Royal College of Nursing Data Scientist Interview Questions and Answers

Supervised learning uses labeled data to train the model, while unsupervised learning uses unlabeled data.

• Supervised learning requires a target variable to predict, while unsupervised learning does not.

• In supervised learning, the model learns from the labeled training data and makes predictions on new data. In unsupervised learning, the model finds patterns and relationships in the data without guidance.

• Examples of sup...read more

Understanding outliers, missing values, overfitting, and underfitting in data science.

• Outliers are data points that significantly differ from other data points in a dataset.

• Missing values are data points that are not present in a dataset.

• Overfitting occurs when a model learns noise in the training data rather than the underlying pattern.

• Underfitting occurs when a model is too simple to capture the underlying pattern in...read more