Terms and definitions from all courses

Output: A message stating what to do next
Overfitting: When a model fits the observed or training data too specifically and is unable to generate suitable estimates for the general population

P

P-value: The probability of observing results as extreme as those observed when the null hypothesis is true
PACE: A workflow data professionals can use to remain focused on the end goal of any given dataset; stands for plan, analyze, construct, and execute
Package: A fundamental unit of shareable code that others have developed for a specific purpose
pandas: A powerful library built on top of NumPy that’s used to manipulate and analyze tabular data
Parameter: A characteristic of a population
Percentile: The value below which a percentage of data falls
Personally identifiable information (PII): Information that permits the identity of an individual to be inferred by either direct or indirect means
Plan stage: Stage of the PACE workflow where the scope of a project is defined and the informational needs of the organization are identified
Point estimate: A calculation that uses a single value to estimate a population parameter Poisson distribution: A probability distribution that models the probability that a certain number of events will occur during a specific time period
pop(): A method that extracts an element from a list by removing it at a given index
Popularity bias: The phenomenon of more popular items being recommended too frequently Population: Every possible element that a data professional is interested in measuring


Population proportion: The percentage of individuals or elements in a population that share a certain characteristic
Positive correlation: A relationship between two variables that tend to increase or decrease together.
Post hoc test: An ANOVA test that performs a pairwise comparison between all available groups while controlling for the error rate Posterior probability: The probability of an event occurring after taking into consideration new information