10.4: Key Terms

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actionable advice: recommendations or guidance in a report, particularly in an executive summary, providing practical ways to apply the report's findings or insights

alt text: brief descriptions of images that accompany the image or may be embedded within the image data, meant to aid readers who are not able to view the images and graphics due to disability or other limitations

assumption: statement that is thought to be true without being verified or proven; foundational hypotheses or beliefs about the structure, relationships, or distribution of data that guide the analytical approach and model selection for a project

audience: the person or group that will be reading the report

bootstrap samples: multiple samples taken from a dataset with duplicates permitted

codebook: data dictionary to detail the variables, their units, values, and labels within the dataset used in the project

constraint: limitation or restriction that is imposed on a project or its solution

cross-validation: validation method that divides the training set into multiple subsets and iteratively trains and evaluates the model on different combinations of these subsets

executive (or data) dashboard: a visual representation tool designed to provide a quick, real-time overview of an organization's key performance indicators (KPIs) and metrics to senior management, often updated in real time or at regular intervals

executive summary: concise, standalone document that encapsulates the essence of a data science report

executives: decision-makers such as managers, CEOs, or others who may not have detailed technical knowledge but need an understanding of the implications of the information for strategic decision-making

experts: individuals with an advanced understanding of the subject matter, often with specialized knowledge or education in the topic at hand

fold: one of a set of equally sized subsets used in k-fold cross-validation

hyperparameter tuning: fine-tuning of certain constants that affect the performance of a model

jargon: specialized and/or technical terms in a given field

k-fold cross-validation: cross-validation strategy that works by dividing the dataset into $k$ equally sized subsets or folds, where the model is trained on $k - 1$ of the folds and tested on the remaining fold, a process that is repeated $k$ times with each fold serving as the test set once

key performance indicators (KPIs): quantifiable metrics used to evaluate the success of an organization, employee, or process in achieving specific objectives and goals

layered approach: writing strategy in which a report is organized into sections or appendices that provide different levels of detail and complexity for different audiences

leave-one-out cross-validation (LOOCV): special case of k-fold cross-validation where $k$ is set to the number of data points in the dataset so that the model is trained on all data points except one, which is used as the validation set, and this process is repeated for each data point in the dataset

mean percentage error (MPE): average of the percentage errors between predicted $({\hat{y}}_{i})$ and actual $(y_{i})$ value: $MPE = \frac{1}{n} \sum_{i = 1}^{n} \frac{y_{i} - {\hat{y}}_{i}}{y_{i}}$

Monte Carlo simulation: random sampling and statistical modeling to estimate the probability of different outcomes under uncertainty

multi-way sensitivity analysis: explores the effects of simultaneous changes in multiple input parameters on the outcome

neurodiversity: normal variation of individual cognitive abilities, especially in realms of communication and processing in formation

nonspecialists: nonexpert audience without specialized knowledge of the subject but with some need to understand the basics of a data science report for a particular reason

one-way sensitivity analysis: examines how changes in one input parameter at a time affect the outcome of a model

scenario analysis: evaluates the outcomes under different predefined sets of input parameters, representing possible future states or scenarios

sensitivity analysis: technique used to determine how different values of an independent variable affect a particular dependent variable under a given set of assumptions

technicians: practical users of information in a data science report who may apply the knowledge in a hands-on manner

validation: evaluation of multiple predictive models and/or hyperparameter tuning

version control system: a software tool that helps keep track of changes to files, code, or any type of digital content over time

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