** Data Science Interview Questions**

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### 100 Data Science Interview Questions and Answers for 2021

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### 100 Data Science Interview Questions and Answers for 2021

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### Tutorial Playlist Top 50 Data Science Interview Questions and Answers for 2021 Data Science Interview Questions on Technical Concepts 1. What are the differences between supervised and unsupervised learning 2. How is logistic regression done 3. Explain the steps in making a decision tree. 4. How do you build a random forest model 5. How can you avoid overfitting your model 6. Differentiate between univariate, bivariate, and multivariate analysis. 7. What are the feature selection methods used to select the right variables 8. In your choice of language, write a program that prints the numbers ranging from one to 50. 9. You are given a data set consisting of variables with more than 30 percent missing values. How will you deal with them 10. For the given points, how will you calculate the Euclidean distance in Python 11. What are dimensionality reduction and its benefits 12. How will you calculate eigenvalues and eigenvectors of the following 3x3 matrix 13. How should you maintain a deployed model 14. What are recommender systems 15. How do you find RMSE and MSE in a linear regression model 16. How can you select k for kmeans 17. What is the significance of pvalue 18. How can outlier values be treated 19. How can timeseries data be declared as stationery 20. How can you calculate accuracy using a confusion matrix 21. Write the equation and calculate the precision and recall rate. 22. People who bought this also bought recommendations seen on Amazon are a result of which algorithm 23. Write a basic SQL query that lists all orders with customer information. 24. You are given a dataset on cancer detection. You have built a classification model and achieved an accuracy of 96 percent. Why shouldnt you be happy with your model performance What can you do about it 25. Which of the following machine learning algorithms can be used for inputting missing values of both categorical and continuous variables 26. Below are the eight actual values of the target variable in the train file. What is the entropy of the target variable 27. 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 28. 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 29. 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 30. 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 Data Science Interview Questions on Basic Concepts 31. What are the feature vectors 32. What are the steps in making a decision tree 33. What is root cause analysis 34. What is logistic regression 35. What are recommender systems 36. Explain crossvalidation. 37. What is collaborative filtering 38. Do gradient descent methods always converge to similar points 39. What is the goal of AB Testing 40. What are the drawbacks of the linear model 41. What is the law of large numbers 42. What are the confounding variables 43. What is star schema 44. How regularly must an algorithm be updated 45. What are eigenvalue and eigenvector 46. Why is resampling done 47. What is selection bias 48. What are the types of biases that can occur during sampling 49. What is survivorship bias 50. How do you work towards a random forest Stay Sharp with Our Data Science Interview Questions Find our Post Graduate Program in Data Science Online Bootcamp in top cities About the Author Recommended Programs Recommended Resources

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Data Science 100 Data Science Interview Questions You Must Prepare for 2021 Data Science Interview Questions Edureka BASIC DATA SCIENCE INTERVIEW QUESTIONS Q1. What is Data Science List the differenc...

### Data Science 100 Data Science Interview Questions You Must Prepare for 2021 Data Science Interview Questions Edureka BASIC DATA SCIENCE INTERVIEW QUESTIONS Q1. What is Data Science List the differences between supervised and unsupervised learning. Q2. What is Selection Bias Q3. What is biasvariance tradeoff Q4. What is a confusion matrix STATISTICS INTERVIEW QUESTIONS Q5. What is the difference between long and wide format data Q6. What do you understand by the term Normal Distribution Q7. What is correlation and covariance in statistics Q8. What is the difference between Point Estimates and Confidence Interval DATA ANALYSIS INTERVIEW QUESTIONS MACHINE LEARNING INTERVIEW QUESTIONS Q40. What is Machine Learning Q41. What is Supervised Learning Q42. What is Unsupervised learning Q43. What are the various classification algorithms Q44. What is Naive in a Naive Bayes Q45. Explain SVM algorithm in detail. Q46. What are the support vectors in SVM Q47. What are the different kernels in SVM Q48. Explain Decision Tree algorithm in detail. Q49. What are Entropy and Information gain in Decision tree algorithm Q50. What is pruning in Decision Tree Q51. What is logistic regression State an example when you have used logistic regression recently. Q52. What is Linear Regression Q53. What Are the Drawbacks of the Linear Model Q54. What is the difference between Regression and classification ML techniques Q55. What are Recommender Systems Q56. What is Collaborative filtering Q57. How can outlier values be treated Q58. What are the various steps involved in an analytics project Q59. During analysis, how do you treat missing values Q60. How will you define the number of clusters in a clustering algorithm Q61. What is Ensemble Learning Q62. Describe in brief any type of Ensemble Learning Q64. How Do You Work Towards a Random Forest Q65. What crossvalidation technique would you use on a time series data set Q66. What is a BoxCox Transformation Q67. How Regularly Must an Algorithm be Updated Q68. If you are having 4GB RAM in your machine and you want to train your model on 10GB data set. How would you go about this problem Have you ever faced this kind of problem in your machine learningdata science experience so far DEEP LEARNING INTERVIEW QUESTIONS Q69. What do you mean by Deep Learning Q70. What is the difference between machine learning and deep learning Q71. What, in your opinion, is the reason for the popularity of Deep Learning in recent times Q72. What is reinforcement learning Q73. What are Artificial Neural Networks Q74. Describe the structure of Artificial Neural Networks Q75. How Are Weights Initialized in a Network Q79. What Is the Difference Between Epoch, Batch, and Iteration in Deep Learning Q80. What Are the Different Layers on CNN Q81. What Is Pooling on CNN, and How Does It Work Q82. What are Recurrent Neural NetworksRNNs Q83. How Does an LSTM Network Work Q84. What Is a Multilayer PerceptronMLP Q85. Explain Gradient Descent. Q86. What is exploding gradients Q87. What is vanishing gradients Q89. What is Back Propagation and Explain its Working. Q90. What are the variants of Back Propagation Q91. What are the different Deep Learning Frameworks Q92. What is the role of the Activation Function Q93. Name a few Machine Learning libraries for various purposes. Q94. What is an AutoEncoder Q95. What is a Boltzmann Machine Q97. What Is the Difference Between Batch Gradient Descent and Stochastic Gradient Descent Q98. Why Is Tensorflow the Most Preferred Library in Deep Learning Q100. What is the Computational Graph Q101. What is a Generative Adversarial Network Q102. What are the important skills to have in Python with regard to data analysis Recommended videos for you 3 Scenarios Where Predictive Analytics is a Must Web Scraping And Analytics With Python Machine Learning with Python Mastering Python An Excellent tool for Web Scraping and Data Analysis Android Development Using Android 5.0 Lollipop Business Analytics with R The Whys and Hows of Predictive ModelingII Data Science Make Smarter Business Decisions Sentiment Analysis In Retail Domain Python Classes Python Programming Tutorial The Whys and Hows of Predictive ModellingI Python Tutorial All You Need To Know In Python Programming Linear Regression With R Application of Clustering in Data Science Using RealTime Examples Python List, Tuple, String, Set And Dictonary Python Sequences Python Loops While, For and Nested Loops in Python Programming Introduction to Business Analytics with R Business Analytics Decision Tree in R Diversity Of Python Programming Python Programming Learn Python Programming From Scratch Recommended blogs for you Matplotlib Tutorial Python Matplotlib Library with Examples How to fetch and modify Date and Time in Python Why R for Marketing Professionals What is CrossValidation in Machine Learning and how to implement it How to Implement Super Function in Python Data Science Tutorial Learn Data Science from Scratch OpenCV Python Tutorial Computer Vision With OpenCV In Python What are Lambda Functions and How to Use Them Top 10 Data Science Certifications in 2021 Your Path to Become a Data Scientist Types of Data Scientist Python Database Connection Know how to connect with database Core Data Scientist Skills Top 50 SAS Interview Questions You Must Prepare In 2021 What is the Average Python Developer Salary What are Stack Data Structures in Python Python Visual Studio Learn How To Make Your First Python Program Top Reasons Why Business Analytics Is A Must For MBA Grads All You Need to Know About Eval in Python What are Generators in Python and How to use them Introduction To Supervised Learning Join the discussionCancel reply Trending Courses in Data Science Data Science Certification Training with Pyth ... Python Programming Certification Training Machine Learning Certification Training Data Science Certification Course using R Data Analytics with R Certification Training Statistics Essentials for Analytics SAS Training and Certification Advanced Predictive Modelling in R Certificat ... Analytics for Retail Banks Decision Tree Modeling Using R Certification ... Browse Categories Subscribe to our Newsletter, and get personalized recommendations.

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### 100 Data Science Interview Questions and Answers For 2021 Edureka

Data Science 100 Data Science Interview Questions You Must Prepare for 2021 Data Science Interview Questions Edureka BASIC DATA SCIENCE INTERVIEW QUESTIONS Q1. What is Data Science List the differenc...

### Data Science 100 Data Science Interview Questions You Must Prepare for 2021 Data Science Interview Questions Edureka BASIC DATA SCIENCE INTERVIEW QUESTIONS Q1. What is Data Science List the differences between supervised and unsupervised learning. Q2. What is Selection Bias Q3. What is biasvariance tradeoff Q4. What is a confusion matrix STATISTICS INTERVIEW QUESTIONS Q5. What is the difference between long and wide format data Q6. What do you understand by the term Normal Distribution Q7. What is correlation and covariance in statistics Q8. What is the difference between Point Estimates and Confidence Interval DATA ANALYSIS INTERVIEW QUESTIONS MACHINE LEARNING INTERVIEW QUESTIONS Q40. What is Machine Learning Q41. What is Supervised Learning Q42. What is Unsupervised learning Q43. What are the various classification algorithms Q44. What is Naive in a Naive Bayes Q45. Explain SVM algorithm in detail. Q46. What are the support vectors in SVM Q47. What are the different kernels in SVM Q48. Explain Decision Tree algorithm in detail. Q49. What are Entropy and Information gain in Decision tree algorithm Q50. What is pruning in Decision Tree Q51. What is logistic regression State an example when you have used logistic regression recently. Q52. What is Linear Regression Q53. What Are the Drawbacks of the Linear Model Q54. What is the difference between Regression and classification ML techniques Q55. What are Recommender Systems Q56. What is Collaborative filtering Q57. How can outlier values be treated Q58. What are the various steps involved in an analytics project Q59. During analysis, how do you treat missing values Q60. How will you define the number of clusters in a clustering algorithm Q61. What is Ensemble Learning Q62. Describe in brief any type of Ensemble Learning Q64. How Do You Work Towards a Random Forest Q65. What crossvalidation technique would you use on a time series data set Q66. What is a BoxCox Transformation Q67. How Regularly Must an Algorithm be Updated Q68. If you are having 4GB RAM in your machine and you want to train your model on 10GB data set. How would you go about this problem Have you ever faced this kind of problem in your machine learningdata science experience so far DEEP LEARNING INTERVIEW QUESTIONS Q69. What do you mean by Deep Learning Q70. What is the difference between machine learning and deep learning Q71. What, in your opinion, is the reason for the popularity of Deep Learning in recent times Q72. What is reinforcement learning Q73. What are Artificial Neural Networks Q74. Describe the structure of Artificial Neural Networks Q75. How Are Weights Initialized in a Network Q79. What Is the Difference Between Epoch, Batch, and Iteration in Deep Learning Q80. What Are the Different Layers on CNN Q81. What Is Pooling on CNN, and How Does It Work Q82. What are Recurrent Neural NetworksRNNs Q83. How Does an LSTM Network Work Q84. What Is a Multilayer PerceptronMLP Q85. Explain Gradient Descent. Q86. What is exploding gradients Q87. What is vanishing gradients Q89. What is Back Propagation and Explain its Working. Q90. What are the variants of Back Propagation Q91. What are the different Deep Learning Frameworks Q92. What is the role of the Activation Function Q93. Name a few Machine Learning libraries for various purposes. Q94. What is an AutoEncoder Q95. What is a Boltzmann Machine Q97. What Is the Difference Between Batch Gradient Descent and Stochastic Gradient Descent Q98. Why Is Tensorflow the Most Preferred Library in Deep Learning Q100. What is the Computational Graph Q101. What is a Generative Adversarial Network Q102. What are the important skills to have in Python with regard to data analysis Recommended videos for you 3 Scenarios Where Predictive Analytics is a Must Web Scraping And Analytics With Python Machine Learning with Python Mastering Python An Excellent tool for Web Scraping and Data Analysis Android Development Using Android 5.0 Lollipop Business Analytics with R The Whys and Hows of Predictive ModelingII Data Science Make Smarter Business Decisions Sentiment Analysis In Retail Domain Python Classes Python Programming Tutorial The Whys and Hows of Predictive ModellingI Python Tutorial All You Need To Know In Python Programming Linear Regression With R Application of Clustering in Data Science Using RealTime Examples Python List, Tuple, String, Set And Dictonary Python Sequences Python Loops While, For and Nested Loops in Python Programming Introduction to Business Analytics with R Business Analytics Decision Tree in R Diversity Of Python Programming Python Programming Learn Python Programming From Scratch Recommended blogs for you Matplotlib Tutorial Python Matplotlib Library with Examples How to fetch and modify Date and Time in Python Why R for Marketing Professionals What is CrossValidation in Machine Learning and how to implement it How to Implement Super Function in Python Data Science Tutorial Learn Data Science from Scratch OpenCV Python Tutorial Computer Vision With OpenCV In Python What are Lambda Functions and How to Use Them Top 10 Data Science Certifications in 2021 Your Path to Become a Data Scientist Types of Data Scientist Python Database Connection Know how to connect with database Core Data Scientist Skills Top 50 SAS Interview Questions You Must Prepare In 2021 What is the Average Python Developer Salary What are Stack Data Structures in Python Python Visual Studio Learn How To Make Your First Python Program Top Reasons Why Business Analytics Is A Must For MBA Grads All You Need to Know About Eval in Python What are Generators in Python and How to use them Introduction To Supervised Learning Join the discussionCancel reply Trending Courses in Data Science Data Science Certification Training with Pyth ... Python Programming Certification Training Machine Learning Certification Training Data Science Certification Course using R Data Analytics with R Certification Training Statistics Essentials for Analytics SAS Training and Certification Advanced Predictive Modelling in R Certificat ... Analytics for Retail Banks Decision Tree Modeling Using R Certification ... Browse Categories Subscribe to our Newsletter, and get personalized recommendations.

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