Data Science Interview Questions

Preparing for a data scientist interview is tough since the questions you will be asked about data science are unclear. An interviewer may surprise you with a set of unexpected questions, regardless of how much job experience you have or what data science credentials you possess.

Data Science Interview Questions

Data Science is one of the most well-known and widely used technologies in the world today. Professionals in this sector are being hired by major corporations. Data Scientists are among the highest-paid IT professionals due to strong demand and limited availability. The most commonly asked questions in Data Science job interviews are covered in this Data Science Interview Preparation article.

Preparing for a data scientist interview is tough since the questions you will be asked about data science are unclear. An interviewer may surprise you with a set of unexpected questions, regardless of how much job experience you have or what data science credentials you possess.

During a data science interview, the interviewer will ask a variety of questions on a variety of subjects. Your statistical, coding, and data modeling abilities will be truly tested with a range of questions that are meant to keep you on your toes and require you to showcase how you handle the pressure.

The Ultimate Guide To Crack Your Data Scientist Job Interview
This is the book to read if you want to ace your data scientist interview. This pdf book contains data science interview questions and answers that are gathered from Facebook, Amazon, Apple, Netflix, Microsoft, and Google.

Given below is the list of popular and most common interview questions for the position of data scientist, machine learning engineer, data engineer, and data analyst.

  1. Why do you want to work at this company as a data scientist?
  2. How did your previous work experiences prepare you for a role as a data scientist?
  3. What is Data Science?
  4. How is Data Science different from traditional application programming?
  5. How do you organize big sets of data?
  6. Is having large amounts of data always preferable?
  7. What is artificial intelligence (AI)?
  8. How do artificial intelligence, machine learning, neural networks, and deep learning relate?
  9. What is the difference between machine learning and deep learning?
  10. What are the differences between supervised and unsupervised learning?
  11. What is variance in Data Science?
  12. What is the Central Limit Theorem and why is it important?
  13. What is sampling? How many sampling methods do you know?
  14. What is selection bias and why is it important?
  15. What is resampling, and why is it useful?
  16. What are the types of biases that can occur during sampling?
  17. What is survivorship bias?
  18. What are correlation and covariance?
  19. What is the Null Hypothesis and Alternate Hypothesis? Why is it important?
  20. What is the difference between type I vs type II error?
  21. What is statistical significance and why is it important?
  22. How to calculate statistical significance?
  23. What is p-value?
  24. Why do we use p-value?
  25. What is the significance of p-value?
  26. What is the difference between an error and a residual error?
  27. Differentiate between univariate, bivariate, and multivariate analysis?
  28. Explain how you would find the relationship between a continuous variable and a categorical variable?
  29. What is a normal distribution?
  30. What is meant by Interpolation and Extrapolation?
  31. Two candidates Aman and Mohan appear for a Data Science Job interview. The probability of Aman cracking the interview is 1/8 and that of Mohan is 5/12. What is the probability that at least of them will crack the interview?
  32. How do you treat outlier values?
  33. How do you treat missing values during an analysis?
  34. You are given a data set consisting of variables with more than 30 percent missing values. How will you deal with them?
  35. What is normalization? What is the benefit of normalization?
  36. What is binning? Should we do binning or not?
  37. What is a data balancing problem? Explain some scenarios?
  38. How do you overcome imbalanced classes?
  39. What do you understand about linear regression? What do the terms p-value, coefficient, and r-squared value mean?
  40. How do you interpret the p-values in the linear regression analysis?
  41. How do you interpret the regression coefficients for linear relationships?
  42. How do you interpret the regression coefficients for curvilinear relationships and interaction terms?
  43. What is Root Mean Square Error (RMSE) and why is it important?
  44. What is MAPE? When is it used?
  45. MAE, MSE, RMSE, Coefficient of Determination, Adjusted R Squared — Which Metric is Better?
  46. What is R-Squared?
  47. What are the key limitations of R-Squared?
  48. Are low R-Squared values inherently bad?
  49. Are high R-Squared values inherently good?
  50. What are the assumptions required for linear regression?
  51. What happens when some of the assumptions required for linear regression are violated?
  52. What are the confounding variables?
  53. What is multicollinearity? What problem can it cause? How can you overcome it?
  54. What is the curse of dimensionality?
  55. What is the importance of dimensionality reduction? Why do we need it?
  56. What is goodness-of-fit for a linear model? OR How do you determine if your linear regression model fits certain data?
  57. You created a predictive model of a quantitative outcome variable using multiple regressions. What are the steps you would follow to validate the model?
  58. What technique do you use to predict categorical responses?
  59. How does logistic regression work?
  60. How do you evaluate your classification model? Precision, Recall, Accuracy, Confusion Matrix, and AUC
  61. Write the equation and calculate the precision and recall rate?
  62. We want to predict the probability of death from heart disease based on three risk factors: age, gender, and blood cholesterol level. What is the most appropriate algorithm for this case?
  63. You are given a dataset on cancer detection. You have built a classification model and achieved an accuracy of 96 percent. Why shouldn't you be happy with your model performance? What can you do about it?
  64. Can you cite some examples where a false positive is more important than a false negative?
  65. Can you cite some examples where both false positives and false negatives are equally important?
  66. Will all gradient descent algorithms lead to the same model when working with Logistic or Linear regression problems?
  67. Differentiate between Batch Gradient Descent, Mini-Batch Gradient Descent, and Stochastic Gradient Descent.
  68. What do you understand about a decision tree?
  69. Explain the steps in making a decision tree.
  70. What is Entropy in a decision tree algorithm?
  71. Below are the eight actual values of the target variable in the train file. What is the entropy of the target variable?
  72. What is Gini index in decision tree algorithm?
  73. What is information gain in a decision tree algorithm?
  74. What is pruning in a decision tree algorithm?
  75. Give me an example of when you have used a decision-tree algorithm?
  76. What do you understand by a random forest model?
  77. How do you build a random forest model?
  78. What do you understand about SVM?
  79. What is a kernel function in SVM?
  80. What are different kernels in SVM and what are their uses?
  81. Give some situations where you will use an SVM over a RandomForest Machine Learning algorithm and vice-versa.
  82. How is k-NN different from k-means clustering?
  83. How can we select an appropriate value of k in k-means?
  84. Which of the following machine learning algorithms can be used for inputting missing values of both categorical and continuous variables?
  85. After studying the behavior of a population, you have identified four specific individual types that are valuable to your study. You would like to find all users who are most similar to each individual type. Which algorithm is most appropriate for this study?
  86. For the given points, how will you calculate the Euclidean distance in Python?
  87. What is a neural network?
  88. What is the difference between regression and a neural network?
  89. How is deep learning different from neural networks?
  90. What is an activation function? Describe some common activation functions?
  91. What is an RNN (recurrent neural network)?
  92. What are variant RNN architectures?
  93. How often would you update an algorithm?
  94. Can you explain the difference between a Validation Set and a Test Set?
  95. When doing text analytics, do you prefer Python or R?
  96. How would you design a taxonomy to determine key customer trends from unstructured data?
  97. What are 6 CRISP-DM Phases?
    The CRoss Industry Standard Process for Data Mining (CRISP-DM) is a process model with six phases that naturally describes the data science life cycle.
  98. Can you break down an algorithm that you used recently on a project?
  99. What is the process of testing models? Compare hold-out vs k-fold  cross validation vs iterated k-fold cross-validation methods of testing.
  100. Describe a challenge you have encountered during a project and how you overcame it?
  101. What is the purpose of A/B testing? Describe how you have used A/B testing recently?
  102. What is the Bias-Variance tradeoff? What are ways to handle it?
  103. How can you avoid overfitting your model?
  104. What is ensemble learning? Why should we consider using an ensemble?
  105. Why does Naive Bayes have the word ‘naive’ in it?
  106. What do you understand by conjugate-prior with respect to Naïve Bayes?
  107. What are recommender systems?
  108. Out of collaborative filtering and content-based filtering, which one is considered better, and why?
  109. 'People who bought this also bought…' recommendations seen on Amazon are a result of which algorithm?
  110. You have run the association rules algorithm on your dataset, and the two rules {banana, apple} => {grape} and {apple, orange} => {grape} have been found to be relevant. What else must be true?
  111. Your organization has a website where visitors randomly receive one of two coupons. It is also possible that visitors to the website will not receive a coupon. You have been asked to determine if offering a coupon to website visitors has any impact on their purchase decisions. Which analysis method should you use?
  112. What is bagging in Data Science?
  113. What is boosting in Data Science?
  114. What is stacking in Data Science?
  115. Describe regularization and its importance?
  116. Explain the difference between L1 and L2 regularization methods?
  117. Why does L1 regularization cause parameter sparsity whereas L2 regularization does not?
  118. What are the advantages and disadvantages of using regularization methods like Ridge Regression?
  119. What is root cause analysis?
  120. How to detect if the time series data is stationary?
  121. What is reinforcement learning?
  122. Explain TF/IDF vectorization.
  123. How do data management procedures like missing data handling make selection bias worse?
  124. What are eigenvalue and eigenvector?
  125. How will you calculate eigenvalues and eigenvectors of the following 3x3 matrix?
  126. If you flip a coin 1,000 times and tails show up 575 times, is the coin biased?
  127. How would you explain to senior management why a data set is important?
  128. Do you prefer using Python or R for Data Science? Why?
  129. What are the popular libraries used in Data Science?
  130. What data visualization tools do you like best?
  131. From the below given ‘diamonds’ dataset, extract only those rows where the ‘price’ value is greater than 1000 and the ‘cut’ is ideal.
  132. Make a scatter plot between ‘price’ and ‘carat’ using ggplot. ‘Price’ should be on the y-axis, ’carat’ should be on the x-axis, and the ‘color’ of the points should be determined by ‘cut.’
  133. Introduce 25 percent missing values in this ‘iris’ datset and impute the ‘Sepal.Length’ column with ‘mean’ and the ‘Petal.Length’ column with ‘median.’
  134. Implement simple linear regression in R on this ‘mtcars’ dataset, where the dependent variable is ‘mpg’ and the independent variable is ‘disp.’
  135. Calculate the RMSE values for the model built.
  136. Implement simple linear regression in Python on this ‘Boston’ dataset where the dependent variable is ‘medv’ and the independent variable is ‘lstat.’
  137. Implement logistic regression on this ‘heart’ dataset in R where the dependent variable is ‘target’ and the independent variable is ‘age.’
  138. Build an ROC curve for the model built.
  139. Build a confusion matrix for the model where the threshold value for the probability of predicted values is 0.6, and also find the accuracy of the model.
  140. Build a logistic regression model on the ‘customer_churn’ dataset in Python. The dependent variable is ‘Churn’ and the independent variable is ‘MonthlyCharges.’ Find the log_loss of the model.
  141. Build a decision tree model on ‘Iris’ dataset where the dependent variable is ‘Species,’ and all other columns are independent variables. Find the accuracy of the model built.
  142. Build a random forest model on top of this ‘CTG’ dataset, where ‘NSP’ is the dependent variable and all other columns are independent variables.
  143. Write a function to calculate the Euclidean distance between two points.
  144. Write code to calculate the root mean square error (RMSE) given the lists of values as actual and predicted.
  145. Write code to calculate the accuracy of a binary classification algorithm using its confusion matrix.
  146. Write a function that when called with a confusion matrix for a binary classification model returns a dictionary with its precision and recall.
The Ultimate Guide To Crack Your Data Scientist Job Interview
This is the book to read if you want to ace your data scientist interview. This pdf book contains data science interview questions and answers that are gathered from Facebook, Amazon, Apple, Netflix, Microsoft, and Google.