Terms and definitions from all courses

Filtering: The process of selecting a smaller part of a dataset based on specified values and using it for viewing or analysis
First-party data: Data that was gathered from inside your own organization
Float: A data type that represents numbers that contain decimals


For loop: A piece of code that iterates over a sequence of values


format(): A string method that formats and inserts specific substrings into designated places within a larger string
Forward selection: A stepwise variable selection process that begins with the null mode—with zero independent variables—and considers all possible variables to add; incorporates the independent variable that contributes the most explanatory power to the model


Function: A body of reusable code for performing specific processes or tasks

G

Generator(): A function that returns an object (iterator) which can be iterated over (one value at a time)
Global outliers: Values that are completely different from the overall data group and have no association with any other outliers
Global variable: A variable that can be accessed from anywhere in a program or script
Gradient boosting machines (GBMs): Model ensembles that use gradient boosting
Gradient boosting: A boosting methodology where each base learner in the sequence is built to predict the residual errors of the model that preceded it GridSearch: A tool to confirm that a model achieves its intended purpose by systematically checking every combination of hyperparameters to identify which set produces the best results, based on the selected metric
groupby(): A pandas DataFrame method that groups rows of the dataframe together based on their values at one or more columns, which allows further analysis of the groups
Grouping: The process of aggregating individual observations of a variable into groups