100+ Data Science Interview Questions and Answers for 2024

Data Science interview questions and answers for 2024 on topics ranging from probability, statistics, data science – to help crack data science job interviews.

100+ Data Science Interview Questions and Answers for 2024
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Hone yourself to be the ideal candidate at your next data scientist job interview with these frequently asked data science interview questions. Data Scientist interview questions asked at a job interview can fall into one of the following categories -

  • Data Science Technical Interview Questions based on data science programming languages like Python, R, etc.

  • Data Science Technical Interview Questions based on statistics, probability, math, machine learning, etc.

  • Practical experience or Role-based data scientist interview questions based on the projects you have worked on and how they turned out.

ProjectPro Free Projects on Big Data and Data Science

Apart from interview questions, we have also put together a collection of 100+ ready-to-use Data Science solved code examples. Each code example solves a specific use case for your project. These can be of great help in answering interview questions and also a handy guide when working on data science projects.

Data Science Interview Questions & Answers

In collaboration with data scientists, industry experts, and top counsellors, we have put together a list of general data science interview questions and answers to help you prepare for applying for data science jobs. This first part of a series of data science interview questions and answers articles focuses only on common topics like data, probability, statistics, and other data science concepts. This blog also includes a list of open-ended questions that interviewers ask to get a rough idea of how often and quickly you can think on your feet. Some data analyst interview questions in this blog can also be asked in a data science interview. These kinds of analytics interview questions are asked to measure if you were successful in applying data science techniques to real-life problems

Data Science Interview Questions and Answers

Data Science is not an easy field to get into. This is something all data scientists will agree on. Apart from having a degree in mathematics/statistics or engineering, a data scientist also needs to go through intense training to develop all the skills required for this field. Apart from the degree/diploma and the training, it is important to prepare the right resume for a data science job and to be well versed with the data science interview questions and answers.

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Data Science Interview Preparation

Consider our top 100 Data Science Interview Questions and Answers as a starting point for your data scientist interview preparation. Even if you are not looking for a data scientist position now, as you are still working your way through hands-on projects and learning programming languages like Python and R – you can start practicing these Data Scientist Interview questions and answers. These Data Scientist job interview questions will set the foundation for data science interviews to impress potential employers by knowing about your subject and being able to show the practical implications of data science.

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Top 100 Common Data Scientist Interview Questions and Answers

Common Data Science Interview Questions

1. What is Machine Learning?

Machine Learning comprises two words-machine and learning, which hint towards its definition - a subdomain in computer science that deals with the application of mathematical algorithms to identify the trend or pattern in a dataset.

The simplest example is the usage of linear regression (y=mt+c) to predict the output of a variable y as a function of time. The machine learning model learns the trends in the dataset by fitting the equation on the dataset and evaluating the best set of values for m and c. One can then use these equations to predict future values.

Access 100+ ready-to-use, sample Python and R codes for data science to prepare for your Data Science Interview

2. Quickly differentiate between Machine Learning, Data Science, and AI.

 

Machine Learning

Data Science

Artificial Intelligence

Basic Meaning

A branch of Artificial Intelligence that deals with the usage of simple statistics-inspired algorithms to identify patterns in the dataset.

Data Science refers to the art of using machine learning and deep learning techniques over large data to predict certain outcomes.

A term that broadly covers the applications of computer science spanning Robotics, Text Analysis, etc.

3. Out of Python and R, which is your preference for performing text analysis?

Python is likely to be everyone’s choice for text analysis as it has libraries like Natural Language Toolkit (NLTK), Gensim. CoreNLP, SpaCy, TextBlob, etc. are useful for text analysis.

4. What are Recommender Systems?

Understanding consumer behavior is often the primary goal of many businesses. For example, consider the case of Amazon. If a user searches for a product category on its website, the major challenge for Amazon’s backend algorithms is to come up with suggestions that are likely to motivate the users to make a purchase. And such algorithms are the heart of recommendation systems or recommender systems. These systems aim at analyzing customer behavior and evaluating their fondness for different products. Apart from Amazon, recommender systems are also used by Netflix, Youtube, Flipkart, etc.

5. Why data cleaning plays a vital role in the analysis?

(Access popular Python and R Codes for data cleaning )It is cumbersome to clean data from multiple sources to transform it into a format that data analysts or scientists can work with. As the number of data sources increases, the time it takes to clean the data increases exponentially due to the number of sources and the volume of data generated in these sources. It might take up to 80% of the time for cleaning data, thus making it a critical part of the analysis task.

6. Define Collaborative filtering.

The process of filtering is used by most recommender systems to identify patterns or information by collaborating viewpoints, various data sources, and multiple agents.

7. What is an Eigenvalue and Eigenvector?

Eigenvectors are used for understanding linear transformations. They are the directions along which a particular linear transformation acts by flipping, compressing, or stretching. Eigenvalues can be referred to as the strength of the transformation in the direction of the eigenvector or the factor by which the compression occurs. We usually calculate the eigenvectors for a correlation or covariance matrix in data analysis.

Ace Your Next Job Interview with Mock Interviews from Experts to Improve Your Skills and Boost Confidence!

Data Science Interview Preparation

8. What is Gradient Descent?

Gradient descent is an iterative procedure that minimizes the cost function parametrized by model parameters. It is an optimization method based on convex function and trims the parameters iteratively to help the given function attain its local minimum. Gradient measures the change in parameter with respect to the change in error. Imagine a blindfolded person on top of a hill and wanting to reach the lower altitude. The simple technique he can use is to feel the ground in every direction and take a step in the direction where the ground is descending faster. Here we need the help of the learning rate which says the size of the step we take to reach the minimum. The learning rate should be chosen so that it should not be too high or too low. When the selected learning rate is too high, it tends to bounce back and forth between the convex function of the gradient descent, and when it is too low, we will reach the minimum very slowly.

9. Differentiate between a multi-label classification problem and a multi-class classification problem.

Multi-label Classification

Multi-Class Classification

A classification problem where each target variable in the dataset can be labeled with more than one class.


For Example, a news article can be labeled with more than two topics, say, sports and fashion.

A classification problem where each target variable in the dataset can be assigned only one class out of two or more than two classes.


For Example, the task of classifying fruits images where each image contains only one fruit.

 

10. What are the various steps involved in an analytics project?

  • Understand the business problem and convert it into a data analytics problem.

  • Use exploratory data analysis techniques to understand the given dataset.

  • With the help of feature selection and feature engineering methods, prepare the training and testing dataset.

  • Explore machine learning/deep learning algorithms and use one to build a training model.

  • Feed training dataset to the model and improve the model’s performance by analyzing various statistical parameters.

  • Test the performance of the model using the testing dataset.

  • Deploy the model, if needed, and monitor the model performance.

11. What is the difference between feature selection and feature engineering methods?

 

Feature Selection

Feature Engineering

Feature selection methods are the methods that are used to obtain a subset of variables from the dataset that are required to build a model that best fits the trends in the dataset.

Feature Engineering methods are the methods that are used to create new features from the given dataset using the existing variables. These methods allow to better fit complicated trends in the dataset.

Example: Intrinsic Methods(Rule and tree-based algorithms, MARS Models, etc.), Filter Methods, Wrapper Methods(Recursive Feature Elimination, Genetic Algorithms, etc.) 

Example: Imputation, Discreteziation, Categorical Encoding, etc.

12. What do you know about MLOps tools? Have you ever used them in a machine learning project?

MLOps tools are the tools that are used to produce and monitor the enterprise-grade deployment of machine learning models. Examples of such tools are MLflow, Pachyderm, Kubeflow, etc.

In case you haven’t worked on an MLOps project, try this MLOps project by Goku Mohandas on Github or this MLOps Project on GCP using Kubeflow for Model Deployment by ProjectPro.

Data Science Technical Interview Questions

13. What do you understand by logistic regression? Explain one of its use-cases.

Logistic regression is one of the most popular machine learning models used for solving a binary classification problem, that is, a problem where the output can take any one of the two possible values. Its equation is given by 

logistic regression
Where X represents the feature variable,  a,b are the coefficients, and Y is the target variable. Usually, if the value of Y is greater than some threshold value, the input variable is labeled with class A. Otherwise, it is labeled with class B.

14. How are univariate, bivariate, and multivariate analyses different from each other?

Univariate Analysis

Bivariate Analysis

Multivariate Analysis

When only one variable is being analyzed through graphs like pie charts, the analysis is called univariate.

When trends in two variables are compared using graphs like scatter plots, the analysis of the bivariate type.

When more than two variables are considered for analysis to understand their correlations, the analysis is termed as multivariate.

15. What is K-means? 

K-means clustering algorithm is an unsupervised machine learning algorithm that classifies a dataset with n observations into k clusters. Each observation is labeled to the cluster with the nearest mean.

16. How will you find the right K for K-means?

To find the optimal value for k, one can use the elbow method or the silhouette method.

17. What do you understand by long and wide data formats?

In wide data format, you will find a column for each variable in the dataset. On the other hand, in a long format, the dataset has a column for specific variable types & a column for the values of those variables.
For example,


Wide Data Format
Wide Data Format

long data format
Long data format

Image Source: Mason John on Quora

18. What do you understand by feature vectors?

Feature vectors are the set of variables containing values describing each observation’s characteristics in a dataset. These vectors serve as input vectors to a machine learning model.

19. How does the use of dropout work as a regulariser for deep neural networks?

Dropout is a regularisation method used for deep neural networks to train different neural networks architectures on a given dataset. When the neural network is trained on a dataset, a few layers of the architecture are randomly dropped out of the network. This method introduces noise in the network by compelling nodes within a layer to probabilistically take on more or less authority for the input values. Thus, dropout makes the neural network model more robust by fixing the units of other layers with the help of prior layers.

20. How beneficial is dropout regularisation in deep learning models? Does it speed up or slow down the training process, and why?

The dropout regularisation method mostly proves beneficial for cases where the dataset is small, and a deep neural network is likely to overfit during training. The computational factor has to be considered for large datasets, which may outweigh the benefit of dropout regularisation.

The dropout regularisation method involves the random removal of a layer from a deep neural network, which speeds up the training process.

21. How will you explain logistic regression to an economist, physician-scientist, and biologist?

Logistic regression is one of the simplest machine learning algorithms. It is used to predict the relationship between a categorical dependent variable and two or more independent variables. The mathematical formula is given by 


Where X is the independent variable,  a,b are the coefficients, and Y is the dependent variable that can take categorical values.

22. What is the benefit of batch normalization?

  • The model is less sensitive to hyperparameter tuning.

  • High learning rates become acceptable, which results in faster training of the model.

  • Weight initialization becomes an easy task.

  • Using different non-linear activation functions becomes feasible.

  • Deep neural networks are simplified because of batch normalization.

It introduces mild regularisation in the network.

23. What is multicollinearity, and how can you overcome it?

A single dependent variable depends on several independent variables in a multiple regression model. When these independent variables are deduced to possess high correlations with each other, the model is considered to reflect multicollinearity. 

One can overcome multicollinearity in their model by removing a few highly correlated variables from the regression equation.

24. What do you understand by the trade-off between bias and variance in Machine Learning? What is its significance?

The expected value of test-MSE (Mean Square Error, for a given value x0, can always be decomposed into the sum of three fundamental quantities: the variance of f0‘(x0), the squared bias of f0(x0), and the variance of the error terms e. That is,

E(y0 − f0‘(x0))2 = Var(f0‘(x0) + [Bias(f0‘(x0))]2 + Var(e

Here the notation(y0 − f0(x0))2 defines the expected test MSE, and refers to the average test MSE that one would obtain if they repeatedly estimated f using a large number of training sets, and tested each at x0. Also, f0‘(x0) refers to the output of the fitted ML model for a given input x0 and e is the deviation of the predicted valuef0‘(x0) from the true value at a given x0.

The equation above suggests that we need to select a statistical learning method that simultaneously achieves low variance and low bias to minimize the expected test error. A good statistical learning method's good test set performance requires low variance and low squared bias. This is referred to as a trade-off because it is easy to obtain a method with extremely low bias but high variance (for instance, by drawing a curve that passes through every single training observation) or a method with a very low variance

but high bias (by fitting a horizontal line to the data). The challenge lies in finding a method for which both the variance and the squared bias are low.

25. What do you understand by interpolating and extrapolating the given data?

Interpolating the data means one is estimating the values in between two known values of a variable from the dataset. On the other hand, extrapolating the data means one is estimating the values that lie outside the range of a variable.

26. Do gradient descent methods always converge to the same point?

No, gradient descent methods do not always converge to the same point because they converge to a local minimum or a local optima point in some cases. It depends a lot on the data one is dealing with and the initial values of the learning parameter.

27. What is the difference between Supervised Learning and Unsupervised Learning?

Supervised Learning

Unsupervised Learning

If an algorithm learns something from the training data so that the knowledge can be applied to the test data, then it is referred to as Supervised Learning. 

If the algorithm does not learn anything beforehand because there is no response variable or training data, it is referred to as unsupervised learning. 

It is majorly used to make predictions for a dependent variable.

It is primarily used to perform analysis and group similar data points together.

Classification and Regression are an examples of Supervised Learning.

Clustering and dimensionality reduction are examples of unsupervised learning.

 

28. What is Regularization and what kind of problems does regularization solve?

Regularization is basically a technique that is used to push or encourage the coefficients of the machine learning model towards zero to reduce the over-fitting problem. The general idea of regularization is to penalize complicated models by adding an additional penalty to the loss function in order to generate a larger loss. In this way, we can discourage the model from learning too many details and the model is much more general.
There are two ways of assigning the additional penalty term to the loss function giving rise to two types of regularization techniques. They are

  • L2 Regularization 

  • L1 Regularization

In L2 Regularization, the penalty term is the sum of squares of the magnitude of the model coefficients while in L1 Regularization, it is the sum of absolute values of the model coefficients.

29. How can you overcome Overfitting?

We can overcome overfitting using one or more of the following techniques
1. Simplifying the model: We can reduce the overfitting of the model by reducing the complexity of model. We can either remove layers or reduce the number of neurons in the case of a deep learning model, or prefer a lesser order polynomial model in case of regression.

2. Use Regularization: Regularization is the common technique used to remove the complexity of the model by adding a penalty to the loss function. There are two regularization techniques namely L1 and L2. L1 penalizes the sum of absolute values of weight whereas L2 penalizes the sum of square values of weight. When data is too complex to be modeled, the L2 technique is preferred and L1 is better if the data to be modeled is quite simple. However, L2 is more commonly preferred.
3. Data Augmentation: Data augmentation is nothing but creating more data samples using the existing set of data. For example, in the case of a convolutional neural network, producing new images by flipping, rotation, scaling, changing brightness of the existing set of images helps in increasing the dataset size and reducing overfitting.
4. Early Stopping: Early stopping is a regularization technique that identifies the point from where the training data leads to generalization error and begins to overfit. The algorithm stops training the model at that point.
5. Feature reduction: If we have a small number of data samples with a large number of features, we can prevent overfitting by selecting only the most important features. We can use various techniques for this such as F-test, Forward elimination, and Backward elimination.
6. Dropouts: In the case of neural networks, we can also randomly deactivate a proportion of neurons in each layer. This technique is called dropout and it is a form of regularization. However, when we use the dropout technique, we have to train the data for more epochs.

30. Differentiate between Batch Gradient Descent, Mini-Batch Gradient Descent, and Stochastic Gradient Descent.

Gradient descent is one of the most popular machine learning and deep learning optimization algorithms used to update a learning model's parameters. There are 3 variants of gradient descent.
Batch Gradient Descent: Computation is carried out on the entire dataset in batch gradient descent.
Stochastic Gradient Descent: Computation is carried over only one training sample in stochastic gradient descent.
Mini Batch Gradient Descent: A small number/batch of training samples is used for computation in mini-batch gradient descent.
For example, if a dataset has 1000 data points, then batch GD, will train on all the 1000 data points, Stochastic GD will train on only a single sample and the mini-batch GD will consider a batch size of say100 data points and update the parameters.

Data Science Statistics Interview Questions

31. How can you make data normal using Box-Cox transformation?

The Box-Cox transformation is a method of normalizing data, named after two statisticians who introduced it, George Box and David Cox. Each data point, X, is transformed using the formula Xa, where a represents the power to which each data point is raised. The box-cox transformation fits the data for values -5 to +5 until the optimal ’a' value that can best normalizes the data is identified. 

32. What does P-value signify about the statistical data?

In statistics, the p-value is used to test the significance of a null hypothesis. A p-value lower than 0.05 suggests that there is only 5% chance that the outcomes of an experiment are random and the null hypothesis must be rejected. On the other hand, a higher p-value,say0.8, suggests that the null hypothesis can not be rejected as 80% of the sample has random outcomes.

33. Why do we use A/B Testing?

A/B Testing is a technique for understanding user experience. It involves serving a user with two different product versions to analyze which version is likely to outperform the other. The testing is also used to understand user preferences.

34. What is the standard normal distribution?

The standard normal distribution is a special kind of normal distribution in statistics that zero mean and standard deviation equals one. The graph of a standard normal distribution looks like the famous bell curve with zero at its center. As you can see, the distribution is symmetrical around the origin, and asymptomatic.

35. What is the difference between squared error and absolute error?

 

Squared Error

Absolute Error

The squared error is the square of the difference between the value of a quantity,x from its inferred value, x’.


It is represented as (x-x’)2.

As the name suggests, the absolute error refers to the modular of the difference between the value of a quantity,x from its inferred value, x’.


It is represented as |x-x’|.

 

In data science, mean squared error is more popular for understanding the deviation of the inferred values from the actual values as it gives relatively more weight to the highly deviated points and gives a continuous derivative which is useful for analysis.

36. What is the difference between skewed and uniform distribution?

A skewed distribution is a distribution where the values in the dataset are not normalized and the distribution curve is inclined towards one side. A uniform distribution on the other hand is a symmetric distribution where the probability of occurrence of each point is same for a given range of values in the dataset.

37. What do you understand by Recall and Precision?

For explaining Recall and Precision, it is best to consider an example of a confusion matrix.

Predicted\
Actual

Cancer Patient

Not a Cancer Patient

Cancer Patient

30

12

Not a Cancer Patient

10

28

 

Assume that the confusion matrix mentioned above represents the results of the classification problem of cancer detection. It is easy to conclude the following:

True Positives, No. of patients actually having cancer = 30

True Negatives, No. of patients that do have cancer = 28

False Positives, No. of patients that do not have cancer but the model predicted otherwise = 12

False Negatives, No. of patients that have cancer but the model predicted otherwise = 10

For such problem, 

Recall = True Positives / (True Positives + False Negatives) = 30/40 = 0.75

The formula for recall clearly suggests that it estimates the ability of a model to correctly identify true positives, that is, the patients who are infected with cancer. To understand it better, take a careful look at the denominator which is nothing but the total number of people possessing cancerous cells. Thus, a recall value of 0.75 suggests that the model was able to correctly identify 75% of the patients that have cancer.On the other hand, Precision = True Positives / (True Positives + False Positives) = 30/42 = 0.71

The formula for Precision suggests that it reflects how many times the model is successful in deducing True positives wrt the false positives. Thus, the number 0.71 suggests that whenever the model predicts a patient has cancer, the chances of making a correct prediction are 71%.

38. What is the curse of dimensionality?

High dimensional data refers to data that has a large number of features. The dimension of data is the number of features or attributes in the data. The problems arising while working with high dimensional data are referred to as the curse of dimensionality. It basically means that error increases as the number of features increases in data. Theoretically, more information can be stored in high-dimensional data, but practically, it does not help as it can have higher noise and redundancy. It is hard to design algorithms for high-dimensional data. Also, the running time increases exponentially with the dimension of data.

39. What is the use of the R-squared value?

The r-squared value compares the variation of a fitted curve to a set of data points with the variation of those points wrt the line that passes through the average value. It can be understood with the help of the formula 

R2 = [Var(mean) - Var(model)] / Var(mean)

It is obvious that the model is likely to fit better than the average line. So, the variation for the model is likely to be less than the variation for the line. Thus, if the r-square has a value of 0.92, it suggests that the model fits the data points better than the line as there is 92% less variation. It also shows that there is a strong correlation between the feature and target value. However, if the r-squared value is less, it suggests that the correlation is weak and the two variables are quite independent of each other.

Data Science Probability Interview Questions

40. What do you understand by Hypothesis in the content of Machine Learning?

In machine learning, a hypothesis represents a mathematical function that an algorithm uses to represent the relationship between the target variable and features.

41. How will you tackle an exploding gradient problem?

By sticking to a small learning rate, scaled target variables, a standard loss function, one can carefully configure the network of a model and avoid exploding gradients. Another approach for tackling exploding gradients is using gradient scaling or gradient clipping to change the error before it is propagated back through the network. This change in error allows rescaling of weights.

42. Is Naïve Bayes bad? If yes, under what aspects.

Naïve Bayes is a machine learning algorithm based on the Bayes Theorem. This is used for solving classification problems. It is based on two assumptions, first, each feature/attribute present in the dataset is independent of another, and second, each feature carries equal importance. But this assumption of Naïve Bayes turns out to be disadvantageous. As it assumes that the features are independent of each other, but in real-life scenarios, this assumption cannot be true as there is always some dependence present in the given set of features. Another disadvantage of this algorithm is the ‘zero-frequency problem’ where the model assigns value zero for those features in the test dataset that were not present in the training dataset.

43. How would you develop a model to identify plagiarism?

Follow the steps below for developing a model that identifies plagiarism:

  • Tokenise the document.

  • Use the NLTK library in Python for the removal of stopwords from data.

  • Create LDA or SDA of the document and then use the GenSim library to identify the most relevant words, line by line.

  • Use Google Search API to search for those words.

44. Explain the central limit theorem.

The central limit theorem says that if someone collects a large number of samples of a population, the distribution spread of their mean values will obey the curve of a normal distribution curve irrespective of the distribution each sample obeys.

45. What is the relevance of the central limit theorem to a class of freshmen in the social sciences who hardly have any knowledge about statistics?

The most important consequence of the central limit theorem is that it reveals how nature likes to obey the normal distribution curve. It allows experts from various fields like statistics, physics, mathematics, computer sciences, etc. to assume that the data they are looking at obeys the famous bell curve.

46. Given a dataset, show me how Euclidean Distance works in three dimensions.

The formula for evaluating euclidean distance in three dimensions between two points defined by coordinates (x1,y1,z1) and (x2,y2,z2) is simply given by 

Distance = _/ (x1-x2)2 + (y1-y2)2 + (z1-z2)2

It simply represents the length of a line that connects the two points in a three-dimensional space.

47. In experimental design, is it necessary to do randomization? If yes, why?

Yes, it is necessary to use randomization while designing experiments. By randomization, we try to eliminate the bias as much as possible. The main purpose of randomization is it automatically controls for all lurking variables. Experiments with randomization establish a clearer causal relationship between explanatory variables and response variables by having control over explanatory variables.

Data Science Coding Interview Questions

48. What will be the output of the following R programming code?

var2<- c("I","Love,"ProjectPro")

var2

It will give an error.

49. Find the First Unique Character in a String.

def frstuniquechar(strng: str) -> int:

    # Lowercase

    strng = strng.lower()

    

    # Here is a dictionary that will contain each unique letter and its counts

    c = {}

    

#Iterating over every letter in the string

    for letter in strng:

        # If can’t find the letter in dictionary, add it and set the count to 1

        if letter not in c:

            c[letter] = 1

        # If can’t find the letter in dictionary, add 1 to the count

        else:

            c[letter] += 1

            

    #Iterating the range of string length

    for i in range(len(strng)):

        # If there's only one letter

        if c[strng[i]] == 1:

            # Return the index position

            return i

    

    # No first unique character

    return -1



# Test cases

for s in ['Hello', 'Hello ProjectPro!', 'Thank you for visiting.']:

    print(f"Index: {frstuniquechar(strng=s)}")

 

50.  Write the code to calculate the Factorial of a number using Recursion.

def fact(num):

    # Extreme cases

    if num< 0: return -1

    if num == 0: return 1

    

    # Exit condition - num = 1

    if num == 1:

        return num

    else:

        # Recursion Used

        return num * factorial(num - 1)

     

# Test cases

for num in [1, 3, 5, 6, 8, -10]:

    print(f"{num}! = {fact(num=num)}")

Statistics Data Science Interview Questions 

  1. Out of L1 and L2 regularizations, which one causes parameter sparsity and why?

  2.  List the differences between Bayesian Estimate and Maximum Likelihood Estimation (MLE).

  3. Differentiate between Cluster and Systematic Sampling?

  4. How will you prevent overfitting when creating a statistical model?

Python Data Science Questions for Interview 

  1. Explain the range function.

  2. How can you freeze an already built machine learning model for later use? What command you would use?

  3. Differentiate between func and func().

  4.  Write the command to import a decision tree classification algorithm using sklearn library.

  5. What do you understand by pickling in Python?

Entry-Level Data Scientist Interview Questions 

  1. What are some common data preprocessing techniques used in data science?

  2. What is the difference between bias and variance in the context of machine learning models?

  3. What is the purpose of feature scaling in machine learning? Name a few techniques used for feature scaling.

  4. How would you handle missing values in a dataset? What are some common imputation techniques?

  5. What is the role of feature selection in machine learning? Discuss some approaches to select relevant features from a large feature space.

Senior Data Scientist Interview Questions 

  1. How do you handle the issue of model interpretability and explainability?

  2. Can you discuss a time when you had to work with a large dataset that exceeded the memory capacity of your machine? What strategies or tools did you use to handle the data and perform analysis efficiently?

  3. How do you approach the process of evaluating and selecting the most appropriate evaluation metrics for a machine-learning model? Can you provide an example of a project where you had to make decisions about evaluation metrics?

  4. Explain the concept of bias and variance in machine learning models. How do you balance these two factors to ensure the optimal performance of a model?

Suggested Answers by Data Scientists for Open-Ended Data Science Interview Questions

  1. How can you ensure that you don’t analyze something that ends up producing meaningless results?

Understanding whether the model chosen is correct or not. Start understanding from the point where you did Univariate or Bivariate analysis, analyzed the distribution of data and correlation of variables, and built the linear model. Linear regression has an inherent requirement that the data and the errors in the data should be normally distributed. If they are not then we cannot use linear regression. This is an inductive approach to find out if the analysis using linear regression will yield meaningless results or not.

Another way is to train and test data sets by sampling them multiple times. Predict on all those datasets to determine whether the resultant models are similar and are performing well.

By looking at the p-value, by looking at r square values, by looking at the fit of the function, and analyzing as to how the treatment of missing value could have affected- data scientists can analyze if something will produce meaningless results.

- Gaganpreet Singh, Data Scientist

So, there you have over 120 data science interview questions and answers for most of them too. These are some of the more common interview questions for data scientists around data, statistics, and data science that can be asked in the interviews. We will come up with more questions – specific to language, Python/ R, in the subsequent articles, and fulfill our goal of providing 120 data science interview questions PDF with answers to our readers.

3 Secrets to becoming a Great Enterprise Data Scientist

•    Keep on adding technical skills to your data scientist’s toolbox.

•    Improve your scientific axiom

•    Learn the language of business as the insights from a data scientist help in reshaping the entire organization.

The important tip, to nail a data science interview is to be confident with the answers without bluffing. If you are well-versed with a particular technology whether it is Python, R, Hadoop, Spark or any other big data technology ensure that you can back this up but if you are not strong in a particular area do not mention it unless asked about it. The above list of data scientist job interview questions is not an exhaustive one. Every company has a different approach to interviewing data scientists. However, we do hope that the above data science technical interview questions elucidate the data science interview process and provide an understanding of the type of data scientist job interview questions asked when companies are hiring data people.

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