Data Science Technical Questions – codewindow.in

Data Science - Questions

Section - I

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1. What is data science?

2. What are the steps in the data science process?

3. What is big data?

4. What is data mining?

5. What is the difference between supervised and unsupervised learning?

6. What is overfitting and how can it be avoided?

7. What is regularization and how does it help prevent overfitting?

8. What is the difference between a decision tree and random forest?

9. What is gradient descent and how does it work?

10. What is the difference between a random sample and a stratified sample?

11. What is a false positive and false negative?

12. What is the curse of dimensionality?

13. What is the difference between linear and logistic regression?

14. What is Naive Bayes and how does it work?

15. What is the difference between a k-means and hierarchical clustering?

16. What is a support vector machine (SVM)?

17. What is the difference between a SVM and a linear discriminant analysis (LDA)?

18. What is a neural network and how does it work?

19. What is the difference between deep learning and traditional machine learning algorithms?

Section - II

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20. What is an artificial neural network (ANN)?

21. What is a convolutional neural network (CNN)?

22. What is a recurrent neural network (RNN)?

23. What is a generative adversarial network (GAN)?

24. What is a Boltzmann machine?

25. What is the difference between reinforcement learning and supervised learning?

26. What is dimension reduction and why is it important?

27. What is feature engineering and why is it important?

28. What is cross-validation and how is it used in model evaluation?

29. What is the difference between precision and recall?

30. What is the F1 score and why is it important?

31. What is the ROC curve and why is it important?

32. What is the difference between a Type I and Type II error?

33. What is the difference between accuracy, precision, recall and F1 score?

34.What is the difference between sensitivity and specificity?

35. What is the difference between L1 and L2 regularization?

36. What is the difference between a t-test and ANOVA?

37. What is the difference between a chi-squared test and a t-test?

38. What is Bayesian statistics and how is it different from frequentist statistics?

39. What is Markov Chain Monte Carlo (MCMC)?

40. What is a Gaussian mixture model (GMM)?

41. What is a Hidden Markov Model (HMM)?

42. What is a Kalman filter?

43. What is a Particle filter?

44. What is an Autoencoder?

45. What is a variational autoencoder (VAE)?

46. What is data science and how does it differ from other related fields?

47. Describe the data science process and the steps involved?

48. What are the common data types you work with in data science and how do you handle each of them?

49. Explain the concept of data cleaning and how it impacts the accuracy of a model?

50. Describe the difference between supervised and unsupervised learning?

51. What are the common evaluation metrics used in data science?

52. How to handle imbalanced datasets in a binary classification problem?

53. What is regularization and how does it help prevent overfitting?

54. Explain the concept of a support vector machine (SVM) and how it works

55. Explain the structure of an artificial neural network (ANN)?

56. Explain the difference between a convolutional neural network (CNN) and a recurrent neural network (RNN)?

57. Describe the concept of a generative adversarial network (GAN)?

58. Describe the process of dimension reduction and its importance in data science?

59. Explain the concept of feature engineering and why is it important?

60. Explain the difference between feature scaling and normalization?

Section - III

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61. Describe the process of cross-validation and its importance in model evaluation?

62. Explain the difference between the F1 score and ROC curve in model evaluation?

63. Describe the difference between a one-tailed and two-tailed test in hypothesis testing?

64. Explain the difference between a chi-squared test and a t-test?

65. Describe the basics of Bayesian statistics and how it differs from frequentist statistics?

66. Explain the concept of Markov Chain Monte Carlo (MCMC)?

67. Explain what a Gaussian mixture model (GMM) is and its applications?

68. Explain the concept of a Hidden Markov Model (HMM) and its applications?

69. Explain the Kalman filter and its applications in data science?

70. Explain the concept of an Autoencoder and its applications in data science?

71. Describe the variational autoencoder (VAE) and its applications in data science?

72. Explain how you handle missing data in a dataset?

73. Describe the difference between a relational database and a NoSQL database?

74. What is data science and what is its importance in today’s business and industry?

75. Explain the concept of data cleaning and its impact on the accuracy of a model?

76. Explain the concept of big data and its implications for data science?

77. Explain how to handle imbalanced datasets in a binary classification problem?

78. Explain how you would approach a real-world problem and apply data science techniques to solve it?

79. What are the key skills and knowledge areas required for a successful data scientist?

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Data Science - Questions

1. What is data science?

2. What are the steps in the data science process?

3. What is big data?

4. What is data mining?

5. What is the difference between supervised and unsupervised learning?

6. What is overfitting and how can it be avoided?

7. What is regularization and how does it help prevent overfitting?

8. What is the difference between a decision tree and random forest?

9. What is gradient descent and how does it work?

10. What is the difference between a random sample and a stratified sample?

11. What is a false positive and false negative?

12. What is the curse of dimensionality?

13. What is the difference between linear and logistic regression?

14. What is Naive Bayes and how does it work?

15. What is the difference between a k-means and hierarchical clustering?

16. What is a support vector machine (SVM)?

17. What is the difference between a SVM and a linear discriminant analysis (LDA)?

18. What is a neural network and how does it work?

19. What is the difference between deep learning and traditional machine learning algorithms?

20. What is an artificial neural network (ANN)?

21. What is a convolutional neural network (CNN)?

22. What is a recurrent neural network (RNN)?

23. What is a generative adversarial network (GAN)?

24. What is a Boltzmann machine?

25. What is the difference between reinforcement learning and supervised learning?

26. What is dimension reduction and why is it important?

27. What is feature engineering and why is it important?

28. What is cross-validation and how is it used in model evaluation?

29. What is the difference between precision and recall?

30. What is the F1 score and why is it important?

31. What is the ROC curve and why is it important?

32. What is the difference between a Type I and Type II error?

33. What is the difference between accuracy, precision, recall and F1 score?

34.What is the difference between sensitivity and specificity?

35. What is the difference between L1 and L2 regularization?

36. What is the difference between a t-test and ANOVA?

37. What is the difference between a chi-squared test and a t-test?

38. What is Bayesian statistics and how is it different from frequentist statistics?

39. What is Markov Chain Monte Carlo (MCMC)?

40. What is a Gaussian mixture model (GMM)?

41. What is a Hidden Markov Model (HMM)?

42. What is a Kalman filter?

43. What is a Particle filter?

44. What is an Autoencoder?

45. What is a variational autoencoder (VAE)?

46. What is data science and how does it differ from other related fields?

47. Describe the data science process and the steps involved?

48. What are the common data types you work with in data science and how do you handle each of them?

49. Explain the concept of data cleaning and how it impacts the accuracy of a model?

50. Describe the difference between supervised and unsupervised learning?

51. What are the common evaluation metrics used in data science?

52. How to handle imbalanced datasets in a binary classification problem?

53. What is regularization and how does it help prevent overfitting?

54. Explain the concept of a support vector machine (SVM) and how it works

55. Explain the structure of an artificial neural network (ANN)?

56. Explain the difference between a convolutional neural network (CNN) and a recurrent neural network (RNN)?

57. Describe the concept of a generative adversarial network (GAN)?

58. Describe the process of dimension reduction and its importance in data science?

59. Explain the concept of feature engineering and why is it important?

60. Explain the difference between feature scaling and normalization?

61. Describe the process of cross-validation and its importance in model evaluation?

62. Explain the difference between the F1 score and ROC curve in model evaluation?

63. Describe the difference between a one-tailed and two-tailed test in hypothesis testing?

64. Explain the difference between a chi-squared test and a t-test?

65. Describe the basics of Bayesian statistics and how it differs from frequentist statistics?

66. Explain the concept of Markov Chain Monte Carlo (MCMC)?

67. Explain what a Gaussian mixture model (GMM) is and its applications?

68. Explain the concept of a Hidden Markov Model (HMM) and its applications?

69. Explain the Kalman filter and its applications in data science?

70. Explain the concept of an Autoencoder and its applications in data science?

71. Describe the variational autoencoder (VAE) and its applications in data science?

72. Explain how you handle missing data in a dataset?

73. Describe the difference between a relational database and a NoSQL database?

74. What is data science and what is its importance in today’s business and industry?

75. Explain the concept of data cleaning and its impact on the accuracy of a model?

76. Explain the concept of big data and its implications for data science?

77. Explain how to handle imbalanced datasets in a binary classification problem?

78. Explain how you would approach a real-world problem and apply data science techniques to solve it?