Top 50 data science interview questions in 2024 - Great Learning Minds

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Top 50 data science interview questions in 2024

The tech world is constantly that too at a rapid speed. One of the relatively newer concepts is data science which is on the rise. Given the increase in demand for data science professionals, there are more opportunities now. With a good data science course, you’ll be prepared for any job you want. We understand how overwhelming it must be for aspirants to deal with data science interview questions and job applications.

It is good to have a reference before you navigate into the job market. Having an idea of what kind of data science questions you get asked in an interview makes things easier. There are several skill sets that individuals can develop through a data science training course before preparing for an interview. Recruiters also do a thorough assessment via machine learning interview questions.

Top 50 data science interview questions in 2024

What is data science?

Data science is an interdisciplinary approach to mine and analyse raw data. The purpose is to recognise patterns and extract useful insights from them. The core foundation of data science consists of concepts from statistics, data analysis, computer science, data visualisation, deep learning and of course machine learning.

So if you are seeking a job in this industry be ready to face my interview questions as well! Knowing just data science questions won’t be enough.

What do recruiters seek through data science interview questions?

The focus is to see whether an interviewee has strong basics and clarity in practical applications. Apart from proper knowledge of data science core tools and processes, you must be prepared for ML interview questions as well.

Here we discuss a bunch of data science questions along with machine learning questions and answers for your reference. Check out these probable questions if you are someone aspiring to be a data scientist. We give you an idea of the kind of questions you might encounter and hopefully crack your job interview!

What are the top data science interview questions that interviewers might ask?

1) How would you define data science?

Ans. Sounds like an easy question, right? But you might get asked.Data science as a term emerged from the evolution of data analysis, statistics and big data. It is an interdisciplinary field which extracts insights from a wide range of data using several scientific methods. Analysis of raw data leads us to hidden patterns.

2) State the difference between data science and machine learning.

Ans. On one hand, data science has algorithms, machine learning, tools and processes that help in pattern recognition from raw data. On the other hand, we have a branch of computer science called machine learning that teaches system programs to learn and improve automatically.

3) What do you understand about a decision tree?

Ans. We use a decision tree in operation research, strategic planning and machine learning. It consists of endpoints that connect to a branch called a node. The more the nodes, the more accurate the decision. The decision is made at the leaves of the tree which are the last nodes.

4) Explain prior probability and likelihood.

Ans. In a data set, the proportion of dependent variables is the prior probability. Whereas, the classification probability of a given observant in another variable’s presence is what likelihood entails.

5) What does Recommender Systems mean?

Ans. Recommender systems assist in users’ preference prediction. In simpler terms, it is a sub-category of information filtering techniques.

6) What biases can occur during sampling?

Ans. We can fall prey to three types of biases during sampling which are-

    ● Survivorship bias

    ● Selection bias

    ● Under coverage bias

7) Why is resampling required?

Ans. There might arise a situation where we need to resample. For example, the following cases-

    ● Label substitution on data points for conducting tests

    ● Estimation of sample statistics accuracy by drawing randomly with replacement

    ● Using random subsets for model validity

8) Why is data cleaning important in data analysis?

Ans. Data cleaning has several purposes in data analysis. However, two of its most important ones are-

    ● Data cleaning is useful for data transformation so that it is easier to work

    ● Data cleaning is also helpful in Increasing machine learning model accuracy

9) What do you mean by Power Analysis?

Ans. For an experimental design, power analysis is quite an integral part. We can estimate the required sample size to determine the effect of a given size with a particular assurance level. In a constrained sample size, we can deploy a certain probability with power analysis.

10) What do you understand by collaborative filtering?

Ans. Collaborative filtering is a technique that helps to filter out processes that recognise patterns and data by agents, collaborative perspectives or numerous information sources. Most recommender systems use collaborative filtering for pattern recognition.

11) Why do we use A/B Testing?

Ans. The purpose of A/B testing is to keep track of any changes made to a website to increase the strategy outcome. It is a statistical hypothesis testing method required for randomised experiments that uses two variables, i.e., A and B.

12) How would you define a P-value? What significance does a P-value have?

Ans. The probability that an observation is made regarding a data set is a random chance is what the p-value expresses. If the p-value is under 5% then it is strong evidence which supports the findings against the null hypothesis. So the higher the p value the likelihood of the result being valid decreases.

13) What is meant by linear regression and logistic regression?


Linear RegressionIn linear regression, we use statistical methods to predict the score of variable Y in comparison to the score of variable X. Here, X is the predictor variable whereas Y is the criterion variable.
Logistic RegressionLogical regression, otherwise known as logistic regression is also a statistical method. This is the technique for binary outcome prediction from a linear combination of predictor variables.

14) Can you elaborate on Eigenvalue and Eigenvector?

Ans. We can make sense of linear transformation through eigenvectors. As data scientists, we need to be able to calculate eigenvectors for correlation and covariance matrix. With eigenvalues, we get directions with linear transformation through flipping, compressing and stretching.

15) Explain what you mean by Dropout

Ans. In data science, a dropout is a tool that we use to drop off a network’s visible and hidden units randomly. Dropout can help in the prevention of data overfitting by dropping by 20% of the nodes. Hence, we get all the space that you need for the arrangement of iterations we need for network convergence.

16) Explain what Artificial Neural Networks mean to you.

Ans. A special set of algorithms that helped in the machine learning revolution is what Artificial Neural Networks entail. We get the best possible result that the network can generate without any output criteria redesigning.

17) Would you need to update the algorithm in Data science? If yes, when?

Ans. In certain cases, we need to update the algorithm. Two of such cases are-

i) When you need data model evolution to the data stream by the infrastructure

ii) Change in the underlying data source

18) What are some of the common algorithms?

Ans. Usually, data scientists use four common algorithms which are namely linear regression, random forest, logical regression and KNN.

19) Can you explain what is meant by hyperparameters?

Ans. As the name suggests, hyperparameters are a type of parameter. We set its value before the learning process to identify the requirements of network training. More so, we can improve the network structure as well. It involves learning rate, recognition of hidden units, epochs, etc.

20) Do you know how an error is different from a residual error?

Ans. When we measure the deviation of an observed value from the true value, it is called an error. Whereas, on the other hand, a residual error is what determines the deviation of an observed value from the estimated value of a particular point in data spread.

21) Please explain what you understand as univariate analysis.

Ans. Univariate analysis focuses on analyzing a single variable at a time, exploring its distribution, central tendency, dispersion, and other characteristics. It involves techniques such as histograms, box plots, and summary statistics, providing insights into the behavior and properties of individual variables without considering their relationships with other variables.

22) What does a recall mean?

Ans. The rate of true positives relative to the total sum of false negatives and true positives is called recall. In other terms, recall is also known as true positive rate.

23) Are there any disadvantages of using a linear model?

Ans. There are a few disadvantages of using a linear model which is-

Linearity assumption of errors

The lack of solutions to overfitting problems

Linearity cannot be applied to binary

24) Can you state a difference between supervised and unsupervised learning?

Ans. On one hand, we have supervised learning where we infer a function from labelled training data. It is a kind of machine learning. Training data comes with a training set as an example.

On the other hand, we have unsupervised learning that refers to inferences drawn from data. The dataset contains input data without any labelled responses. Unsupervised learning, on the other hand, is when inferences are drawn from datasets.

25) What do you mean by 'naive' in Naive Bayes algorithm?

Ans. The Bayes Theorem is the core of the Naive Bayes Algorithm. We get to know the probability of the occurrence of an event. The probability is based on prior information of a condition that is related to a particular event.

26) Can you define Back Propagation?

Ans. Back propagation is an important part of neural net training. It can be defined as eight tuning of the neural network which depends on the error rate coming from earlier epochs.

27) Define an epoch.

Ans. In data science, an epoch refers to a single iteration of the whole dataset. Epoch consists of everything that we can apply to a learning model.

28) Do you know what a Random Forest is?

Ans. A machine learning method that is useful for regression and classification tasks is what we mean by random forest. Usually, we can also apply random forest for treating outliers and missing values.

29) What is the difference between a random forest and a decision tree?

Ans. A decision tree is a single tree-like structure where each node represents a decision based on features, while a random forest is an ensemble method consisting of multiple decision trees trained on random subsets of data and features, averaging predictions to improve generalization and reduce overfitting.

30) In a clustering algorithm, how do you determine the number of clusters?

Ans. We can determine the number of clusters in a clustering algorithm using the elbow method and silhouette.

31) Can you elaborate on Normal Distribution?

Ans. A set of continuous variables that spread across a bell-shaped curve or a normal curve is what we mean by a normal distribution. It is an important concept in statistics that is popularly called continuous probability distribution. We can analyse variables and how they interact through the distribution.

32) What is the purpose of sampling?

Ans. Sampling is done to find a smaller pool of data that is representative of a bigger population.

33) Do you think that treating a categorical variable as a continuous variable would have a better predictive model?

Ans. Yes, converting a categorical variable to a continuous variable results in a better predictive model. However, a categorical value can only be treated as a continuous variable when it is at an ordinal level.

The following are some machine learning interview questions and answers-

34) Can you name the different kinds of clustering algorithms?

Ans. Some of the types of clustering algorithms are K nearest neighbour (KNN), K-means clustering and fuzzy clustering.

35) If you were to choose from machine learning algorithms K-means clustering Linear regression K-NN (k-nearest neighbour) Decision trees for inputting missing values of both categorical and continuous variables?

Ans. Using the K nearest neighbour (KNN) and K-means clustering linear regression to input missing values of both continuous and categorical variables should be the choice.

36) What does the ROC Curve mean?

Ans. The ROC curve refers to measuring the performance of problem classification at different thresholds. It is a probability curve representing the measure or degree of separability.

37) Can you explain how a ROC Curve works?

Ans. The Receiver Operating Characteristic (ROC) curve illustrates the trade-off between true positive rate (sensitivity) and false positive rate (1-specificity) across various classification thresholds. It helps assess the performance of a binary classifier, indicating its ability to discriminate between classes. The curve’s shape and area under it reveal the classifier’s effectiveness: a curve closer to the top-left corner indicates better discrimination between classes.

38) How do you distinguish between "long" and "wide" format data?

Ans. In wide format data, for every data point, we have a single row with several columns that consist of various attribute values. In the long format, we have an equal number of data points and several attribute rows. The rows have particular values for every attribute for a given data point.

39) Describe what you mean by Ensemble Learning. Elaborate on its types.

Ans. Ensemble learning refers to clubbing several machine learning classifiers or weak learners that use aggregation to predict the result. We often see that classifiers have better results when aggregated rather than individually. Random forest classifier is an example of ensemble learning.

40) Can you tell the difference between type I and type II errors?

Ans. Type I and type II errors are important aspects of hypothesis testing. Type I error refers to the occurrence of a false positive, which means that our findings predicted a positive result that was an error. Whereas, a false positive comes under type II error where we mistake a negative result to be positive.

41) Do you know how to treat outlier values?

Ans. Data analysis filters outliers or values that do not fit certain criteria. We can use filters in the data analysis tool for automatic outlier elimination. But there might be situations where outliers do provide insights about low percentage possibilities. If such an occasion arises, then analysts can group the outliers to study them separately.

42) Are there any instances where you can find a false negative holding more importance than a false positive?

Ans. In cases where the predictions are related to a disease or medical testing based on symptoms, a false negative is more important than a false positive.

43) How can you determine MSE and RMSE in a linear regression model?

Ans. The squared sum of all data points gives us the mean square error or MSE. With MSE, we can find the approximate total square sum of errors. On the other hand, the squared sum of errors is what the root mean square (RMSE) calculates.

44) What do you mean by Confounding Variables?

Ans. Sometimes you might encounter a third variable while investigating the relationship between two other variables, a cause and its effect. This variable can impact both the cause and effect variables. This third variable is termed a confounding variable.

45) What do you think are the benefits of applying statistics as a data scientist?

Ans. A data scientist can use statistics to have a better understanding of a user’s expectations. Statistics give a clear idea about user behaviour, interest, engagement, retention and more. This knowledge helps in developing powerful data models for validating particular predictions and inferences.

46) How can you define deep learning?

Ans. Deep learning draws inspiration from the structure of an artificial neural network (ANN) that is concerned with algorithms.

47) Can you explain root cause analysis?

Ans. Root cause analysis refers to the process of how we use data to recognise an underlying pattern that results in a particular change.

48) Can you state an example of a data set with a non-gaussian distribution?

Ans. The variable of height in a population can be an example of a data set in a non-gaussian distribution.

49) What does LSTM stand for? What is the function of LSTM?

Ans. LSTM is the acronym for long short-term memory. LSTM can learn long-term dependencies as a recurrent neural network. It can recall information as default for a longer period.

50) Which algorithm will be most appropriate for studying a population's behaviour similar to four individual types?

Ans. Kmeans clustering is the most appropriate algorithm used in such a case because we need grouping based on four similarities that show the value of k. We recommend you go through and explore more resources about data science like workshops and courses. We mentioned questions recruiters might ask you to see whether you have clarity of the basics or practical knowledge.

The Rise of Data Science

When the world was battling with the COVID-19 pandemic, in 2020, businesses were dealing with unforeseen challenges. Such trying situations compelled the tech industry to find better solutions to problems. To be more particular, data science was on the rise with a maximum of 50% demand in industries worldwide.

However, the rise of data scientists plummeted quickly. In 2022 and 2023, soon after the pandemic subsided there was a drastic change in the data science business. The initial spree of recruitment died down when the layoff season came in.

During the layoff season, big companies let go 90% of their employees. The market was harsh for both an experienced and entry-level data scientist. There were more than 5,00,000 layoffs over 2 years. 30% of these layoffs were from data science and engineering roles.

Nevertheless, the market is finally becoming stable even with decreased job opportunities. There is still demand for specialised job roles. Experienced data scientists with good programming languages like Python are of high value.

Is there a demand for Data Scientists In India?

The demand for data scientists is quite high in India. As of now, there are approximately 50,000 job postings for data scientists. Even in the years to come, we can see a projected rise at a 14% rate by the year 2026. The growth rate is due to the increase in data usage in industries like finance, healthcare and e-commerce.

What are the recent trends in the salary of a data scientist in 2024?

In India, the average salary of a data science professional is approximately $689,412 each year. However, the amount is subject to several other factors like location and level of experience. An entry-level professional can expect around $500,000 per year which can rise to $610,811 per year.

To Wrap Up

The information technology sector is an evolving industry where data science plays an important role. Most of the aspirants prepare rigorously for the interviews for any challenging data science questions. Here, you have a bunch of questions which you should know about data science. To prepare well, a simple search for data science course near me could do the trick. But if you are confused, explore Great Learning Minds (GLM) which offers both training and employment. Be focused and get placed with GLM!

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