Data Science Interview Questions

What is data science?

Explain the difference between supervised and unsupervised learning in data science.

What are some common algorithms used in data science?

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What is feature engineering and why is it important in data science?

What is overfitting and how can it be avoided in machine learning models?

What is the purpose of exploratory data analysis (EDA) in data science?

Explain the concept of regularization in machine learning.

How do you handle missing data in a dataset?

What is the role of visualization in data science?

Describe the steps involved in a typical data science project lifecycle.

What is the difference between precision and recall in evaluating classification models?

How can you assess the performance of a machine learning model?

What is cross-validation and why is it important in model evaluation?

Explain the Bias-Variance tradeoff in machine learning models.

What are some common tools and programming languages used in data science?

What is a confusion matrix and how is it used to evaluate classification models?

What is clustering and what are some popular clustering algorithms?

How do you handle imbalanced datasets in classification problems?

What is the purpose of feature selection in machine learning?

Explain the difference between correlation and causation in data analysis.

What is data science?