Terms and definitions from all courses

H

Hackathon: An event where programmers and data professionals come together and work on a project
Head(): A function that returns a preview of the column names and the first few rows of a dataset
Heatmap: A type of data visualization that depicts the magnitude of an instance or set of values based on two colors
Help(): A Python help function used to display the documentation of modules, functions, classes, keywords, and more
Histogram: A data visualization that depicts an approximate representation of the distribution of values in a dataset
Hold-out sample: A random sample of observed data that is not used to fit the model
Homoscedasticity assumption: An assumption of simple linear regression stating that the variation of the residuals (errors) is constant or similar across the model


Hyperparameters: Parameters that can be set by the modeler before the model is trained Hyperparameter tuning: Refers to changing parameters that directly affect how the model trains, before the learning process begins
Hypothesis: A theory or an explanation, based on evidence, that has not yet been refuted


Hypothesis testing: A statistical procedure that uses sample data to evaluate an assumption about a population parameter

I

if: A reserved keyword that sets up a condition in Python


iloc[]: A type of notation in pandas that indicates when the user wants to select by integer-location-based position
Immutability: The concept that a data structure or element’s values can never be altered or updated


Immutable data type: A data type in which the values can never be altered or updated
Implicit conversion: The process Python uses to automatically convert one data type to another without user involvement
Import statement: A statement that uses the import keyword to load an external library, package, module, or function into the computing environment