Top 10+ Data Science Interview Questions and Answers

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  • June 15, 2024
  • Data Science
Top 20+ Data Science Interview Question and Answers

Top 10+ Data Science Interview Questions and Answers

Data science Course in Pune blends math and statistics, specialized programming, sophisticated analytics, Artificial Intelligence (AI), and Machine Learning with expertise in the area to reveal significant findings in an organization’s data. Making decisions and developing strategies can be aided by these insights. The increasing abundance of data sources, and hence data, has made data science one of the fastest-growing fields in all industries. Consequently, it’s no wonder that Harvard Business Review named data science the “sexiest job of the 21st century”. Organizations are increasingly relying on them to understand data and make meaningful recommendations to improve business performance. Discover the top 10+ data science interview questions and answers to help you ace your next job interview and land your dream data science role.


Enroll in SevenMentor to have the best outcome by attending our Data Science Training in Pune. Refer to the Interview section of our blog to enrich your knowledge to perform your Data science Interviews.


Most Asked Technical Interview Questions on Data Science

1. Why do we perform A/B testing? 

A/B testing is a statistical hypothesis test for randomized trials with two variables (A and B). It is mostly used in user experience research, in which two alternative versions of a product are compared based on user feedback. In data science, it is used to test various machine learning models in the creation and analysis of data-driven solutions within a corporation.


2. How can you prevent your model from becoming overfitted? 

Overfitting happens when your model performs well on the training and validation datasets but fails on the unknown test dataset. 

To circumvent this, we can: 

  • Maintaining a simple model 
  • Avoid training for extended Epocs. 
  • Designing features Employing cross-validation techniques 
  • Use regularization techniques. 
  • Shap-based model evaluation


3. What are eigenvectors and eigenvalues, respectively? 

Eigenvectors are column vectors or unit vectors with length and magnitude equal to one. They’re also known as right vectors. Eigenvalues are coefficients applied to eigenvectors to give them variable length or magnitude values. Eigendecomposition refers to the process of breaking down a matrix into Eigenvectors and Eigenvalues. These are later employed in machine learning approaches such as PCA (Principal Component Analysis) to extract significant insights from a given matrix.


4. What exactly is logistic regression? Provide an example of how you recently used logistic regression. 

Logistic Regression is often referred to as the Logit model. It is a method for predicting a binary outcome using a linear combination of variables (known as the predictor variables). For example, suppose we want to predict the outcome of an election for a particular political leader. So, we want to know if this leader will win the election. As a result, the outcome is binary, i.e. either win (1) or lose (0). However, the input is a combination of linear variables such as advertising spending, previous work by the leader and the party, and so on.


5. The probability of seeing a shooting star or a group of them over 15 minutes is 0.2. What is the likelihood of witnessing at least one-star shoot from the sky if you are under it for an hour? 

Let Prob be the likelihood that we will witness at least one shooting star in 15 minutes. So, Prob = 0.2. The probability that we won’t see any shooting stars within 15 minutes is now = 1-Prob 1-0.2 = 0.8. How likely of not seeing any shooting stars during an hour is: = (1-Prob)(1-Prob)(1-Prob)*(1-Prob) = 0.8 * 0.8 * 0.8 = (0.8)⁴   ≈ 0.40 

So, there is a 60 percent possibility that we will witness a shooting star in an hour.


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

The thin lines in the diagram above represent the distance between the classifier and the nearest data points. These are commonly referred to as support vectors. As a result, we can define the support vectors as the data points or vectors closest to the hyperplane. They have an impact on the hyperplane’s position. Support vectors are named for the fact that they support the hyperplane.


7. What does a computational graph mean?

The term “Dataflow Graph” is another name for a computational graph. The popular deep learning program TensorFlow is built on the computational graph. The computation graph of Tensorflow is made up of a network of nodes, each of which has a specific function. In this network, the edges stand in for tensor values, and the nodes for actions.


8.  What exactly are auto-encoders? 

Auto-encoders serve as learning networks. They turn inputs into outputs with the fewest possible defects. This means that the desired output should be almost equal to or as close to the input as possible. Multiple layers are inserted between the input and output layers, each one smaller than the one before it.


9. What is a p-value and how does it relate to the Null Hypothesis? 

The P-value is a number that ranges from 0 to 1. The p-value in a hypothesis test in statistics indicates how strong the results are. The null hypothesis is the claim that remains open to research or trial. A low p-value (p-value less than or equal to 0.05) indicates that the data strongly contradicts the Null Hypothesis, meaning that it can be rejected. A high p-value, defined as a p-value greater than 0.05, indicates that the data supports the Null Hypothesis, meaning that it can be accepted.


10. What function does selection bias serve? 

Selection bias arises when a subset of the sample is chosen for study without using randomization. This bias shows that the sample being examined does not represent the whole population to be studied. For example, the figure below shows that the sample we selected does not fully represent our population. This allows us to determine whether we chose the correct data analysis.


11. How do you create a ROC curve and what does it entail?

The comparison of true and false positive rates at different thresholds is shown by the Receiver Operating Characteristic (ROC) curve. We may compare specificity and sensitivity using it to see how effectively the model can distinguish between classes. True positive rates, or the percentage of positive observations that were accurately predicted to be positive out of all positive observations, are plotted against false positive rates to determine the true positive rate. On the other hand, false-positive rates represent the percentage of negative observations that were mistakenly predicted to be positive out of all negative observations.


12. Will converting categorical variables to continuous variables result in a better predictive model? 

Yes! A categorical variable can be classified into two or more categories but has no particular category ordering. Ordinal variables are comparable to category variables in that they are ordered properly and clearly. So, if the variable is ordinal, interpreting the category value as a continuous variable yields stronger predictive models.


13. How frequently should a machine learning algorithm be updated? 

We do not update ML algorithms regularly because it disturb the well-defined phases of problem-solving and produces issues in systems that currently use the method. We only modify the algorithm in the following scenarios: If we discover that the algorithm is inefficient in solving the problem and performs poorly. In that situation, we’d replace it with a more effective algorithm. In the situation of non-stationarity. If the underlying data structure alters. If we need the model to update as fresh data is received.


14. Why is TensorFlow the most widely used deep learning library? 

TensorFlow is the most popular deep learning library since it compiles faster than other libraries such as Keras and PyTorch. It supports both GPU and CPU computing devices and has C++ and Python APIs, making it easier to work with than the other libraries.


15. Give an example in which false positives and false negatives are equally important. 

In banking, lending loans are the primary source of income for banks. However, if the repayment rate is not favorable, there is a possibility of significant losses rather than gains. Giving out loans to clients is a gamble since banks cannot afford to lose good customers while also being unable to gain poor customers. This is a famous situation of equal importance in false positive and false negative scenarios.


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