Terms and definitions from all courses

T

Tableau: A business intelligence and analytics platform that helps people visualize, understand, and make decisions with data
Tabular data: Data that is in the form of a table, with rows and columns


Target population: The complete set of elements that someone is interested in knowing more about
Third-party data: Data gathered outside your organization and aggregated
Tolist(): A NumPy method to convert arrays into lists
Tree-based learning: A type of supervised machine learning that performs classification and regression tasks Tuple: An immutable sequence that can contain elements of any data type


tuple(): A function that transforms input into tuples


Two-Way ANOVA: A type of statistical testing that compares the means of one continuous dependent variable based on three or more groups of two categorical variables
type(): A function used to identify the type of data in a list

U

Undercoverage bias: Refers to when some members of a population are inadequately represented in a sample
union(): A function that finds all the elements from both sets
Unsupervised model: A machine learning model that is used to discover the natural structure of the data, finding relationships within unlabeled data
Upper limit: When constructing an interval, the calculation of the sample means plus the margin of error
Upsampling: The process of taking observations from the minority class and either adding copies of those observations to the dataset or generating new observations to add to the dataset

V

Validating: The process of verifying that the data is consistent and high quality; one of the six practices of EDA
values(): A dictionary method to retrieve only the dictionary’s values