Python Interview Questions for Freshers

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1. What is Python? What are the benefits of using Python
2. What is a dynamically typed language?
3. What is an Interpreted language?
4. What is PEP 8 and why is it important?
5. What is Scope in Python?
6. What are lists and tuples? What is the key difference between the two?
7. What are the common built-in data types in Python?
8. What is pass in Python?
9. What are modules and packages in Python?
10. What are global, protected and private attributes in Python?
11. What is the use of self in Python?
12. What is init?
13. What is break, continue and pass in Python?
14. What are unit tests in Python?
15. What is docstring in Python?
16. What is slicing in Python?
17. Explain how can you make a Python Script executable on Unix?
18. What is the difference between Python Arrays and lists? Python Interview Questions for Experienced
19. How is memory managed in Python?
20. What are Python namespaces? Why are they used?
21. What is Scope Resolution in Python?
22. What are decorators in Python?
23. What are Dict and List comprehensions?
24. What is lambda in Python? Why is it used?
25. How do you copy an object in Python?
26. What is the difference between xrange and range in Python?
27. What is pickling and unpickling?
28. What are generators in Python?
29. What is PYTHONPATH in Python?
30. What is the use of help() and dir() functions?
31. What is the difference between .py and .pyc files?
32. How Python is interpreted?
33. How are arguments passed by value or by reference in python?
34. What are iterators in Python?
35. Explain how to delete a file in Python?
36. Explain split() and join() functions in Python?
37. What does *args and **kwargs mean?
38. What are negative indexes and why are they used?

Python OOPS Interview Questions

39. How do you create a class in Python?
40. How does inheritance work in python? Explain it with an example.
41. How do you access parent members in the child class?
42. Are access specifiers used in python?
43. Is it possible to call parent class without its instance creation?
44. How is an empty class created in python?
45. Differentiate between new and override modifiers.
46. Why is finalize used?
47. What is init method in python?
48. How will you check if a class is a child of another class?

Python Pandas Interview Questions

49. What do you know about pandas?
50. Define pandas dataframe.
51. How will you combine different pandas dataframes?
52. Can you create a series from the dictionary object in pandas?
53. How will you identify and deal with missing values in a dataframe?
54. What do you understand by reindexing in pandas?
55. How to add new column to pandas dataframe?
56. How will you delete indices, rows and columns from a dataframe?
57. Can you get items of series A that are not available in another series B?
58. How will you get the items that are not common to both the given series A and B?
59. While importing data from different sources, can the pandas library recognize dates?

Numpy Interview Questions

60. What do you understand by NumPy?
61. How are NumPy arrays advantageous over python lists?
62. What are the steps to create 1D, 2D and 3D arrays?
63. You are given a numpy array and a new column as inputs. How will you delete the second column and replace the column with a new column value?
64. How will you efficiently load data from a text file?
65. How will you read CSV data into an array in NumPy?
66. How will you sort the array based on the Nth column?
67. How will you find the nearest value in a given numpy array?
68. How will you reverse the numpy array using one line of code?
69. How will you find the shape of any given NumPy array? Python Libraries Interview Questions
70. Differentiate between a package and a module in python.
71. What are some of the most commonly used built-in modules in Python?
72. What are lambda functions?
73. How can you generate random numbers?
74. Can you easily check if all characters in the given string is alphanumeric?
75. What are the differences between pickling and unpickling?
76. Define GIL.
77. Define PYTHONPATH.
78. Define PIP.
79. Are there any tools for identifying bugs and performing static analysis in python?
80. Differentiate between deep and shallow copies.
81. What is main function in python? How do you invoke it?

Python Programming Examples

82. Write python function which takes a variable number of arguments.
83. WAP (Write a program) which takes a sequence of numbers and check if all numbers are unique.
84. Write a program for counting the number of every character of a given text file.
85. Write a program to check and return the pairs of a given array A whose sum value is equal to a target value N.
86. Write a Program to add two integers >0 without using the plus operator.
87. Write a Program to solve the given equation assuming that a,b,c,m,n,o are constants:
88. Write a Program to match a string that has the letter ‘a’ followed by 4 to 8 'b’s.
89. Write a Program to convert date from yyyy-mm-dd format to dd-mm-yyyy format.
90. Write a Program to combine two different dictionaries. While combining, if you find the same keys, you can add the values of these same keys. Output the new dictionary

Data Science Interview Questions for Freshers

* ANS. LINK(https://www.interviewbit.com/data-science-interview-questions/)

1. What is Data Science?
2. What is the difference between data analytics and data science?
3. What are some of the techniques used for sampling? What is the main advantage of sampling?
4. List down the conditions for Overfitting and Underfitting.
5. Differentiate between the long and wide format data.
6. What are Eigenvectors and Eigenvalues?
7. What does it mean when the p-values are high and low?
8. When is resampling done?
9. What do you understand by Imbalanced Data?
10. Are there any differences between the expected value and mean value?
11. What do you understand by Survivorship Bias?
12. Define the terms KPI, lift, model fitting, robustness and DOE.
13. Define confounding variables.
14. Define and explain selection bias?
15. Define bias-variance trade-off?
16. Define the confusion matrix?
17. What is logistic regression? State an example where you have recently used logistic regression.
18. What is Linear Regression? What are some of the major drawbacks of the linear model?
19. What is a random forest? Explain it’s working.
20. In a time interval of 15-minutes, the probability that you may see a shooting star or a bunch of them is 0.2. What is the percentage chance of you seeing at least one star shooting from the sky if you are under it for about an hour?
21. What is deep learning? What is the difference between deep learning and machine learning?
22. What is a Gradient and Gradient Descent?

Data Science Interview Questions for Experienced

23. How are the time series problems different from other regression problems?
24. What are RMSE and MSE in a linear regression model?
25. What are Support Vectors in SVM (Support Vector Machine)?
26. So, you have done some projects in machine learning and data science and we see you are a bit experienced in the field. Let’s say your laptop’s RAM is only 4GB and you want to train your model on 10GB data set.
27. Explain Neural Network Fundamentals.
28. What is Generative Adversarial Network?
29. What is a computational graph?
30. What are auto-encoders?
31. What are Exploding Gradients and Vanishing Gradients?
32. What is the p-value and what does it indicate in the Null Hypothesis?
33. Since you have experience in the deep learning field, can you tell us why TensorFlow is the most preferred library in deep learning?
34. Suppose there is a dataset having variables with missing values of more than 30%, how will you deal with such a dataset?
35. What is Cross-Validation?
36. What are the differences between correlation and covariance?
37. How do you approach solving any data analytics based project?
38. How regularly must we update an algorithm in the field of machine learning?
39. Why do we need selection bias?
40. Why is data cleaning crucial? How do you clean the data?
41. What are the available feature selection methods for selecting the right variables for building efficient predictive models?
42. During analysis, how do you treat the missing values?
43. Will treating categorical variables as continuous variables result in a better predictive model?
44. How will you treat missing values during data analysis?
45. What does the ROC Curve represent and how to create it?
46. What are the differences between univariate, bivariate and multivariate analysis?
47. What is the difference between the Test set and validation set?
48. What do you understand by a kernel trick?
49. Differentiate between box plot and histogram.
50. How will you balance/correct imbalanced data?
51. What is better - random forest or multiple decision trees?
52. Consider a case where you know the probability of finding at least one shooting star in a 15-minute interval is 30%. Evaluate the probability of finding at least one shooting star in a one-hour duration?
53. Toss the selected coin 10 times from a jar of 1000 coins. Out of 1000 coins, 999 coins are fair and 1 coin is double-headed, assume that you see 10 heads. Estimate the probability of getting a head in the next coin toss.
54. What are some examples when false positive has proven important than false negative?
55. Give one example where both false positives and false negatives are important equally?
56. Is it good to do dimensionality reduction before fitting a Support Vector Model?
57. What are various assumptions used in linear regression? What would happen if they are violated?
58. How is feature selection performed using the regularization method?
59. How do you identify if a coin is biased?
60. What is the importance of dimensionality reduction?
61. How is the grid search parameter different from the random search tuning strategy?

Frequently Asked Questions

62. How do I prepare for a data science interview?
63. Are data science interviews hard?
64. What are the top 3 technical skills of a data scientist?
65. Is data science a good career?
66. Are coding questions asked in data science interviews?
67. Is python and SQL enough for data science?
68. What are Data Science tools?

Machine Learning Interview Questions For Freshers

* Ans Link(https://www.interviewbit.com/machine-learning-interview-questions)

1. Why was Machine Learning Introduced?
2. What are Different Types of Machine Learning algorithms?
3. What is Supervised Learning?
4. What is Unsupervised Learning?
5. What is ‘Naive’ in a Naive Bayes?
6. What is PCA? When do you use it?
7. Explain SVM Algorithm in Detail
8. What are Support Vectors in SVM?
9. What are Different Kernels in SVM?
10. What is Cross-Validation?
11. What is Bias in Machine Learning?
12. Explain the Difference Between Classification and Regression?

Advanced Machine Learning Questions

13. What is F1 score? How would you use it?
14. Define Precision and Recall?
15. How to Tackle Overfitting and Underfitting?
16. What is a Neural Network?
17. What are Loss Function and Cost Functions? Explain the key Difference Between them?
18. What is Ensemble learning?
19. How do you make sure which Machine Learning Algorithm to use?
20. How to Handle Outlier Values?
21. What is a Random Forest? How does it work?
22. What is Collaborative Filtering? And Content-Based Filtering?
23. What is Clustering?
24. How can you select K for K-means Clustering?
25. What are Recommender Systems?
26. How do check the Normality of a dataset?
27. Can logistic regression use for more than 2 classes?
28. Explain Correlation and Covariance?
29. What is P-value?
30. What are Parametric and Non-Parametric Models?
31. What is Reinforcement Learning?
32. Difference Between Sigmoid and Softmax functions?

Linear Regression Basic Interview Questions

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1. What is linear regression, and how does it work?
2. What are the assumptions of a linear regression model?
3. What are outliers? How do you detect and treat them? How do you deal with outliers in a linear regression model?
4. How do you determine the best fit line for a linear regression model?
5. What is the difference between simple and multiple linear regression?
6. What is linear Regression Analysis?
7. What is multicollinearity and how does it affect linear regression analysis?
8. What is the difference between linear regression and logistic regression?
9. What are the common types of errors in linear regression analysis?
10. What is the difference between a dependent and independent variable in linear regression?
11. What is an interaction term in linear regression and how is it used?
12. What is the difference between biased and unbiased estimates in linear regression?
13. How do you measure the strength of a linear relationship between two variables?
14. What is the difference between a population regression line and a sample regression line?
15. What is the difference between linear regression and non-linear regression?
16. What are the common techniques used to improve the accuracy of a linear regression model?
17. What is a residual in linear regression and how is it used in model evaluation?
18. What is the difference between a parametric and non-parametric regression model?
19. What are the assumptions of the ordinary least squares method for linear

Linear Regression Advanced Interview Questions

20. How do you determine the significance of a predictor variable in a linear regression model?
21. What is the role of a dummy variable in linear regression analysis?
22. What is heteroscedasticity?
23. What is the difference between a categorical and continuous variable in linear regression?
24. What is the impact of correlated predictor variables on linear regression analysis?
25. How do you evaluate the goodness of fit of a linear regression model?
26. What is the role of a regression coefficient in linear regression analysis?
27. What is a prediction interval in linear regression and how is it used?
28. How to find RMSE and MSE?
29. How do you test for autocorrelation in a linear regression model?
30. What are the common challenges faced when building a linear regression model?
31. Can you explain the concept of collinearity and how it affects a linear regression model?
32. How do you choose the right variables for a linear regression model?
33. What is the role of regularization techniques in preventing overfitting in linear regression?
34. Can you explain the concept of overfitting in linear regression?
35. What are the limitations of linear regression?
36. What are the possible ways of improving the accuracy of a linear regression model?
37. Can you explain the concept of bias-variance tradeoff in linear regression?
38. Can you explain the difference between a linear regression model that assumes homoscedasticity and one that assumes heteroscedasticity?
39. What is the difference between a linear regression model with a linear relationship and one with a non-linear relationshi
40. What is the curse of dimensionality? Can you give an example?
41. What is the difference between correlation and regression?
42. What is the main problem with using a single regression line?
43. What does locally weighted regression results depend on?
44. Which of the following is the simplest error detection method?
45. If you have only one independent variable, how many coefficients will you require to estimate in a simple linear regression model?
46. What is the performance of the model aer adding a non important feature to a linear regression model?
47. Linearity in regression corresponds to what ?
48. Which of the following plots is best suited to test the linear relationship of independent and dependent continuous variables?
49. What is the primary difference between R squared and adjusted R squared?
50. What is the importance of the F-test in a linear model?
51. Explain the Gradient Descent algorithm with respect to linear regression?
52. For a linear regression model, how do we interpret a Q-Q plot?
53. What are MAE and MAPE?