Data science interviews encompass a variety of challenging questions to test your knowledge in machine learning, probability, SQL and more. Hone your skills with these questions.

Model deployment is the process of integrating a machine learning model into a production environment where it can take in an input and return an output.

Feature importance involves calculating a score for all input features in a machine learning model to determine which ones are the most important. Here’s how to do it.

Machine learning models are categorized as either supervised or unsupervised. Here’s what you need to know about each model and when to use them.

Learn 10 advanced SQL concepts you should know for data science interviews to take your SQL skills to the next level.

Collect unbiased data utilizing these four types of random sampling techniques: systematic, stratified, cluster, and simple random sampling.

Statistical bias is when a model or statistic is unrepresentative of the population. There are six main types of bias in statistics.