Top 100 Data Science Interview Questions for Freshers | Data Science Interview Questions and Answers скачать в хорошем качестве

Top 100 Data Science Interview Questions for Freshers | Data Science Interview Questions and Answers

🔥1000+ Free Courses With Free Certificates: https://www.mygreatlearning.com/acade... Top 100 Data Science Interview Questions for Freshers | Data Science Interview Questions and Answers 🔥 Get your free certificate of completion for the Data Science with Python Course; Register Now: https://glacad.me/GLA_datascience_python Hello and welcome to the Data Science tutorial powered by Great Learning. In this video, you will learn the answers to the most asked top 100 Data Science interview questions for freshers. If you're moving down the path to becoming a data scientist, you must be prepared to impress prospective employers with your knowledge. In addition to explaining why data science is so important, you'll need to show that you're technically proficient with Big Data concepts, frameworks, and applications. In this video, we have included some of the most popular data science interview questions you can expect to face in your interview. 🏁 Topics Covered: Data Science Interview Questions: 1. How to Handle missing value imputation for quantitative values? 2. How will you compare 2 quantitative variables? 3. What is the DER model? And what are the methods dependent on it? 4. What is forward regression? 5. What is stepwise regression? 6. What is VIF and in what rage it varies? 7. What is fisher's exact test? 8. What is a correlation, and are its measures? 9. What are the types of simple and multiple regression? 10. What do you mean by Mean Squared error? 11. Is R-squared goodness-of-fit? 12. What is the difference between RMSE and R squared in statistics? 13. What is the state-of-the-art technique in pattern recognition and machine learning? 14. What is the difference between general linear models and generalized linear models? 15. What is the best application of linear regression? 16. What is the assumption of Linear Regression? 17. What are the consequences of violating linear regression assumptions? 18. What are the limitations of linear regression modeling in data analysis? 19. How is least squares different from linear regression analysis? 20. How is hypothesis testing used in linear regression? 21. Can you use a categorical variable in linear regression? 22. What are the advantages and disadvantages of linear regression? 23. What’s the linear regression slope formula? 24. Why is logistic regression considered a linear model? 25. What are the advantages of logistic regression? 26. What are the disadvantages of logistic regression? 27. How do you interpret concordance in logistic regression? 28. What does the bias term represent in logistic regression? 29. Is there an error term in logistic regression? 30. Why is collinearity a problem for logistic regression? 31. What is the C Parameter in logistic regression? 32. How is predictive modeling used in logistic regression? 33. Which feature selection methods are better in logistic regression? 34. Why is logistic regression so important for machine learning? 35. Does scikit-learn support ordinal regression? 36. How can you improve the precision/recall of a logistic regression model? 37. What is log-likelihood in logistic regression? 38. How would you evaluate a logistic regression model? 39. What is the difference between neural networks and logistic regression? 40. Does logistic regression require the independent variable to be normally distributed? 41. How do I deal with missing data when running a logistic regression? 42. What is SVM? 43. Write some real-life applications of SVM? 44. What are the advantages of SVM algorithms? 45. Which is the best kernel for SVM and Why? 46. Why does XGBoost perform better than SVM? 47. What is a multi-class SVM Method? 48. How does an SVM choose its support vectors? 49. What is the disadvantage of structural SVM? 50. What is the best way to train an SVM on a very large dataset? 51. What is the difference between the perception learning algorithm and SVM? 52. What is the difference between SVM Rank and SVR (support vector regression) 53. How many kinds of SVM algorithms exist? 54. What is the difference between the normal soft margin SVM and SVM with a linear kernel? 55. How is a linear classifier relevant to SVM? 56. Is one class SVM really unsupervised? 57. How can you choose the parameter C for SVM? 58. In layman’s terms, how does Naive Bayes work? 59. Why is “naïve Bayes” naïve? 60. What are the advantages of using a naïve Bayes for classification? 61. Are Gaussian Naïve Bayes the same as binomial Naïve Bayes? 62. What is the difference between the Naïve Bayes Classifier and the Bayes classifier? 63. In What real-world applications in Naïve Bayes classifier used? 64. Is naive Bayes supervised or unsupervised? 65. When is Naive Bayes better than logistic regression #datascience #interviewquestions