components like trend, seasonality, and noise, which is unnecessary for other types of regression.

2. What are Support Vectors in SVM (Support Vector Machine)?

Why it's important: Support vectors are the critical elements of data in the Support Vector Machine algorithm, which help in creating an optimal hyperplane for classification. Understanding the role of support vectors is essential for grasping how SVM works in classifying data effectively, especially in high-dimensional spaces.

Sample Answer:

Support Vectors in SVM (Support Vector Machine) refer to the data points that are closest to the decision boundary, or hyperplane, that separates different classes. These points are critical because they directly influence the position of the hyperplane and determine the decision boundary.

• Role of Support Vectors: Support vectors are the data points that lie on the margin edges, which is the distance between the decision boundary and the closest points from each class. These points are the most informative for the model, and they help maximise the margin, which in turn improves the generalisation of the SVM model.
• Maximising Margin: The core idea behind SVM is to create a hyperplane that maximises the margin between classes. The support vectors define this margin, and by focusing on these critical points, the algorithm can achieve a robust classification model.
• Impact of Support Vectors: If you remove a support vector, the hyperplane could change, affecting the classification result. This makes support vectors highly significant for model accuracy, as they provide the model with essential guidance on how to separate the classes effectively.

For example, in binary classification of emails as spam or not spam, support vectors would be the emails closest to the decision boundary that separate spam from non-spam emails.

3. Explain Neural Network Fundamentals.

Why it's important: Neural networks are the foundation of deep learning and play a critical role in a variety of machine learning applications. Understanding neural network fundamentals is essential for implementing and optimizing models that can recognize patterns, classify data, and make predictions.

Sample Answer:

Neural networks are a class of machine learning algorithms inspired by the structure and function of the human brain. They consist of layers of interconnected neurons (nodes) that process input data and learn patterns over time. A neural network typically consists of the following components:

• Input Layer: This layer receives the input data and passes it onto the next layer. Each node represents a feature or characteristic of the input data.
• Hidden Layers: These layers perform computations and extract features from the input data. Neural networks can have one or more hidden layers, and the more layers there are, the deeper the network.
• Output Layer: The final layer produces the output or prediction. For a classification task, this might represent the predicted class, and for regression, it could be the predicted value.
• Activation Functions: These functions determine whether a neuron should be activated or not, helping the network learn nonlinear patterns. Common activation functions include ReLU (Rectified Linear Unit), Sigmoid, and Tanh.
• Weights and Biases: Each connection between neurons has a weight that determines the importance of the input. Bias terms help shift the activation function, making the network more flexible.

Neural networks learn by adjusting weights and biases using a process called backpropagation during training, which minimises the loss function. As the network processes more data, it learns complex patterns and improves its predictions.

4. What is Generative Adversarial Network (GAN)?

Why it's important: GANs are powerful models in machine learning used for generating new data instances that resemble the training data. Understanding GANs is crucial for working with generative models, such as for creating realistic images or data augmentation.

Sample Answer:

Generative Adversarial Networks (GANs) are a type of deep learning model that consists of two networks working together in a competitive manner: a generator and a discriminator. The generator creates fake data instances, while the discriminator evaluates them. The two networks are trained simultaneously in a process called adversarial training.

• Generator: The generator creates fake data from random noise, attempting to generate data that closely resembles real-world data. The generator’s goal is to improve over time, fooling the discriminator into thinking its generated data is real.
• Discriminator: The discriminator’s job is to distinguish between real and fake data. It compares the generated data with real data and provides feedback to the generator, guiding it to improve its outputs.
• Adversarial Process: As the generator and discriminator compete, both improve their performance. The generator learns to produce more realistic data, while the discriminator becomes better at detecting fakes. This process continues until the generator produces data indistinguishable from real data.

GANs have found applications in various fields, including image generation, video generation, and even deepfake technology. A popular use of GANs is in creating high-quality images for games, movies, and virtual environments, or in data augmentation for training other machine learning models.

Need guidance on transitioning into Data Science or growing your career? Connect with a Topmate mentor who can provide 1:1 coaching and help you take the next big step.

‍

‍

5. What is a computational graph?

Why it's important: A computational graph is a visual representation of a mathematical computation where nodes represent operations or variables, and edges represent data flow. It’s critical for understanding how algorithms work under the hood, especially in neural networks, as it helps in optimising computations and enabling efficient parallel processing.

Sample Answer:

A computational graph is a diagram that represents the flow of operations and data in a computation. It consists of nodes (which represent operations like addition or multiplication) and edges (which represent data being passed between operations). This graph is fundamental for efficiently managing complex computations, especially in deep learning frameworks like TensorFlow or PyTorch.

• Nodes: Each node in the graph represents an operation, such as matrix multiplication, activation function, or addition.
• Edges: The edges represent the flow of data (or tensor) from one operation to the next. These data points are passed through the graph to perform calculations and generate the final result.
• Use in Deep Learning: Computational graphs are especially important in deep learning, as they allow frameworks to automatically differentiate through the entire graph. This feature is key for backpropagation, where gradients are computed for optimisation.

Overall, computational graphs help organise and optimise computations, making machine learning algorithms more efficient and scalable, particularly in large models.

6. What are auto-encoders?

Why it's important: Autoencoders are a class of neural networks used for unsupervised learning tasks such as dimensionality reduction, anomaly detection, and data denoising. Understanding how autoencoders work is key for building models that can compress and reconstruct data without supervision, which is essential in various machine learning applications.

Sample Answer:

An autoencoder is a type of neural network used to learn efficient codings of input data. It consists of two parts: the encoder and the decoder. The encoder compresses the input into a lower-dimensional representation, while the decoder reconstructs the input data from this compressed form. The goal of an autoencoder is to minimize the reconstruction error between the original input and the reconstructed data.

• Encoder: The encoder part of the network reduces the input data into a compressed representation, called the latent space or bottleneck. This step captures the essential features of the data.
• Decoder: The decoder reconstructs the input from the compressed representation. The reconstruction aims to be as close as possible to the original input.
• Use cases: Autoencoders are often used for dimensionality reduction, anomaly detection, and image denoising. For instance, in anomaly detection, autoencoders can identify unusual patterns in data by comparing the reconstruction error.

7. What are Exploding Gradients and Vanishing Gradients?

Why it's important: Exploding and vanishing gradients are common problems that arise during the training of deep neural networks, especially when using backpropagation. These issues can lead to poor model performance or make it impossible to train deep models, which is why understanding them is essential for building stable, effective neural networks.

Sample Answer:

Exploding and vanishing gradients are issues that occur during the backpropagation process in deep neural networks. These problems arise when gradients (the derivatives used to adjust weights) either become too large (exploding gradients) or too small (vanishing gradients), causing learning to be unstable or slow.

• Exploding Gradients: This occurs when gradients become too large during backpropagation, causing the model weights to update excessively and resulting in numerical instability. It often leads to model weights that are far outside the ideal range, making the learning process unstable.
• Vanishing Gradients: This happens when gradients become extremely small as they propagate back through the network, causing the weights to update very little. This typically occurs in very deep networks or when using activation functions like Sigmoid or Tanh, which squash gradients to small values.
• Solutions: To prevent exploding gradients, techniques like gradient clipping are used to limit the size of gradients during training. For vanishing gradients, using activation functions like ReLU (Rectified Linear Unit), which do not squash gradients, can help.

Both problems hinder the training process, making it critical to apply proper techniques for network stability during training.

8. What are the differences between correlation and covariance?

Why it's important: Understanding the difference between correlation and covariance is essential for data analysis, as both are used to measure the relationship between two variables. While they share similarities, they differ in terms of scale, interpretation, and applicability, which is why knowing when to use each is important for accurate analysis.

Sample Answer:

Correlation and covariance both measure the relationship between two variables, but they differ in their scale and interpretation.

• Covariance: Covariance measures the degree to which two variables change together. It indicates whether the variables tend to increase together (positive covariance) or one increases while the other decreases (negative covariance). The magnitude of covariance is affected by the scale of the variables, which can make it difficult to compare across datasets.
  
  • Example: In finance, the covariance between the returns of two stocks might show whether they tend to rise and fall together.
• Correlation: Correlation is a standardised measure of the relationship between two variables, making it easier to interpret regardless of the scale. It normalises the covariance by dividing by the standard deviations of the variables, producing a value between -1 and 1, where 1 indicates a perfect positive relationship and -1 indicates a perfect negative relationship.
  
  • Example: The correlation between the height and weight of a group of people typically shows a positive value, indicating that as height increases, weight tends to increase as well.

While covariance is useful for understanding the direction of the relationship, correlation provides a clearer, standardised measure of the strength of the relationship.

9. How regularly must we update an algorithm in the field of machine learning?

Why it's important: Regularly updating machine learning algorithms is crucial for ensuring that they remain accurate and relevant as new data becomes available. The frequency of updates depends on the type of algorithm and the dynamics of the data, so understanding the update strategy is essential for maintaining model performance over time.

Sample Answer:

Machine learning algorithms need to be updated regularly to maintain their accuracy and effectiveness as new data emerges. The frequency of updates depends on several factors, including the nature of the data, the model’s sensitivity to changes, and the application’s requirements.

• Time-Sensitive Applications: In fields like stock market prediction, fraud detection, or real-time recommendation systems, algorithms need frequent updates to adapt to changing patterns in the data.
• Data Drift: Over time, the distribution of data might change, a phenomenon known as data drift. If not updated, the model’s predictions may become less reliable, leading to performance degradation.
• Model Retraining: In some cases, the model may need to be retrained periodically using new data to capture emerging trends. This is particularly true for supervised learning models.

To ensure optimal performance, it’s crucial to monitor the model's accuracy and implement a systematic update process, whether through periodic retraining, incremental learning, or real-time adaptation.

10. During analysis, how do you treat missing values?

Why it's important: Missing values are a common issue in data analysis, and how you handle them can significantly impact the quality of the analysis. Proper treatment of missing values ensures the robustness and accuracy of the model, preventing biases or distortions in the results.

Sample Answer:

Missing values are a common problem in data analysis, and how you handle them is critical to maintaining the quality of your results. There are several methods for dealing with missing data, depending on the nature of the dataset and the problem at hand.

• Removal: One of the simplest methods is to remove rows or columns with missing values. This works well if the missing data is minimal and doesn’t significantly impact the dataset. However, excessive removal can lead to a loss of valuable data.
• Imputation: Another approach is imputing the missing values. Common imputation techniques include replacing missing values with the mean, median, or mode of the column, or using more advanced methods like k-nearest neighbors (KNN) or multiple imputation. Imputation helps retain the integrity of the dataset while addressing missing values.
• Model-based Approaches: In some cases, machine learning models can be used to predict missing values based on the available data. This is useful when missing values are not random and can be inferred from other features.

The key is to understand the reasons behind the missing data and choose the method that aligns with the dataset's characteristics to avoid introducing bias into your analysis.

Next, let’s focus on some practical advice to ensure success in a data science interview.

Tips to Succeed in a Data Science Interview

Excelling in a data science interview requires more than just theoretical knowledge. By focusing on practical skills and a strong approach to problem-solving, you can significantly improve your chances of success. Below are five key tips to help you prepare effectively for your next data science interview:

1. Understand the Fundamentals Thoroughly

A strong grasp of basic concepts such as statistics, probability, and machine learning algorithms is essential when answering data science interview questions for freshers.

2. Work on Real-World Projects

Employers are always looking for candidates who can apply theoretical knowledge to real-world problems. By working on projects involving data cleaning, feature engineering, or building machine learning models, you’ll be able to discuss these projects confidently during interviews.

3. Practice Coding and SQL

A large portion of data science interviews involves coding challenges, particularly around data manipulation and analysis. Practicing coding problems and SQL queries on platforms like LeetCode or HackerRank will improve your skills and readiness for data science interview questions for freshers.

Ready to kickstart your career in Data Science? Book a consultation with Topmate, where our experts will guide you through mastering essential interview skills. Get personalised advice to help you stand out in your upcoming Data Science interviews!

‍

‍

4. Prepare to Discuss Your Analytical Thinking

Data science is not just about applying algorithms, but also about how you approach problems. Interviewers will often ask you to walk through your thought process when solving data-related challenges.

5. Stay Updated on Industry Trends

The data science field is rapidly evolving, with new tools, technologies, and methods emerging regularly. Staying updated on the latest advancements, like deep learning, artificial intelligence, and big data technologies, will show that you're committed to continuous learning.

Now that you’re prepared with these tips, let's take a look at some recruiters actively hiring for data science roles to help you take the next step in your career.

Recruiters Actively Hiring for Data Science Roles

In 2025, the data science job market continues to grow, and recruiters are actively seeking talented individuals to fill roles across various industries. For freshers eager to enter this dynamic field, connecting with industry professionals and recruiters is key to landing the right opportunity. Below are some of the Topmate resources on recruiters who are currently hiring for data science roles:

1. Babu Ebenezer Chinnathambi – Sr. Career Coach – Data Science/AI/ML at upGrad

Babu specializes in talent acquisition for Data Science, AI, and ML. He offers career coaching and international hiring opportunities for aspiring data scientists.

Connect with Babu Ebenezer Chinnathambi

2. Shubham Gupta – APJ Talent Discovery Partner – Data Science at SAP

Shubham focuses on hiring for data science and tech roles at SAP. He helps discover top talent for leading global companies.

Connect with Shubham Gupta

Connecting with these professionals will not only help you get noticed but also offer insights into the hiring process and what companies are looking for in a data science candidate. Take advantage of these opportunities to network and take the next step in your data science career!

Conclusion

The journey to becoming a successful data scientist starts with building a strong foundation in key concepts like statistics, programming, and machine learning. Continuous learning and hands-on experience are crucial for staying up-to-date with the rapidly evolving field. 

Preparing thoroughly for data science interview questions for freshers ensures that you are well-equipped to handle technical challenges and demonstrate your problem-solving abilities. By focusing on real-world projects and practicing coding and SQL, you can make a solid impression in interviews.

Looking to ace your Data Science interview in 2025? Topmate offers comprehensive support— from personalised career guidance and mock interview sessions with data industry experts to job referrals from top companies. Be it mentorship, salary negotiation tips, or expert feedback on your resume, Topmate has you covered

Contact us today and let us help you navigate your path to success in the Data Science field!