Data Science Interview Preparation Day 1.pdf
950.2 KB
Data Science #Interview Preparation:
Day 1 Questions:
Q1. What is the difference between AI, Data Science, ML, and DL?
Q2. What is the difference between Supervised learning, Unsupervised learning and Reinforcement learning?
Q3. Describe the general architecture of Machine learning.
Q4. What is Linear Regression?
Q5. OLS Stats Model (Ordinary Least Square)
Q6. What is L1 Regularization (L1 = lasso) ?
Q7. L2 Regularization(L2 = Ridge Regression)
Q8. What is R square(where to use and where not)?
Q9. What is Mean Square Error?
Q10. Why Support Vector Regression? Difference between SVR and a simple regression model?
Python World
Day 1 Questions:
Q1. What is the difference between AI, Data Science, ML, and DL?
Q2. What is the difference between Supervised learning, Unsupervised learning and Reinforcement learning?
Q3. Describe the general architecture of Machine learning.
Q4. What is Linear Regression?
Q5. OLS Stats Model (Ordinary Least Square)
Q6. What is L1 Regularization (L1 = lasso) ?
Q7. L2 Regularization(L2 = Ridge Regression)
Q8. What is R square(where to use and where not)?
Q9. What is Mean Square Error?
Q10. Why Support Vector Regression? Difference between SVR and a simple regression model?
Python World
Data Science Interview Preparation Day 3.pdf
762.8 KB
Data Science #Interview Preparation Day 3
Day 3 Questions:
Q1. How do you treat heteroscedasticity in regression?
Q2. What is multicollinearity, and how do you treat it?
Q3. What is market basket analysis? How would you do it in Python?
Q4. What is Association Analysis? Where is it used?
Q5. What is KNN Classifier ?
Q6. What is Pipeline in sklearn ?
Q7. What is Principal Component Analysis(PCA), and why we do?
Q8. What is t-SNE?
Q9. VIF(Variation Inflation Factor), Weight of Evidence & Information Value. Why and when to use?
Q10: How to evaluate that data does not have any outliers ?
Q11: What you do if there are outliers?
Q12: What are the encoding techniques you have applied with Examples ?
Q13: Tradeoff between bias and variances, the relationship between them.
Q14: What is the difference between Type 1 and Type 2 error and severity of the error?
Q15: What is binomial distribution and polynomial distribution?
Q16: What is the Mean Median Mode standard deviation for the sample and population?
. . .
Day 3 Questions:
Q1. How do you treat heteroscedasticity in regression?
Q2. What is multicollinearity, and how do you treat it?
Q3. What is market basket analysis? How would you do it in Python?
Q4. What is Association Analysis? Where is it used?
Q5. What is KNN Classifier ?
Q6. What is Pipeline in sklearn ?
Q7. What is Principal Component Analysis(PCA), and why we do?
Q8. What is t-SNE?
Q9. VIF(Variation Inflation Factor), Weight of Evidence & Information Value. Why and when to use?
Q10: How to evaluate that data does not have any outliers ?
Q11: What you do if there are outliers?
Q12: What are the encoding techniques you have applied with Examples ?
Q13: Tradeoff between bias and variances, the relationship between them.
Q14: What is the difference between Type 1 and Type 2 error and severity of the error?
Q15: What is binomial distribution and polynomial distribution?
Q16: What is the Mean Median Mode standard deviation for the sample and population?
. . .
100 Essential Python #Interview Questions:
www.techbeamers.com/python-interview-questions-programmers
www.techbeamers.com/python-interview-questions-programmers