Terms and definitions from all courses

Random seed: A starting point for generating random numbers Random variable: A variable that represents the values for the possible outcomes of a random event
Range: The difference between the largest and smallest value in a dataset range(): A Python function that returns a sequence of numbers starting from zero, increments by 1 by default, and stops before the given number
Recall: The proportion of actual positives that were identified correctly to all actual positives
Recommendation systems: Unsupervised learning techniques that use unlabeled data to offer relevant suggestions to users
Refactoring: The process of restructuring code while maintaining its original functionality Regression analysis: A group of statistical techniques that use existing data to estimate the relationships between a single dependent variable and one or more independent variables Regression coefficient: The estimated betas in a regression model Regression models: (Refer to regression analysis)
Regularization: A set of regression techniques that shrinks regression coefficient estimates towards zero, adding in bias, to reduce variance
remove(): A method that removes an element from a list
Representative sample: A sample that accurately reflects the characteristics of a population
reshape(): A NumPy method used to change the shape of an array
Residual: The difference between observed or actual values and the predicted values of the regression line
Response variable: (Refer to dependent variable)
return: A reserved keyword in Python that makes a function produce new results which are saved for later use
Reusability: The capability to define code once and using it many times without having to rewrite it


Right join: A way of combining data such that all the keys in the right dataframe are included—even if they aren’t in the left dataframe