Data Science Interview Questions
In this article, I am going to share some data science questions which are being asked in the interview. I have given more than 10–15 interviews for the Data Scientist job role. Both machine learning and deep learning questions are included. So here we go….
- Difference between Overfitting & Underfitting
- How to handle missing values in data?
- How to handle Imbalanced data?
- Difference between Covariance and Correlation.
- What are feature engineering and feature selections
- Difference between Normalization and Standardization
- What is PCA?
- Hyperparameter Tuning
- Difference between Linear and Logistics Regression.
- Explanation about Algorithms in your projects.
- What is Normal distribution?
- What is Multicollinearity and how do you handle it?
- Tell me about Central Limit Theorem.
- What is VIF?
- Explain Bias & Variance Trade-off?
- Precision and Recall Trade-off
- Difference between Bagging and Boosting.
- Explanation of Type-I & Type-II error
- Explain Lasso & Ridge?
- Difference between R2 and Adjusted R2
- What are MAE and RMSE?
- Explain K-Means clusters and the Elbow method.
- Explain AUC-ROC.
- Explain Activation Functions?
- Explain Optimizers?
- Difference between Sigmoid and Softmax.
- Difference between Vanishing and Explode Gradient Descent.
- Why Relu is used in Hidden Layers?
- How do we decide how many hidden layers and neurons should be taken?
- Architecture of CNN
- Difference between RNN and LSTM.
Conclusions
Based on my interviews experience, I have written the above questions to make readers aware of and they can prepare accordingly. Questions can be different for an individual but people at least get some ideas.