What are some common data science interview questions?

What are some common data science interview questions I should prepare for?

Some common data science interview questions include:

1. What is the difference between supervised and unsupervised learning?
2. Can you explain the bias-variance tradeoff?
3. How do you handle missing data in a dataset?

Can you explain the bias-variance tradeoff?

The bias-variance tradeoff is a fundamental concept in machine learning. Bias refers to the error due to overly simplistic assumptions in the learning algorithm, while variance refers to the error due to excessive complexity in the model. A good model should balance both to minimize total error.

What techniques can I use to handle missing data?

You can handle missing data using several techniques:

1. Imputation (mean, median, mode)
2. Deleting rows or columns with missing values
3. Using algorithms that support missing values, like certain tree-based methods.

What is feature engineering?

Feature engineering is the process of using domain knowledge to create new features or modify existing ones to improve the performance of machine learning models.