Data analysis is a fundamental process in any project. However, data can be lumped into different types, with categorical and continuous data seeming almost opposed at first glance.

Categorical versus Numerical. Data that represent categories, such as dichotomous (two categories) and nominal (more than two categories) observations, are collectively called categorical (qualitative ...

The application of quantitative and qualitative skills varies depending on the industry. Tasker gave some examples of how different industries use these skills: Artificial Intelligence/Data Science.

This book discusses categorical data analysis and its implementation with the SAS System. Both nonparametric methods and model-based parametric methods are discussed. Specific topics include ...

Data Dependency: Quantitative analysis is heavily dependent on the quality and availability of numerical data. If the data is inaccurate, outdated, or incomplete, the analysis and the subsequent ...

Nature Methods - Data structure informs choice of color maps. ... we described methods for color-coding categorical data (August 2010) 1. Here we focus on creating color maps for quantitative data.