Top 80 Data Science Interview Questions & Answers


The "Sexiest Job of the Twenty-First Century," according to Harvard Business Review, is a data scientist. It was ranked first on Glassdoor's list of the 25 Best Jobs in America. The year 2020 was projected by IBM to witness a surge in demand of this position by 28 percent. It should come as no surprise that data scientists are becoming rock stars in the new era of big data and machine learning. Companies that can use vast volumes of data to improve the way they service consumers, produce products, and operate their operations will fare well in this economy.

And, if you're pursuing a career as a data scientist, you'll need to be ready to impress potential employers with your knowledge. And in order to do so, you'll need to be able to ace your next data science interview in one sitting! We've compiled a list of the most often asked data science interview questions so you can prepare for your next interview!

We've compiled a list of the most often requested Data Science interview questions for both newcomers and seasoned professionals in this article.

Below is a list of the most common data science interview questions you should expect to be asked as well as how to construct your responses.

Technical Data Science Interview Questions and Answers

1. Differentiate Supervised learning from Unsupervised learning.

Supervised Learning: Input data is known and labeled. There is a feedback mechanism in supervised learning. Decision trees, logistic regression, and support vector machines are the most often used supervised learning algorithms. Unsupervised Learning: Input data is unlabeled. There is no feedback mechanism in unsupervised learning. K-means clustering, hierarchical clustering, and the apriori algorithm are the most often used unsupervised learning techniques.

2. What is logistic regression and how does it work?

Logistic regression is another statistical tool that machine learning has adopted.
It's the method of choice for binary classification issues -problems with two class values. By estimating probability using its underlying logistic function, logistic regression evaluates the connection between the dependent variable and one or more independent variables.

3. How do you keep your model from overfitting? 

Overfitting refers to a model that has only been trained on a small amount of data and ignores the bigger picture. 

There are three fundamental strategies for avoiding overfitting: Reduce the number of variables in the model to help reduce some of the noise in the training data. 

Cross-validation techniques like k folds cross-validation should be used.
Use regularization techniques like LASSO to punish particular model parameters that are prone to overfitting.

4. Describe the different analyses in univariate, bivariate, or multivariate.

Univariate data consists of only one variable. The goal of the univariate analysis is to characterize the data and discover patterns within it.

The patterns can be investigated using terms like mean, median, mode, dispersion or range, minimum, maximum, and so on.

Bivariate: Two variables are involved in bivariate data. This form of data analysis is concerned with causes and relationships, and it is carried out to determine the relationship between the two variables.

Multivariate: Multivariate data consists of three or more variables and is classified as such. A multivariate analysis is similar to a bivariate analysis, but it includes more than one dependent variable.

Conclusions can be drawn using the mean, median, and mode, as well as dispersion or range, minimum, and maximum.

5. What feature selection approaches are utilized to choose the appropriate variables?

The filter and wrapper methods are the two basic approaches for selecting features.

Filter Method entails:

  • Linear discrimination analysis
  • ANOVAChi-Square

It's all about cleaning up the data coming in when we're limiting or selecting features.

Wrapper Method entails:

Forward Selection: here you test one feature at a time, adding more as needed until you find a satisfactory fit.

Backward Selection: You test all of the features and then begin to remove them to discover what works best.

Feature Elimination in Recursive Mode: Looks at all of the different features and how they work together in a recursive manner.

Wrapper approaches are time-consuming, and if you're doing a lot of data analysis with them, you'll need a powerful computer.

6. What is dimensionality reduction and what are its merits?

The process of turning a data collection with many dimensions (fields) into data with fewer dimensions (fields) in order to convey identical information more clearly is known as dimensionality reduction.

This decrease helps in data compression and reduction of storage space. Additionally, it reduces the computation time since there are lesser dimensions. It eliminates features that are unnecessary; for example, holding a value in two separate units is pointless (meters and inches).

7. What are recommender systems, and how do they work?

Based on the user's choices, a recommender system predicts how a user will evaluate a certain product. The process can be categorized into two sections- Collaborative filtering and content-based filtering.

Filtering by Collaboration:, for instance, recommends tracks that other users with similar tastes listen to frequently. Customers may notice the following message accompanied by product recommendations after completing a purchase on Amazon: "Users who bought this also bought..."Filtering based on content: Pandora, for instance, uses a song's qualities to suggest music with comparable properties. Instead of looking at who else is listening to music, we focus on the substance.

8. What is the best way to choose k for K-means?

The elbow approach is used in choosing the K for K-means. This elbow approach functions on the data set by performing K-means clustering. The K here denotes the number of clusters. In the sum of squares, it is defined as the sum of the squared distances between each member of the cluster and its centroid (WSS).

9. What can be done about outlier values?

Outliers can only be removed if the value is garbage.
For instance- Adult height = abc ft. This isn't possible because the height can't be a string value. Outliers can be deleted in this scenario.

The outliers can be deleted if their values are excessive. For example, if all of the data points are clustered between zero and ten, but one is at one hundred, we can eliminate that point.

10. Explain the ROC curve, and how does it work?

The ROC curve is a graph that shows the True Positive Rate on the y-axis and the False Positive Rate on the x-axis. It is used in binary classification.

The ratio between False Positives and the total number of negative samples is used to compute the False Positive Rate (FPR), whereas the ratio between True Positives and the total number of positive samples is used to get the True Positive Rate (TPR).

The TPR and FPR values are displayed on several threshold values to create the ROC curve. The area range under the ROC curve varies from 0 to 1. The ROC of a perfectly random model, represented by a straight line, is 0.5. The model's efficiency is determined by the amount of divergence a ROC has from this straight line.

Data Science Interview Questions - Basic Concepts

11. What are feature vectors and how do you use them? 

An item is represented by a feature vector, which is an n-dimensional vector of numerical features. Feature vectors are used in machine learning to describe numeric or symbolic qualities also called features of an item in a mathematically easy to analyze fashion.

12. What is root cause analysis, and how does it work?

Originally created to investigate industrial accidents, root cause analysis is now widely employed in a variety of fields. It's a problem-solving method for determining the source of flaws or problems. If a factor's deduction from the problem-fault-sequence prevents the final unwanted event from occurring, it is considered a root cause.

13. What are recommender systems? 

Recommender systems are a type of information filtering system that is designed to forecast a user's preferences or ratings for a product.

14. Define Cross-validation and explain its functionality.

Cross-validation is a model validation approach for determining how well the results of a statistical investigation will generalize to another set of data. It's mostly employed in situations when the goal is to forecast and the user wants to know how accurate a model will be in practice.

The purpose of cross-validation is to create a data set to test the model in the training phase i.e. validation data set in order to avoid issues like overfitting and get insight into how the model will generalize to a different data set.

15. Explain how collaborative filtering works?

By integrating views, numerous data sources, and multiple agents, most recommender systems use this filtering process to uncover patterns and information.

16. What is the use of A/B testing?

For randomized experiments with two variables, A and B, statistical hypothesis testing is used. A/B testing is used to identify any modifications to a web page in order to maximize or improve the outcome of a strategy.

17. What are the demerits of using a linear model?

  • The linearity of the mistakes assumption.
  • It can't be utilized for binary or count results.
  • There are some overfitting issues that it is unable to resolve.

18. Explain the concept of  LLN.

LLN stands for the law of large numbers. It's a theorem that outlines the outcome of repeating an experiment many times. Frequency-style thinking is based on this theorem. The sample mean, sample variance, and sample standard deviation all converge to the value they're trying to approximate.

19. What are the variables that can cause confusion?

Extraneous variables in a statistical model are variables that have a direct or inverse relationship with both the dependent and independent variables. The confounding factor is not taken into account in the estimate.

20. How often should an algorithm be updated?

You may need to update an algorithm when the following cases arise:
You want to develop or upgrade a model as data flows occur through infrastructure
There is a shift in the origin or source of the underlying data
There is a non-stationarity case.

21. Explain a star schema?

It's a classic database schema with a single central table. Satellite tables relate IDs to physical names or descriptions and can be linked to the central fact table via ID fields; these tables are known as lookup tables and are most helpful in real-time applications since they save a lot of memory. To recover information faster, star schemas may use numerous layers of summarization.

22. What is the difference between an eigenvalue and an eigenvector?

The directions along which a linear transformation occurs by flipping, compressing, or stretching are known as eigenvalues. While eigenvectors are used to understand linear transformations.  In data analysis, the eigenvectors of a correlation or covariance matrix are commonly calculated.

23. What is the purpose of resampling?

Resampling is employed and applied in the following situations:

  • Using subsets of accessible data to estimate the accuracy of sample statistics, or drawing randomly with replacement from a set of data points
  • When running significance tests, substituting labels on data points
  • Employing random subsets like bootstrapping/ cross-validation to validate models

24. How would you define selection bias?

In general, selection bias is a problem in which inaccuracy is created owing to a non-random population sample.

25. What are the many sorts of sample biases that can occur?

  • Bias in selection
  • Bias in the media
  • Survivorship prejudice

26. What is the meaning of survivorship bias?

The logical fallacy of focusing on variables that promote surviving a process while carelessly neglecting those that did not due to their lack of prominence is known as survival bias. This has the potential to lead to inaccurate conclusions in a variety of ways.

27. How would you describe Markov chains?

According to Markov Chains, a state's future probability is solely determined by its existing state.

The type of process that Markov chains belong to is the stochastic process.
The system of word recommendation is an excellent example of Markov Chains. The model recognizes and proposes the next word in this system based on the immediately preceding word and nothing else. The Markov Chains use the previous paragraphs, which are similar to training data sets, to generate recommendations for the current paragraphs based on the previous word.

28. What are the uses of  R in Visualization?

R is used in data visualization for a variety of reasons as follows:

  • R allows us to design practically any sort of graph.
  • R comes with a number of libraries, including lattice, ggplot2, leaflet, and many other functions.
  • In comparison to Python, it is easier to customize graphics in R.
  • R may be used for feature engineering as well as exploratory data analysis.

29. Distinguish histogram from a box plot?

Box plots and histograms are both visual representations of the frequency of a feature's values.

Boxplots are more commonly used to compare multiple datasets because they take up less space and contain fewer features than histograms. Histograms are used to determine and comprehend the probability distribution of a dataset.

30. What is NLP?

NLP stands for Natural Language Processing. It is concerned with the study of how computers use programming to learn a large amount of textual data. Stemming, Sentimental Analysis, Tokenization, and the removal of stop words are all instances of NLP.

31. How will you explain Normal Distribution?

Gaussian Distribution is another name for Normal Distribution. It's a form of symmetrical probability distribution around the mean. It demonstrates that the data is closer to the mean and that the frequency of occurrences in the data is significantly lower than the mean.

32. How will you deal with data that has missing values?

There are numerous approaches to dealing with missing values in a dataset:

  • You may reduce the values
  • You may opt to delete the observer however this choice is not always recommended
  • Replace the value of the observation by mean, median, and mode.
  • Using regression to predict the value
  • Find the accurate value with clustering

33. Differentiate Long-data and Wide-data formats.

Long-data: Each row of the data represents a subject's one-time information. Each subject's data would be organized in different or multiple rows.

Recognition of data can be made by considering rows as groupings.
Long-data format is most typically employed in R analysis and for writing to log files at the end of each experiment.

Wide-Data: The subject's repeated responses are divided into various columns 
Recognition of data is made by considering columns as groupings.
This data format is most widely used in stats packages for repeated measures ANOVAs and is rarely utilized in R analysis.

34. What does it imply to have high and low p-values? 

A p-value is a measure of the likelihood of getting outcomes that are equal to or greater than those obtained under a certain hypothesis, assuming the null hypothesis is true. This indicates the likelihood that the observed discrepancy occurred by coincidence. If the p-value ≥ 0.05, the null hypothesis can be rejected, and the data is unlikely to be true null. The strength in support of the null hypothesis is indicated by a high p-value, i.e. values ≥ 0.05. It indicates that the data is true null. The hypothesis can go either way with a p-value of 0.05.

35. Define "imbalanced data"

When data is spread unequally across several categories, it is said to be highly unbalanced. These datasets produce inaccuracies in the model as well as performance issues. 

36. Is there a distinction between the expected and mean values? 

Although there aren't many variations between these two, it's worth noting that they're employed in different situations. In general, the mean value refers to the probability distribution, whereas the anticipated value is used when dealing with random variables. 

37. What is Survivorship Bias? 

Due to a lack of prominence, this bias refers to the logical fallacy of focusing on parts that survived a procedure while missing those that did not. This bias can lead to incorrect conclusions being drawn.

38. Define the terms key performance indicators (KPIs), lift, model fitting, robustness, and DOE. 

KPI stands for Key Performance Indicator, which is a metric that measures how successfully a company meets its goals. Lift is a measure of the target model's performance when compared to a random choice model. The lift represents how well the model predicts compared to if there was no model. Model fitting is a measure of how well the model under consideration fits the data. Robustness: This refers to the system's ability to successfully handle differences and variances. DOE stands for the design of experiments, and it refers to the task of describing and explaining information variance under postulated settings in order to reflect factors.

39. What are Confounding variables? 

Confounders are another term for confounding variables. These variables are a form of extraneous variable that has an impact on both independent and dependent variables, generating erroneous associations and mathematical correlations between variables that are related but not incidentally.

40. Give the distinctions of time series difficulties from other regression problems?

Time series data can be thought of as an extension of linear regression, which employs terminology such as autocorrelation and average movement to summarise previous data of y-axis variables in order to forecast a better future.

The major purpose of time series issues in forecasting and prediction is when precise predictions can be produced but the underlying reasons are not always known. The inclusion of Time in a problem does not necessarily suggest that it is a time series issue.
 For a problem to become a time series problem, there must be a relationship between target and time.

The observations that are close in time are expected to be similar to those that are far away, providing seasonality accountability. Today's weather, for example, would be similar to tomorrow's weather but not to weather four months from now. Hence, weather forecasting using past data becomes a time series difficulty.

41. Explain Cross-Validation, and how does it work?

A cross-validation is a statistical approach for enhancing the performance of a model. To ensure that the model performs adequately for unknown data, it will be trained and tested with rotation using different samples of the training dataset. The training data will be divided into groups, and the model will be tested and verified against each group in turn.

The most widely and commonly utilized methods include:

  • K- Fold method
  • Leave p-out method
  • Leave-one-out method
  • Holdout method

42. What's the distinction between correlation and covariance?

The following are the distinctions between these two terms, which are used to construct a relationship and reliance between any two random variables:

Correlation is a technique used to assess and quantify the quantitative relationship between two variables, with the strength of the link being measured in terms of how closely the variables are related. While the extent to which the variables change together in a cycle is referred to as covariance. This explains the systematic link between two variables, in which changes in one variable influence changes in the other.

43. How do you go about tackling a data analytics project?

In general, the below steps can be followed:

  • The first stage is to fully comprehend the company's need or problem.
  • Then, carefully examine and analyze the data you've been given. In the event of any missing data missing clarify the needs by contacting the company.
  • The following stage is to clean and prepare the data, which will then be used for modeling. The variables are transformed and the missing values are available here.
  • To acquire useful insights, run your model on the data, create meaningful visualizations, and analyze the findings.
  • Release the implemented model and assess its utility by tracking the outcomes and performance over a specified time period.
  • Validate the model using cross-validation.

44. Why is data cleaning so important?

To get good insights while running an algorithm on any data, it is critical to have correct and clean data that contains only essential information. Poor or erroneous insights and projections are frequently the product of contaminated data, which can have disastrous consequences.

45. Does considering categorical variables as continuous variables give a stronger prediction model?

Yes! A categorical variable is one that has no particular category ordering and can be allocated to two or more categories. Ordinal variables are comparable to categorical variables in that they have a defined and consistent ordering. If the variable is ordinal, interpreting the category value as a continuous variable will lead to more accurate predictive models.

46. Differentiate validation set from test set?

The test set is essentially utilized to test and assess the trained model's performance. It evaluates the model's prediction ability.

While the validation set is a subset of the training set that is used to choose parameters to avoid overfitting the model.

47. What do you mean by kernel trick?

Kernel functions are generalized dot product functions that are utilized in high-dimensional feature space to compute the dot product of vectors xx and yy. Kernal trick approach is used to solve a non-linear problem by changing linearly inseparable data into separable data in higher dimensions using a linear classifier.

48. Is it better to use a random forest or a series of decision trees?

Because random forests are an ensemble method that ensures numerous weak decision trees learn forcefully, they are far more robust, accurate, and less prone to overfitting than multiple decision trees.

49. Give an example of a situation in which both false positives and false negatives are equally essential.

In the banking industry, lending loans are the primary source of revenue for banks. However, if the payback rate isn't good, there's a chance of big losses rather than earnings. Giving out loans to consumers is thus a gamble, as banks cannot afford to lose good customers while also being unable to afford to gain bad customers. This is a typical example of the importance of both false positive and false negative scenarios in false positive and false negative scenarios.

50. Is it necessary to reduce the dimensionality of a Support Vector Model before fitting it? 

When the number of features exceeds the number of observations, dimensionality reduction enhances the SVM (Support Vector Model). Hence, it is necessary.

51. What are the different assumptions in linear regression? What happens if they are breached?

The following assumptions are made when performing linear regression:

  • The population is represented by the sample data utilized in the modeling.
  • Between the X-axis variable and the mean of the Y variable, there is a linear relationship.
  • For each X value, the residual variance is the same. This is referred to as homoscedasticity.
  • The observations are unrelated to each other.
  • For each value of X, Y follows a normal distribution.
  • Excessive deviations from the above assumptions result in redundant outcomes. The variation or bias of the estimations increases as these are broken down.

52. What role does regularisation play in feature selection?

Regularization is the method of assigning penalties to various parameters in a machine learning model to decrease the model's freedom and reduce overfitting. There are multiple regularization methods available like Lasso L1, Linear regularization, etc. 

The predictors are multiplied by a penalty given to the coefficients in the linear model regularisation. The Lasso/L1 regularisation has the property of decreasing some coefficients to zero, allowing them to be eliminated from the model.

53. How can you tell whether a coin is skewed?

To determine this, we use the following hypothesis test:

If the probability of head flipping is 50%, the coin is unbiased, according to the null hypothesis. The coin is biassed, according to the alternative hypothesis, and the probability is not equal to 500. Follow the instructions below:

  • Flipping the coin 500 times
  • Determine the p-value.
  • When the p-value is compared to the alpha, the result of a two-tailed test (0.05/2 = 0.025) is obtained.

The following two scenarios are possible:

  • If the p-value is greater than alpha, the null hypothesis is true, and the coin is unbiased.
  • The null hypothesis is rejected, and the coin is skewed as a result of the p-value alpha.

54. Define p-value?

The statistical significance of observation is measured by the P-value. The probability shows how important the finding is in relation to the data. The p-value is used to calculate the test statistics for a model. It usually helps us decide whether the null hypothesis should be accepted or rejected.

55. How is an error different from a residual mistake?

A value error occurs, whereas a prediction depicts the difference between the dataset's observed and true values. On the other hand, the residual error is the difference between the observed and projected values. Because the true values are never known, we utilize the residual error to assess an algorithm's performance. Hence, we apply residuals to determine the degree of inaccuracy based on the observed data. It aids us in calculating an exact estimate of the error.

56. In Data Science, what is a bias-variance trade-off?

The goal of using Data Science or Machine Learning is to create a model with low bias and variance. We all know that bias and variance are errors that emerge when a model is either simple or overly sophisticated. Consequently, while developing a model, the possibility of achieving high accuracy is when there is a concrete understanding of the tradeoff between bias and variance.

When a model is too simple to capture the patterns in a dataset, it is said to be biased. Hence to reduce bias, the model should be made complicated. Although increasing the model's complexity can reduce bias, if the model becomes too complicated, it might become stiff, resulting in large variance. As a result, the tradeoff between bias and variance is that as complexity increases, the bias decreases, and when the variance increases, the complexity decreases. The bias increases while the variance decreases. Hence, the goal should be to establish a balance between a model that is complicated enough to produce minimal bias but not so complex that it produces significant variance.

57. Explain RMSE?

It is the root mean square error. It's a metric for regression accuracy. The root means square error (RMSE) is a method for determining the magnitude of a regression model's error. 

The calculation of RMSE can be done by following the steps below:

  • The first step involves the calculation of the errors in the regression model's predictions. This is how the differences between actual and expected numbers are determined.
  • The mistakes are then squared. The mean of the squared errors is next computed, followed by the square root of the mean of these squared errors.
  • This is known as the root mean square error (RMSE), and a model with a lower RMSE is expected to produce fewer errors, meaning that it is more accurate.

58. Explain ensemble learning, and how does it work?

The goal when applying Data Science and Machine Learning to build models is to create a model that can understand the underlying trends in the training data and make accurate predictions or classifications. However, some datasets are exceedingly complex, and understanding the underlying trends in these datasets might be difficult for a single model. Sometimes in the attempt to boost performance, multiple unique models are merged and this method is known as ensemble learning.

59. What is the concept of Bagging in Data science? 

Bagging is a type of ensemble learning. Bootstrap aggregation is what it's called. Some data are generated with this methodology by employing the bootstrap method, which uses an existing dataset to generate many samples of the N size. The bootstrapped data is then utilized to train many models simultaneously, resulting in a more robust bagging model than a simple model. For making a prediction, trained models are essentially employed and then average the results in the case of regression, and in the case of classification, the result provided by the models with the highest frequency is selected.

60. What is boosting in Data Science?

One of the ensemble learning strategies is boosting. It is not, unlike bagging, a technique for simultaneously training our models. In boosting, we construct a large number of models and train them sequentially by iteratively combining weak models so that the training of a new model is dependent on the training of prior models.

We use the patterns learned by the previous model to train the new model and test it on a dataset. In each iteration, we provide more weight to observations in the dataset that were incorrectly handled or predicted by past models. Boosting can be additionally used for reducing model bias.

61. What is stacking in Data Science?

Stacking is an ensemble learning strategy, similar to bagging and boosting. In bagging and boosting, we could only combine weak models that used the same learning techniques, such as logistic regression. It also goes by the name Homogeneous learners.

Stacking, on the other hand, enables us to mix weak models that employ various learning methods. . It also goes by Heterogeneous learners. Stacking works by training many (and diverse) weak models or learners, then combining them by training a meta-model to make predictions based on the multiple outputs of these multiple weak models.

62. What are the various kernel functions available in SVM?

The varied Kernel functions in SVM includes the following:

  • Linear Kernel
  • Polynomial Kernel
  • Radial basis Kernel
  • Sigmoid shape Kernel

63. Explain reinforcement learning, and how does it work?

Reinforcement learning is a subset of Machine Learning that focuses on creating software agents that do behaviors in order to maximize the number of cumulative rewards.

Here, a reward is utilized to inform the model during training whether a specific activity leads to the achievement of or puts it closer to the objective. 

Reinforcement learning is employed to develop these types of agents that can make real-world decisions to aid the model to achieve a clearly defined goal.

64. Explain the concept of TF/IDF vectorization.

Term Frequency–Inverse Document Frequency is abbreviated as TF/IDF. It's a numerical metric for determining how important a word is to a document in a corpus of papers. TF/IDF is frequently used in text mining and information retrieval.

65. Which one is better for text analytics: Python or R?

When it comes to working with text data, both Python and R have a lot to offer. R has a large number of text analytics libraries, but its data mining libraries are still in their infancy. Python is best suited for use at the enterprise level and to boost software productivity. R has a large number of support packages for dealing with unstructured data. Python excels at managing massive amounts of data, but R has memory limits and is slower to respond to big amounts of data. As a result, whether to use Python or R relies on the functionality and application.

66. What is the difference between AUC and ROC? 

The AUC curve is a comparison of precision and recall. Precision is calculated using the formulas TP/(TP + FP) and TP/(TP + FN). ROC, on the other hand, measures and plots True Positive over False Positive Rate.

67. Define confusion matrix.

A confusion matrix is a table that shows how well a supervised learning system performs. It gives a summary of categorization problem prediction outcomes. You can use the confusion matrix to not only determine the predictor's errors, but also the types of errors.

68. What distinguishes Deep Learning from Machine Learning?

Machine Learning is a subset of Deep Learning. It's a subfield of machine learning that focuses on creating algorithms that replicate the human nervous system. Deep Learning entails the use of neural networks that have been trained on massive datasets to understand patterns and then conduct classification and prediction.

69. What causes descent algorithms to always lead to the same result?

This is due to the fact that they sometimes reach a local or local optima point. The methods aren't always successful in achieving global minima. This is also reliant on the data, the velocity of fall, and the point of descent's origin.

70. Describe box-cox transformation?

The Box-Cox transformation is used to convert the response variable so that the data matches the required assumptions. This technique can be used to convert non-normal dependent variables into normal shapes. With the aid of this transformation, we can run a larger number of tests.

71. Explain the concept of the Curse of dimensionality

There are times when the amount of variables or columns in the dataset is excessive while evaluating it. We are, however, only allowed to extract significant variables from the group. Consider the fact that there are a thousand features. However, we only need to extract a few key characteristics. The 'curse of dimensionality refers to the dilemma of having many features when only a few are required.

72. Differentiate recall from precision.

The fraction of instances that have been categorized as true is known as recall. Precision, on the other hand, is a metric for weighing instances that are genuinely true. Precision is a genuine value that shows factual information, whereas recall is an estimate.

73. Explain the pickle module?

The pickle module is used to serialize and de-serialize objects in Python. Pickle is used for saving this object to the hard drive. It converts a character stream from an object structure.

74. Compare and contrast the DELETE and TRUNCATE commands.

To delete some rows from a table, use the DELETE command in conjunction with the WHERE clause. This action has the ability to be reversed.

TRUNCATE, on the other hand, is used to delete all the rows in a table, and this action cannot be reversed.

75. What are the most common SQL clauses?

The following are some of the most commonly used SQL clauses:


76. Define foreign key? 

A foreign key is a unique key that belongs to one database but can also be used as the main key in another. We reference the foreign key with the primary key of the other table to create a relationship between the two tables.

77. What is the use of "data integrity"? 

Data integrity allows us to define the data's accuracy as well as consistency. This integrity must be maintained throughout the life of the product.

78. What role does Hadoop play in Data Science? 

Hadoop allows data scientists to work with vast amounts of unstructured data. Furthermore, new Hadoop extensions like Mahout and PIG offer a variety of functionalities for analyzing and implementing machine learning algorithms on massive data sets. As a result, Hadoop is a complete system capable of processing a wide range of data types, making it an ideal tool for data scientists.

79. What are the different kinds of selection biases?

The numerous types of selection bias are as follows:

  • Sampling Bias: is a systematic error that occurs when a non-random sample of a population causes some individuals to be less likely to be included than others, resulting in a biased sample.
  • Time Interval- Even when all variables have a similar mean, a trial may be stopped at an extreme value for ethical concerns. However, the extreme value is more likely to be reached by the variable with the largest volatility.
  • Data - The results of randomly selecting specific data subsets to support a conclusion or rejecting faulty data.
  • Attrition - Attrition is defined as the loss of participants, the discounting of trial subjects, or the failure to complete testing.

80. Describe how the Sensitivity of machine learning models can be calculated?

Sensitivity is used in machine learning to validate the accuracy of classifiers like Logistic, Random Forest, and SVM. It's also known as TPR or REC (recall) (true positive rate).
The ratio of predicted real occurrences to total events is known as sensitivity.
True Positives / Positives in the Actual Dependent Variable = Sensitivity
True events are those that happened exactly as a machine learning model predicted. The highest sensitivity is 1.0, while the lowest is 0.0.


Those are the most important questions centered around data science Interviews that we have curated for prospective candidates. It will ideally help them significantly and aspiring individuals must be well-informed in all the key areas before venturing into the career path. The above data science interview questions and answers will help you a great deal in preparing yourself for a data science career.

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