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Table of contents
1.
Introduction
2.
Data Science Interview Questions for Freshers
2.1.
Question 1:What is Data Science?
2.2.
Question 2: What is the difference between Data Analytics and Data Science?
2.3.
Question 3: What are the differences between supervised and unsupervised learning?
2.4.
Question 4: What is Machine learning?
2.5.
Question 5: During sampling, what types of biases can occur?
2.6.
Question 6: What does Linear Regression mean?
2.7.
Question 7: What are the drawbacks of a linear model?
2.8.
Question 8: What do you understand by the term Logistic regression?
2.9.
Question 9: What are the sampling techniques?
2.10.
Question 10: When does Bias occur?
3.
Data Science Interview Questions for Intermediate
3.1.
Question 1:How are skewed and uniform distributions different from each other?
3.2.
Question 2: What do you mean by cluster sampling technique?
3.3.
Question 3: What do you mean by a decision tree?
3.4.
Question 4: What do you mean by Eigenvector?
3.5.
Question 5: What is Back Propagation?
3.6.
Question 6: What do you mean by Boltzmann Machine
3.7.
Question 7: What do you mean by a random forest model?
3.8.
Question 8: Write the python libraries used for data analysis and scientific computations.
3.9.
Question 9: What are the techniques used to overcome overfitting?
3.10.
Question 10: What do you understand by covariance?
4.
Data Science Interview Questions for Experienced
4.1.
Question 1: What do you understand about the terms RMSE and MSE?
4.2.
Question 2: What do you know about the Activation function?
4.3.
Question 3: What do you understand by a kernel trick?
4.4.
Question 4: What is the Computational Graph?
4.5.
Question 5: What do you understand by validation sets?
4.6.
Question 6: Mention the various steps involved in an analytics project.
4.7.
Question 7: What do you mean by cluster sampling and systematic sampling?
4.8.
Question 8: What is the CART algorithm for decision trees?
4.9.
Question 9: Enlist the steps to handle missing values in data.
4.10.
Question 10: What is the difference between univariate, bivariate, and multivariate analyses?
5.
Frequently Asked Questions
5.1.
How do I prepare for a data scientist interview?
5.2.
Is coding asked in data science interview?
5.3.
Is data scientist hard?
6.
Conclusion 
Last Updated: Jun 13, 2024
Easy

Top Data Scientist Interview Questions and Answers in 2023

Author Monika Yadav
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Crack Google SDE interview : Essential projects
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20 Jun, 2024 @ 01:30 PM

Introduction

Are you looking for a Data scientist job? If yes, then what are you waiting for? Here are the Data scientist interview questions. As you know, the final stage of every job is an Interview. Tech companies offer high-paying jobs for data scientist roles, as we are aware of the fact that the Demand for data scientists is increasing day by day. 

data scientist interview questions

Moreover, there is a prediction that by 2025 the sector of data science in India will grow by 16 billion dollars. So there are plenty of job opportunities for those who are interested in pursuing a Data scientist career. Let’s start with Data scientist interview questions. Moving forward, let’s discuss easy-level data scientist interview questions.

Data Science Interview Questions for Freshers

Question 1:What is Data Science?

Answer: Data science is the field of study that uses various techniques, processes and algorithms to extract useful information from structured or unstructured data. These techniques and tools includes statistics, Artificial IntelligenceMachine Learning, and so on. The extracted information is further used across various applications, businesses, industries, and so on.

Question 2: What is the difference between Data Analytics and Data Science?

Answer:

Data Analytics

Data Science

Data Analytics has the goal of displaying the precise data of retrieved insights.Data Science discovers meaningful insights from massive datasets and extracts the finest solutions to resolve the problem.
Data Analysts collect, store, handle, analyze maintain the results.Data Scientists use different algorithms and machine learning principles to improve the ways in which the data support the goals.

Question 3: What are the differences between supervised and unsupervised learning?

Answer:

Supervised Learning

Unsupervised Learning

It is used for labeled data.It is used for unlabelled data.
It predicts the output.It finds the hidden patterns in the data.
It takes direct feedback to check if the output is correct or not.It does not take into account any such feedback.
It is categorized into Classification and Regression problems.It is categorized into Association and Clustering problems.

Question 4: What is Machine learning?

Answer: Machine Learning is a subfield of  Data science that allows our computers to learn from earlier experiences and predict the output without being explicitly programmed.

Question 5: During sampling, what types of biases can occur?

Answer: In the process of sampling, there are three types of biases. These are as follows.

  • Selection Bias: Selection bias occurs whenever the selected sample is not representative of the population.
     
  • Under coverage Bias: Under coverage bias is helpful whenever subpopulations are underrepresented in the sample.
     
  • Survivorship Bias: Survivorship bias occurs whenever the successful members of the population are considered in the study.

Question 6: What does Linear Regression mean?

Answer: Linear regression can be defined as a statistical technique that allows us to understand the relationship between dependent and independent variables. In linear regression, the relationship between two variables is linear which means relationship is expressed as a straight line. 

Question 7: What are the drawbacks of a linear model?

Answer: Linear model drawbacks are given below:

  • It can not be used for binary and count outcomes.
     
  • In the linear model, there is an overfitting problem. Overfitting is nothing but when the model ignores the bigger picture and only sets for a very small amount of data.

Question 8: What do you understand by the term Logistic regression?

Answer: Logistic regression can be defined as a statistical technique that allows us to understand the relationship between binary dependent and independent variables. It uses logistic function to model a relation between the binary variables. Logistic regression is also known as the logit model.

Question 9: What are the sampling techniques?

Answer: There are two types of sampling techniques:

Probability Sampling Techniques: The probability sampling technique is helpful in research studies. It ensures that the sample must be representative of the population.

Non-Probability Sampling Techniques: The non-probability sampling technique is helpful when obtaining the list of members of the population is tough or maybe impossible. 

Question 10: When does Bias occur?

Answer: Bias can occur during data processing, developing, and deploying models. It can also occur during model evaluation.

Moving forward, let’s discuss medium-level Data Scientist interview questions.

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Data Science Interview Questions for Intermediate

Question 1:How are skewed and uniform distributions different from each other?

Answer: When the data is neither symmetric nor normal and distributed on one side of the plot, then a skewed distribution occurs. On the other hand, uniform distribution occurs when the data is equally distributed.

Question 2: What do you mean by cluster sampling technique?

Answer: Cluster sampling is also known as Multi-stage sampling. It is used to study large, spread-out populations. This technique divides the population into clusters(groups). Then, any random cluster is picked and considered for the study. After this, the individuals from clusters are then included in the sample.

Question 3: What do you mean by a decision tree?

Answer: In Data science, Decision trees are non-parametric supervised algorithms. It is utilized for classification as well as regression. Decision trees consist of root nodes, leaf nodes, and branches. It has a hierarchical tree structure.

Question 4: What do you mean by Eigenvector?

Answer: To understand linear transformation, we can use eigenvectors. For the covariance matrix, we calculate the eigenvectors. Eigenvectors are unit vectors. Unit vectors means that their magnitudes and length is equal to 1.

Question 5: What is Back Propagation?

Answer: Backpropagation or backward propagation is a generally used algorithm for training feedforward artificial neural networks. For improving the accuracy of predictions in data mining, Back propagation plays an important role as a mathematical tool.

Question 6: What do you mean by Boltzmann Machine

Answer: The purpose of the Boltzmann Machine is to advance the solution of a problem. It is a simple learning algorithm. The Boltzmann machine algorithm allows us to optimize the weight and quantity related to that particular problem.

Question 7: What do you mean by a random forest model?

Answer: Random forest model is an algorithm that belongs to the supervised learning technique. This can be used for regression as well as for classification problems. Random forest is a classifier that contains many decision trees on several subsets of the dataset and takes the average to enhance the predictive accuracy of the dataset.

Question 8: Write the python libraries used for data analysis and scientific computations.

Answer: Libraries in python used for data analysis and scientific computations are:

  • SciPy
     
  • Pandas
     
  • Matplotlib
     
  • NumPy
     
  • SciKit
     
  • Seaborn

Question 9: What are the techniques used to overcome overfitting?

Answer: We can overcome overfitting by using many techniques. Some of them are given below:

  • By reducing the complexity of the model.
     
  • Using Regularization in a model.
     
  • Early Stopping.
     
  • Creating more data samples from the existing data.
     
  • Dropouts.

Question 10: What do you understand by covariance?

Answer: Covariance is a technique that helps us to know the systematic relationship between pairs of variables. Covariance is very helpful to understand the relationship between continuous variables. With the help of Covariance, we get to know the direction of variables. For example, if there is positive covariance between two variables, then they increase or decrease together. Whereas in the case of negative covariance, the variables move in the opposite direction.


Moving forward, let’s discuss hard-level Data Scientist interview questions.

Check out IBM Interview Experience to learn about their hiring process.

Data Science Interview Questions for Experienced

Question 1: What do you understand about the terms RMSE and MSE?

Answer: RMSE: RMSE stands for Root Mean Square Error. RMSE measures the average magnitude of the errors and is concerned with the deviations from the actual value.

MSE: MSE stands for Mean Squared Error. MSE measures how close a regression line is to a set of data points. 

Question 2: What do you know about the Activation function?

Answer: The activation function checks whether a neuron should be activated or not. This is done by calculating the weighted sum and adding Bias. The activation function helps in introducing nonlinearity into the output of a neuron. It is done to help the learning process when it comes to complex functions.

Question 3: What do you understand by a kernel trick?

Answer: The kernel trick is a method used to solve a nonlinear problem using a linear classifier. It transforms the inseparable linear data into separable data in higher dimensions. The kernel function helps in calculating the similarity between two data points. The correct kernel function can be choose on the basis of problem and characteristics of data.

Question 4: What is the Computational Graph?

Answer: Computational Graph is also known as Data Flow Graph. It is a graphical representation of TensorFlow. It is a vast network of different kinds of nodes where each node represents a specific mathematical operation. Tensors are nothing but the edges of nodes. That is why a computational graph is called a TensorFlow of inputs.

Question 5: What do you understand by validation sets?

Answer: A validation set can be defined as a portion of a dataset. It helps in evaluating the trained model’s performance. In machine learning, the original dataset is split into a training set and a validation set. The training and validation sets are helpful in training a model and evaluating the performance of that model, respectively. The validation set is used to select the parameters for avoiding model overfitting. 

Question 6: Mention the various steps involved in an analytics project.

Answer: The various steps involved in an analytics project are as follows:

  • Recognize the business issues.
     
  • Search for the data and understand it.
     
  • Prepare the data for modeling.
     
  • Run the model and analyze the result.
     
  • Using a new dataset, Verify the model.
     
  • Lastly, Implement the model and track the outcome. 

Question 7: What do you mean by cluster sampling and systematic sampling?

Answer: A cluster sample is obtained by dividing the total population under observation into sections or clusters, then randomly selecting one or more of the clusters and using all of its members as the members of the sample. This is usually used when the population is large or there is a large geographic area. In contrast, Systematic sampling is where a researcher assigns a counting number to every member of the population, selects a random number, and selects members for the sample at regular intervals from the starting random number that was selected. For example, let’s say you wanted to know how much time people living in a singles-only apartment complex spent watching Netflix on a weekly basis.

Question 8: What is the CART algorithm for decision trees?

Answer: The CART algorithm for the decision tree works via the following process:

  • Obtain the best-split point of each input.
     
  • Identified the new best-split point. 
     
  • According to the best-split point, Split the chosen input.
     
  • Continue splitting until no further splitting is available.

Question 9: Enlist the steps to handle missing values in data.

Answer: There are many ways to handle the missing values in data:

  • Dropping the values.
     
  • Delete the observation.
     
  • Replace the value with the observation's mean, median, and mode.
     
  • Predict value with regression.
     
  • Find appropriate values with clustering.
     
  • With clustering, find the proper value.

Question 10: What is the difference between univariate, bivariate, and multivariate analyses?

Answer: When only one variable is analyzed through graphs, that analysis is known as univariate. On the other hand, when two variables are being compared using scatter plots, that analysis is known as bivariate. And, when more than two variables are analyzed to understand the correlations between them then it is called as multivariate.

You can also check out Data Analyst vs Data Scientist here.

Frequently Asked Questions

How do I prepare for a data scientist interview?

Prepare by studying statistics, machine learning, and coding. Practice problem-solving and be ready to discuss past projects, algorithms, and data analysis techniques.

Is coding asked in data science interview?

Yes, coding is often a part of data science interviews, assessing your ability to implement algorithms, analyze data, and solve problems using programming languages like Python or R.

Is data scientist hard?

Data science can be challenging due to its multidisciplinary nature, involving statistics, programming, and domain knowledge. However, with dedication and continuous learning, it becomes manageable.

Conclusion 

Dealing with many questions, we came to the end of the article. These questions help you to get aware of basic knowledge of data science. So far, we have discussed easy, medium, and hard-level data science interview questions. 

We hope this article helped you in learning Data scientist interview questions. You can read more such articles on our platform, Coding Ninjas Studio. You will find articles on almost every topic on our platform. Also, you can practice coding questions at Coding Ninjas to crack good product-based companies. 

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