40+ Data Science Interview Questions and How to Answer Them!

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Data Science Interview Questions

Data Science is one of the world’s leading and most popular technologies today. Because of the increasing need and scarcity of these individuals, major organizations are paying top dollar for them. Data scientists are among the most highly compensated IT specialists. This data science interview preparation blog contains answers to the most frequently requested data science job interview questions. Here is a list of frequently asked data science interview questions.

Must Read: Data Science Courses in India: Eligibility, Fees, Scope

Top 10 Data Science Interview Questions 

Here are the top 10 data science interview questions that you can prepare for:

  1. What precisely is Data Science?
  2. Distinguish between data analytics and data science.
  3. What exactly is the distinction between supervised and unsupervised learning?
  4. Describe the stages involved in creating a decision tree.
  5. Separate univariate, bivariate, and multivariate analyses.
  6. How should a deployed model be maintained?
  7. What exactly is a Confusion Matrix?
  8. How is logistic regression carried out?
  9. What is the meaning of the p-value?
  10. Describe several sampling procedures.

Relevant Read: Syllabus of Data Science: For Beginners, IIT Subjects

Data Science Interview Questions: Basic and Advanced

Here is a collection of the most common data science interview questions on technical concepts that you should expect to confront, as well as how to construct your responses.

1. What are the distinctions between supervised learning and unsupervised learning?

Supervised LearningUnsupervised Learning
As input, known and labelled data is used.As input, unlabeled data is used.
There is a feedback mechanism in supervised learning. There is no feedback mechanism in unsupervised learning. 
Decision trees, logistic regression, and support vector machines are the most often used supervised learning algorithms.Unsupervised learning algorithms that are often employed include k-means clustering, hierarchical clustering, and the apriori algorithm.

2. How is Logistic Regression done?

Logistic regression estimates the probability of the association between the dependent variable (our label for what we want to predict) and one or more independent variables (our characteristics) by applying its underlying logistic function (sigmoid).

The diagram below shows how logistic regression works:

Image Source: Simplilearn

The sigmoid function formula and graph are as follows:

Image Source: Simplilearn

3. Describe the stages involved in creating a decision tree.

  • Consider the complete data set as input.
  • Determine the entropy of the target variable and the predictor qualities.
  • Calculate your overall information gain (we obtain information by categorizing distinct objects from one another).
  • As the root node, select the property with the biggest information benefit. 
  • Repeat this technique for each branch until the decision node for each branch is reached.

4. How does one go about creating a random forest model?

A random forest is made up of several decision trees. If you divide the data into multiple packages and create a decision tree for each set of data, the random forest will bring all of those trees together.

  • Steps for creating a random forest model:
  • Choose ‘k’ features at random from a total of features, where k << m
  • Calculate node D using the optimal split point among the ‘K’ characteristics.
  • Using the best split, divide the node into daughter nodes.
  • Repeat steps 2 and 3 until all leaf nodes are complete. 
  • build a forest by repeating steps one through four ‘n’ times to build ‘n’ trees. 

5. How can you keep your model from being overfitting?

Overfitting is defined as a model that is only trained on a small quantity of data and ignores the larger picture. To avoid overfitting, there are three basic approaches:

  • Keep the model simple—consider fewer variables, removing some of the noise in the training data.
  • Cross-validation techniques such as k folds cross-validation should be used. 
  • Use regularisation techniques like LASSO to penalize model parameters that are likely to cause overfitting.

Relevant Read: SOP for MS in Data Science Samples, Tips and Format

Interview Questions for Freshers

1. Describe Data Science.

Data Science is an interdisciplinary field that consists of numerous scientific procedures, algorithms, tools, and machine-learning approaches that strive to help uncover common patterns and extract meaningful insights from provided raw input data through statistical and mathematical analysis.

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

  • Data science is the task of converting data via the use of various technical analysis methods in order to derive useful insights that data analysts may apply to their business scenarios.
  • Data analytics is concerned with testing current hypotheses and facts and providing answers to inquiries in order to make better and more successful business decisions.
  • Data Science drives innovation by addressing questions that lead to new connections and solutions to future challenges. Data analytics is concerned with extracting current meaning from existing historical context, whereas data science is concerned with predictive modeling.
  • Data Science is a broad subject that uses various mathematical and scientific tools and algorithms to solve complex problems, whereas data analytics is a specific field that deals with specific concentrated problems using fewer statistical and visualization tools.

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

Data analysis cannot be performed on a big volume of data at once, especially when dealing with enormous datasets. It is critical to collect some data samples that may be used to represent the entire population and then analyze them. While doing so, it is critical to carefully select sample data from the massive dataset that properly represents the complete dataset.

Based on the use of statistics, there are primarily two types of sampling techniques:

  • Clustered sampling, simple random sampling, and stratified sampling are all probability sampling approaches.
  • Techniques for non-probability sampling include quota sampling, convenience sampling, snowball sampling, and others.

4. What does it signify when the p-values are both large and small?

A p-value is a measure of the likelihood of obtaining outcomes that are equal to or greater than those obtained under a certain hypothesis, assuming that the null hypothesis is correct. This shows the likelihood that the observed discrepancy occurred by coincidence.

  • A p-value of less than 0.05 indicates that the null hypothesis can be rejected and that the data is unlikely to be true or null.
  • A high p-value, i.e. the value of 0.05, suggests that the null hypothesis is strong. It denotes that the data is true and null.
  • A p-value of 0.05 indicates that the hypothesis is open to interpretation.

5. When is resampling used?

Resampling is a sampling technique used to improve accuracy and quantify the uncertainty of population parameters. It is done to ensure that the model is good enough by training it on different patterns in a dataset to guarantee that variances are handled. It is also done when models need to be validated using random subsets or when labeling data points when doing tests.

6. What do you mean by Imbalanced Data?

When data is spread unequally across several categories, it is said to be highly unbalanced. These datasets cause an error in model performance and inaccuracy.

7. Are there any distinctions between the expected and mean values?

There aren’t many distinctions between these two, but it’s worth noting that they’re employed in various settings. In general, the mean value relates to the probability distribution, but the anticipated value is used in contexts containing random variables.

8. What is meant by Survivorship Bias?

This bias refers to a logical fallacy that occurs while focusing on components that survived a procedure and ignoring others that did not function due to a lack of prominence. This prejudice can lead to incorrect findings.

9. Define the terms KPIs, lift, model fitting, robustness, and DOE.

  • KPI: KPI stands for Key Performance Indicator, and it monitors how successfully a company fulfills its goals.
  • Lift is a measure of the target model’s performance when compared to a random choice model. Lift represents how well the model predicts compared to the absence of a model.
  • Model fitting: How well the model under examination fits the provided observations.
  • Robustness: This shows the system’s ability to properly handle differences and variances.
  • DOE is an abbreviation for the design of experiments, which is the task design that aims to describe and explain information variance under hypothesized conditions to reflect factors.

10. Define confounding variables

Confounders are another term for confounding variables. These variables are a form of extraneous variable that influences both independent and dependent variables, resulting in false association and mathematical correlations between variables that are correlated but not casually related to one another.

11. What is the definition and explanation of selection bias?

When a researcher must choose which person to study, he or she is subject to selection bias. Selection bias is connected with studies in which the participant selection is not random. The selection effect is another name for selection bias. The manner of sample collection contributes to the selection bias.

The following are four types of selection bias:

  • Sampling Bias: Due to a non-random population, some individuals of a population have fewer chances of being included than others, resulting in a biased sample. As a result, a systematic inaccuracy known as sampling bias occurs.
  • Time interval: Trials may be terminated early if any extreme value is reached, but if all variables are invariant, the variables with the highest variance have a greater probability of obtaining the extreme value.
  • Data: It occurs when specific data is arbitrarily chosen and the generally agreed-upon criteria are not followed.
  • Attrition: In this context, attrition refers to the loss of participants. It is the exclusion of subjects who did not complete the study.

12. What is logistic regression? Give an example of a time when you employed logistic regression.

The logit model is another name for logistic regression. It is a method for predicting a binary outcome given a linear combination of variables (referred to as the predictor variables). 

Assume we want to forecast the outcome of an election for a specific political leader. So we want to know whether or not this leader will win the election. As a result, the outcome is binary, i.e. win (1) or defeat (0). However, the input is a combination of linear variables such as advertising budget, previous work done by the leader and the party, and so on. 

13. What exactly is Linear Regression? What are the key disadvantages of the linear model?

Linear regression is a technique that predicts the value of a variable Y based on the value of a predictor variable X. Y is known as the criterion variable. The following are some of the disadvantages of Linear Regression:

  • The assumption of error linearity is a significant disadvantage.
  • It cannot be used to produce binary results. For that, we have Logistic Regression.
  • There are overfitting issues that cannot be resolved.

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

Deep learning is a machine learning paradigm. Multiple layers of processing are used in deep learning to extract high-value characteristics from data. Neural networks are built in such a way that they attempt to mimic the human brain. 

Deep learning has demonstrated extraordinary performance in recent years due to its strong parallel with the human brain.

The distinction between machine learning and deep learning is that deep learning is a paradigm or subset of machine learning inspired by the structure and operations of the human brain, known as artificial neural networks.

15. What are Gradient and Gradient Descent?

Gradient: A gradient is a property that measures how much the output has changed in response to a little change in the input. In other words, it is a measure of the change in weights in relation to the change in error. The gradient can be expressed mathematically as the slope of a function.

Gradient Descent: Gradient descent is a minimization algorithm that reduces the Activation function to its simplest form. It can minimize any function that is supplied to it, however, it is usually simply given the activation function. 

Gradient descent, as the name implies, refers to a reduction or descent in something. The analogy of gradient descent is frequently used to describe a person climbing down a hill/mountain. The following equation explains what gradient descent means:

So if a person is ascending down a slope, the next position to be reached is denoted by “b” in this equation. Then there’s a minus sign to represent minimization (since gradient descent is a minimization procedure). The Gamma term is referred to as a waiting factor, and the remaining word, which is the Gradient term, indicates the direction of the steepest decline. 

Relevant Read: MBA in Data Science – Popular Courses & Top Universities

List of Interview Questions for Experienced Data Scientists

  1. How are the time series problems different from other regression problems?
  2. What are RMSE and MSE in a linear regression model?
  3. What are Support Vectors in SVM (Support Vector Machine)?
  4. So, you have done some projects in machine learning and data science and we see you are a bit experienced in the field. Let’s say your laptop’s RAM is only 4GB and you want to train your model on 10 GB data set.

What will you do? Have you experienced such an issue before?

  1. Explain Neural Network Fundamentals.
  2. What is Generative Adversarial Network?
  3. What is a computational graph?
  4. What are auto-encoders?
  5. What are Exploding Gradients and Vanishing Gradients?
  6.  What is the p-value and what does it indicate in the Null Hypothesis?
  7.  Since you have experience in the deep learning field, can you tell us why TensorFlow is the most preferred library in deep learning?
  8.  Suppose there is a dataset having variables with missing values of more than 30%, how will you deal with such a dataset?
  9.  What is cross-validation?
  10.  What are the differences between correlation and covariance?
  11.  How do you approach solving any data analytics-based project?

Relevant Read: Ph.D. in Data Science – All You Need To Know

How to Prepare for a Data Science Interview?

There are four to five stages to the data science interviews. You will be asked questions on statistical and machine learning, coding (Python, R, SQL), behavioral, product knowledge, and occasionally leadership questions. 

You can prepare for all levels by doing the following:

  1. Investigating the firm and job responsibilities will assist you in prioritizing your efforts in a certain discipline of data science. 
  2. Examining previous portfolio projects: The hiring manager will evaluate your abilities by asking questions about your work. 
  3. Revisiting the principles of data science: probability, statistics, hypothesis testing, descriptive and Bayesian statistics, and dimensionality reduction. Cheat sheets are the most effective approach to learning the fundamentals quickly. 
  4. Coding practice includes taking assessment tests, doing online code challenges, and reviewing frequently asked coding questions. 
  5. Working on end-to-end projects allows you to sharpen your skills in data cleansing, manipulation, analysis, and visualization. 
  6. Reading the most often asked interview questions: product knowledge, statistical, analytical, behavioral, and leadership questions. 
  7. Mock interviews allow you to practice an interview with a friend, strengthen your statical vocabulary, and gain confidence. 

Must Read: Self Introduction For Data Scientists


Q.1. How do I prepare for a data science interview?

Ans: Here are a few examples:
-Refreshing my knowledge in programming languages(Python, R, and others).
-Examining Data Structures and Algorithms.
-Experimenting with Data Manipulation and Analysis Techniques.
-Discovering Common Machine Learning Models and Techniques.
-Understanding the Industry and the Company with which you are interviewing.

Q.2. What are the 4 pillars of data science?

Ans: Domain knowledge, math and statistics abilities, computer science, communication, and visualization are the four pillars of data science. Each is necessary for any data scientist’s success. Understanding the data, what it means, and how to use it requires domain expertise.

Q.3. What are the 3 main concepts of data science?

Ans: Statistics, Visualization, Deep Learning, and Machine Learning are important Data Science concepts.

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