Data Science Interview Questions & Answers (Learn By Heart!)

As the world continues to evolve into the era of big data, there is a rapid increase in the requirement for its storage. Till 2010, it remained the primary obstacle and source of uncertainty for the business industry. The primary areas of focus were the establishment of a framework and the development of methods to store data. Now that Hadoop and other frameworks have successfully handled the challenge of storage, the focus has switched to the analysis of this data. Data science is the elixir that unlocks all of these mysteries. 

With data science, all of the concepts that are shown in Hollywood science fiction movies are truly capable of being transformed into a reality. The field of data science is where artificial intelligence is headed in the foreseeable future. As a result, it is of utmost importance to understand what data science is and how it may offer value to an organization.

Click here to explore a specialized placement-focused crash course in data science that takes you from a rookie to an evolving expert in 2.5 months.

To help you brush up on important topics, here is a comprehensive list of data science interview questions.

Data science interview questions 

If you aspire to be a data scientist then you must go through these data science interview questions. 

Let's start with the basic data science interview questions and then proceed to the advanced level.

1. What can you tell us about "Deep Learning"?

Deep learning is a subset of artificial intelligence and machine learning which uses deep neural networks. These are algorithms, nearly modeled after the human brains, to instruct computer systems to replicate human brains in terms of thinking and learning. This is accomplished by teaching computers to learn in a manner similar to how human brains learn.

In contrast to more conventional approaches to machine learning, deep learning models can hold millions or even billions of parameters. This makes them more difficult to analyze but gives a significant advantage in comprehending data. A deep learning model can be considered a "hologram" of information since it stores weights. So they are interrelated and reflect pieces of what the model has learned in the process of deep learning. 

2. Explain the distinctions between big data and data science.

Data science is a multidisciplinary discipline that focuses on the analytical elements of data and incorporates principles from statistics, data mining, & machine learning model. These fundamentals are the building blocks that data scientists use to construct reliable hypotheses based on empirical evidence.

Big data works with a vast collection of data sets and tries to solve challenges connected to the management and handling of data for more informed decision-making. 

3. Explain the regression data set.

The regression data set is a reference to the directory containing the test data for the linear regression model, known as the data set directory. The most basic kind of regression is determining the best linear connection between a given set of data (xi, yi) that has been collected.

 4. Why data cleansing is necessary?

The process of "data cleansing" involves sorting through all of the information contained in a database and removing or updating any information that is found to be inaccurate, incomplete, insufficient, or excessive. Because doing so enhances the reliability of the data, which is of critical importance.

5. What is meant by the term "machine learning"?

Machine learning is a subfield of computer science that uses mathematical algorithms to discover the trend or pattern in such an entire dataset. The term "machine learning" consists of two words: machine and learning, which hint at what it is.

The most elementary illustration of this concept is linear regression, which is represented by the equation "y = mt + c" and is used to forecast the value of a single random variable y as an activation function of time. By applying the equation to the data in the set and determining which values for m and c produce the best fit. The machine learning model can learn about the patterns in the data. After that, one can utilize these equations and confounding variables to estimate what the future values will be.

6. What do you know about recommendation systems?

Gaining an understanding of the behavior of customers and potential customers is the major focus of many firms. Think about the situation with Amazon, for example. Amazon's backend classification algorithms have a significant hurdle whenever a user searches for a product category on the company's website: the goal is to generate suggestions for such users that really are likely to encourage them to make a purchase.

In addition, these classification algorithms provide the essential component of recommendation systems, often known as recommender systems. These systems are designed to analyze customers' behavior and determine the level of love customers have for various products. Recommender systems are utilized by various online retailers, including but not limited to Netflix, YouTube, Flipkart, and others, in addition to Amazon.

7. What is the difference between an "Eigenvalue" and an" Eigenvector"?

While attempting to comprehend linear transformations, eigenvectors are an invaluable tool. They are also the directions where a specific linear transformation acts by either flipping, compressing, or stretching the data. The pace of the change in the eigenvector's direction is known as the eigenvalue, which can also be thought of as the factor by which the compression takes place. In data analysis, computing the eigenvectors of a correlation or covariance matrix is a common practice.

8. Could you please explain feature vectors?

The collection of dependent variables inside an entire dataset that contain values describing the qualities of each observation is referred to as a feature vector. These vectors are used as input vectors for a statistical model that uses machine learning.

9. What is meant by the term "Regularization"?

Regularization is a method utilized to push or stimulate the coefficients of the logit model of machine learning or deep learning toward zero to eliminate the issue of over-fitting. This is accomplished through the use of a technique known as "push-learning." As a broad concept, regularization aims to simplify complex models by making them more difficult to solve by including an additional penalty in the loss function that causes it to produce a greater loss. Because of this, we can prevent the model from acquiring excessive detail, which results in the statistical model having a significantly more general understanding.

10. What does "P-value" signify?

In statistics, the p-value is a method for determining whether or not the null hypothesis has any significant bearing on the data. A p-value that is less than 0.05 indicates that there is less than a 5% probability that the results of a randomized experiment are random, indicating that the null hypothesis should be rejected. On the other hand, if the p-value is greater, say 0.8, it indicates that the null hypothesis cannot be rejected because 80% of the sample consists of individuals who experienced random outcomes.

11. Could you explain the concept of the normal distribution in its standard form?

In the field of statistics, a unique sort of normal distribution known as the standard normal distribution is characterized by a mean value of zero and a standard deviation that equals one. According to the conventional definition, the graph of a normal distribution looks like the well-known bell curve with zero in the middle of it. As can be seen, the probability distribution is perfectly symmetrical around the point of origin, and it does not exhibit any symptoms.

12. What is "the curse of dimensionality"?

The term "high dimensional data" refers to information with a significant number of defining characteristics. The amount of distinct features or attributes contained inside the data is referred to as the dimension of the data. The term "curse of dimensionality" refers to the difficulties that manifest themselves while working with data that has a high number of dimensions. In a nutshell, it essentially signifies that the magnitude of the mistake will increase in relation to the number of features present in the data. High-dimensional data can, in theory, retain more information than lower-dimensional data. 

Still, in practice, this is not helpful because high-dimensional data might have higher levels of noise and redundancy. When working with high-dimensional data, it might be challenging to create classification algorithms. Additionally, the amount of time required to complete the task increases exponentially with the number of data dimensions.

13. Why do we do "A/B" tests, and what do we want to accomplish with them?

(This is one of the most-asked data science interview questions.)

The A/B test is a type of statistical hypothesis testing designed for use in a randomized experiment involving two different confounding variables, A and B. The purpose of A/B testing is to determine whether or not a web page has been modified in any way, with the end objective of increasing the possibility of an outcome that is of some importance to the participant.

A/B testing is a very dependable strategy that can be used to determine the most effective online marketing and advertising tactics for just a company. This method can be used to test anything, from sales emails to internet advertisements and website copy, and to apply to all of these areas.

14. Can you explain the difference between linear regression and logistic regression?

A type of statistical method known as linear regression model is one in which the value of a certain random variable, denoted by the letter Y, is predicted based on the value of a second variable, denoted by the letter X and referred to as the predictor variable. The letter Y denotes the criteria random variable, which is present in the equation. The linear regression model helps in determining the values of a particular data and spot the pattern. 

Logistic regression is a statistical approach for predicting the binary result using only a linear combination of predictor-dependent variables. 

15. What is the meaning of the term "dropout"?

In the field of data science, the term "dropout" refers to a technique employed to arbitrarily remove units from a network, both hidden and apparent. By removing as many as twenty percent of the nodes from the network, they avoid overfitting the data and make it possible to arrange the necessary space for the iterations required to converge the network.

16. What is meant by the term "cost function"?

A model's performance can be evaluated using cost functions to determine how well the logit model is doing. It considers any faults or losses that occurred in the output layer while the back-propagation is carried out. In a scenario like this one, the residual errors are shifted in the neural network in the opposite direction, and various other training kernel functions are implemented.

17. Could you please explain the meaning of hyperparameters?

Hyperparameter is a type of parameter, and its value is determined even prior to the learning process starts. This is to make sure that the network training requirements can be determined and the structure of the network may be improved. This also includes identifying things like hidden units, learning rates, and epochs, amongst other things.

18. Could you please explain the concept of batch normalization?

Batch normalization is a technique that enables us to enhance the stability and efficiency of a neural network. Such improvements could be made via the use of this technique. This can be done by standardizing the inputs for each output layer. So, the output activation continues to be zero while the standard deviation is kept at one.

19. Tell me about autoencoders?

Simple learning networks, also known as autoencoders, are put to use in translating inputs into outputs with as little room for mistakes as feasible. What this indicates is that the results of the output are quite similar to the results of the input.

A couple of layers have been added between the input and the output, with each layer having a size that is lower than the size that pertains to the input layer. Input that is not labeled is taken in by an autoencoder and encoded so that the output can be reconstructed.

20. What are tensors, and how do they work?

Tensors are mathematical entities that reflect the accumulation of extra dimensionality of data inputs in alphabets, numerals, and a rank that are provided as inputs to a neural network. In other words, tensors are the collection of higher dimensional data inputs.

21. What is the activation function?

The activation function facilitates the introduction of non-linearity into the neural network. When it comes to learning complicated kernel functions, this is done to help make the learning process easier. Without the activation function, the neural network will be unable to work the linear activation function solely or implement linear combinations. The activation function consequently offers complicated kernel functions & combinations by using artificial neurons, that assist in offering output based on the inputs. This is made possible by the application of artificial neural networks.

22. Could you please explain RNN to me?

"Recurrent Neural Networks" are a type of artificial neural network made up of a sequence of data. This data sequence might include things like time series, stock markets, and a variety of other things. The main aim of the RNN application is to compare the fundamentals behind feedforward nets.

23. What is meant by the phrase "reinforcement learning"?

In the machine learning model, the unsupervised learning technique known as reinforcement learning is known as reinforcement learning. This unsupervised learning is also called a state-based learning approach. During the training phase, the system will be able to transition from one state to another since the models include rules for changing states that have already been set in unsupervised learning. 

24. Can you please explain the concept of cross-validation?

Cross-validation is used to evaluate how the findings of statistical analysis would extrapolate to another data set that is not related to the first. Its primary application is in situations where prediction is the desired outcome, and one needs to evaluate the degree to which a predictive model performs accurately in actual situations.

This exercise aims to define a data set with the intention of testing a model while it is still in the training phase and limiting issues of overfitting and underfitting, respectively. To prevent things from worsening, the validation set and the training set will be selected from the same probability distribution.

25. Explain selection bias.

Selection bias is the process when there is no random collection of research participants . The way the random sample is collected can introduce a bias into statistical analysis, which can then be seen as having been distorted as a result. The term "selection effect" can sometimes be used interchangeably with "selection bias." If experts fail to account for possible biases in the selection of study subjects, the results of their research may not be reliable.

26. What are the support vectors in SVM?

Support vectors are data points that are situated in closer proximity to the hyperplane and have an effect on the position & orientation of the hyperplane. We are able to increase the margin of such a classifier by making use of these support vectors. The location of the hyperplane shifts if the support vectors that are now there are removed. These are the considerations that aid us in the construction of our SVM.

27. What is meant by the phrase "root cause analysis"?

The process of tracing back an event's occurrence and the circumstances that lead to that thought is known as root cause analysis. It is typically carried out in the event that a piece of software experiences a problem. Root cause analysis is a technique used in data science that enables businesses to better comprehend the meanings associated with particular results.

28. Can you please explain the concept of clustering?

The term "clustering" refers to organizing individual data points into several distinct groups. The division is carried out in such a way that all of the data points placed within the same group are much more comparable to one another than the data points placed within the other groups. Clustering can take many different forms, some of which include the following: hierarchical clustering, "K"-means clustering, density-based clustering, fuzzy clustering, etc.

29. What is imbalanced data?

Imbalanced data refers to many sorts of datasets in which there is an unequal distribution of observations toward the target class. To put this another way, imbalanced data refers to distinct types of entire datasets, which suggests that one class label has a bigger number of observations compared to the other label.

30. Please explain "Star Schema".

The database organization within the star architecture, where all the measured data is kept in a single fact table is referred to as star schema. It is called a star schema due to the fact that the primary table occupies the middle position in a logical diagram, while the subsidiary tables radiate outward from it like the nodes on a star.

31. What is meant by the term "Power Analysis"?

The experimental design should always include a power analysis as an essential component. It assists in determining the sample size required to discover the impact of a certain size from a cause with such a particular level of assurance. In addition, it allows you to implement a certain probability within the confines of a random sample size limitation.

32. What is meant by the term "back propagation"?

Training a neural network relies heavily on back propagation as its central methodology. The approach of setting the weights of a neural net to depend upon the residual error rate achieved in the prior epoch is known as the back propagation of errors method. The correct tuning enables you to lower error rates and make the model more dependable by boosting the model's level of generalization.

33. Can you define "Boltzmann Machine"?

The "Boltzmann Machines" is a straightforward form of machine learning and deep learning method. It assists in locating the characteristics of the training data that best represent the intricate patterns of interest. Using this approach, you can optimize both the weights and the quantity for the problem that has been presented to you.

34. What is meant by the phrase "random forest"?

In the machine learning model, classification plays an extremely significant role. It is extremely vital to have a clear understanding of the category to which an observation belongs. Now, machine earning and deep learning has several different classification algorithms, such as logistic regression, support vector machine, decision trees, "Naive Bayes classifier," etc. The random forest classifier is one type of classification method close to the top of the classification hierarchy. The random forest is also among the top-used classifiers. 

35. What is the key distinction between simple and residual errors?

The disparity between the expected value and the obtained value is what is meant by the term "error." The "Mean Absolute Error" (MAE), the "Mean Squared Error" (MSE), and also the Root Mean Squared Error are the three methods in data science that are utilized the most frequently to calculate errors (RMSE).

The discrepancy between a set of values that have been observed and the arithmetic mean of those values is referred to as residual. The term "error" refers to anything that cannot be directly observed, but "residual error" refers to something that can be plotted on a graph. The residual error is a representation of the degree to which the observed training dataset deviates from the true population. A residual, on the other hand, is a representation of the observed data's differences from the data collected from the random sample standard population.

36. What is "systematic sampling"?

The method of statistical sampling also known as systematic sampling involves selecting elements at random from inside an ordered sampling frame. When conducting systematic sampling, the list is worked through like a circle; hence, when you get to the bottom of the list, you start working your way back up to the beginning. The equal probability method is the most prominent illustration of the systematic sampling approach instead of random sampling.

37. What is meant by "pruning" in the context of the decision tree?

In the field of machine learning and deep learning, pruning is a method that may be used to shrink the size of decision trees by chopping off branches or nodes that don't contribute significantly to the tree's overall ability to classify instances. Therefore, the act of removing sub-nodes from a decision node is referred to as pruning, which is the reverse of the splitting process.

38. Could you please explain the "Computational" Graph?

A graphical representation constructed using TensorFlow is referred to as a computational graph. It comprises a vast network with various nodes, each of which stands for a distinct mathematical process. Tensors are what you term the connections between these nodes. This is because the computational graph is also referred to as a TensorFlow of inputs. Since the data flow in the shape of a graph, the computational graph is also referred to as the DataFlow Graph. This is because the data flows are what describe the graph.

39. What is the difference between the test and validation sets?

A validation set is utilized to fine-tune a model while it is being trained. During the training process, this is the collection of training dataset that the model "checks against" to determine how well it performs as a predictive tool. It should have a comparable appearance to the training set, but it should also be sufficiently different to teach the model how to recognize new cases.

Whenever the training of a model is finished, it will be compared to another set of data, known as a test set. This allows the random forest model to validate its accuracy. The model has never been exposed to any of this information before, and it is being used to test whether or not it can be generalized.

40. What are exploding gradients?

Exploding gradients refer to the buildup of huge residual error gradients, which as a result, causes unusually large modifications to the neural network model weights while the network is being trained. This, in turn, contributes to the instability of the network.

There is also the possibility that the values of the weights could become so high as to overflow, resulting in something that is referred to as "NaN" values.

41. What is the fundamental difference between tuples and lists?

In some programming languages, lists are declared in a manner analogous to that of arrays. When it comes to data structures, a list functions as a container that can store multiple pieces of data simultaneously. Maintaining track of a data sequence by iterating over it using lists is helpful since lists are a useful method to organize this information in deep learning. 

Another sort of data that can be used in sequences is called a tuple, and unlike other sequence data types, it cannot be changed. Tuples can contain elements of a wide variety of training dataset types. To put it another way, a tuple is a collection of Python objects that are delineated from one another by commas. The tuple is significantly quicker than the list because of the static nature of the data it contains.

42. What is "Polling" in regard to CNN?

Polling is a method used to lessen the effect of a CNN's spatial dimensions. Downsampling processes are doen during this, such as those used to reduce dimensionality and produce pooled feature maps. The process of pooling in CNN is beneficial for sliding the filter matrix across the input matrix.

43. What are the key distinctions between univariate analysis and multivariate analysis?

Statistics that simply take into account one random variable at a time are called univariate statistics or univariate analysis. Statistics that compare and contrast single variable are called bivariate statistics. Statistics that compare more than two dependent variables are called multivariate statistics multivariate analysis .

44. What is random sampling?

The sampling technique includes a component known as random sampling, which ensures that every possible sample standard has an equal chance of being selected. The purpose of a sample that is selected at random is to provide an accurate representation of the entire population. Random sampling is used very commonly in data science.

45. In a binary tree, what are leaf nodes?

In a tree structure known as a binary tree, each node can only have a maximum of two offspring. Each node has its own memory for storing data. Nodes that do not have any offspring are known as leaf nodes, while nodes that do have children are referred to as inner nodes.

46. What are false positives?

A false positive, also known as a Type I error or an alpha error, is an error that occurs when a researcher incorrectly concludes that an effect exists or when a researcher incorrectly rejects a null hypothesis even though the null hypothesis is true. Both of these examples are examples of errors or false positives that can occur in research.

Sample HR questions

Here are some of the top HR round questions with sample answers.

47. What about your current job makes you wish to seek employment elsewhere?

During the past three years, I have had a wonderful time working at my current workplace. I had a fantastic team to support me, and I also like my coworkers. On the other hand, I believe that I have dealt with all the possible difficulties and brand-new circumstances that could potentially be there. Because of this, I am currently searching for fresh challenges and possibilities that will force me out of my comfort zone and put the extent of my capabilities to the test. The position that your organization is offering will be ideal for me, and I can promise you that I would add both value and experience to the team if I were to join it.

48. What is your strategy for responding to constructive criticism?

I am always open to constructive criticism from others, and if someone does so, I will immediately put my attention on improving myself and gaining knowledge from the same. This would greatly assist me in developing more and moving forward. If the criticism is unfavorable, I am an adult enough to brush it off and keep my concentration on doing my job effectively to my abilities without letting it dampen my enthusiasm.

49. Why should we hire you?

I believe that I would be an excellent candidate for this work opportunity. I am confident in my suitability for this position because I match the qualifications listed in the job description. Additionally, I also have experience working in the field of web development. I prefer problem-solving & performing well in teams. I also have the impression that the values this company upholds are consistent with those I hold. I believe that this career opportunity will allow me to pursue my interests and present me with intriguing & exciting opportunities to make a positive contribution to the growth of this organization. I couldn't have asked for a better chance than this one.

Also read: How To Answer "Why Should We Hire You?" (With Examples)

50. Please explain the gap in your resume?

Following the completion of my master's degree, I went straight into the workforce and did not take a break for the next six years. Because of this, I've decided to take some time off from my job so that I can volunteer in other activities. This assisted me in decluttering my thoughts while teaching me new skills and allowing me to lend a helping hand to others (like communication and organizational ability).

51. What motivates you to work hard and perform a good job?

I am motivated to perform to the best of my abilities to achieve success. Knowing that the effort I put in and the perseverance I show will pay off in the form of increased professional achievement is what drives me to succeed. One of the ways that I believe this can be accomplished is by aligning the organization's goals and principles with those I hold for myself. Because I am aware that my efforts are heading in the right direction, it is a source of motivation for me to exert even more effort.

Consequently, I place a high priority on advancement, both in terms of my own life and the success of the organization I work for. The company's success serves as a source of motivation for its employees since it brings them closer to achieving their own goals and financial success.

52. What will you do if we don't hire you?"

If I'm not selected, I will naturally be slightly disappointed since this is my dream job. However, that would not be the end of the way I'm going. If I'm not selected, I will discover where I've made a mistake, and I will do everything I can to conquer it as soon as I can. The very next time I come, I'll be selected.

53. How do you perform under pressure and stress?

Over the course of the last few years, I have seen that I tend to perform better when I am under some sort of time crunch or another kind of stress. When I have fewer tasks to complete, I frequently find myself working at a more leisurely pace. Consequently, I ended up putting in the same number of hours for a significantly reduced quantity of work. If I have a lot of due dates all at once, I will work much more effectively and complete a lot more work in the same amount of time as usual.

54. Why did you change companies frequently?

When starting my career, I had a bad habit of bouncing about from job to job because I could never find one in which I felt my abilities were being utilized to their utmost potential. After reading your job requirements and learning more about the company profile, I believe that I will be able to make the most of my potential and acquire new skills while working in this position.

55. How do you adapt to change?

I believe that how easy it is to adjust to a change is directly proportional to the magnitude of the change. When I was younger, a career opportunity required that I relocate to a new city. It was a significant obstacle for me, particularly since I was unable to communicate with locals in their native tongue, which made it more challenging for me to adjust to the new environment. It took me some time to acclimate, and in that period, I also attempted to educate myself in the language. In the end, I decided to try it and ended up remaining there for a total of six months; by the time I left, I considered that location my new home.

56. What would your notion of the perfect workplace look like?

In my opinion, the perfect place of employment is one that acknowledges the efforts of its workforce and provides appreciation and incentives in exchange for those efforts. I believe that an employer who acknowledges and rewards employees is sending a message to those workers that the organization recognizes and appreciates their efforts.

About Data Science 

Data science is an interdisciplinary area that uses a wide variety of tools, methodologies, and technologies, which are always evolving.

From an operational perspective, managing distribution networks, product stockpiles, distribution networks, and customer support can all be optimized through data science efforts. On a more basic level, they are a directional indicator that leads to better efficiency and lower expenses. Data science also allows businesses to develop strategies and business plans based on detailed analyses of customer behavior, market trends, and competition in their respective markets. If they do not have it, organizations risk missing out on possibilities and making poor decisions.

(Please note: These data science interview questions are for both freshers and experienced candidates.)

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