  Data scientists across disciplines are increasingly in need of exploratory
analysis tools for data sets with a high volume of features of mixed data type
(quantitative continuous and discrete categorical). We introduce Sirius, a
novel visualization package for researchers to explore feature relationships
among mixed data types using mutual information. The visualization of feature
relationships aids data scientists in finding meaningful dependence among
features prior to the development of predictive modeling pipelines, which can
inform downstream analysis such as feature selection, feature extraction, and
early detection of potential proxy variables. Using an information theoretic
approach, Sirius supports network visualization of heterogeneous data sets
(consisting of continuous and discrete data types), and provides a user
interface for exploring feature pairs with locally significant mutual
information scores. Mutual information algorithm and bivariate chart types are
assigned on a data type pairing basis (continuous-continuous,
discrete-discrete, and discrete-continuous). We show how this tool can be used
for tasks such as hypothesis confirmation, identification of predictive
features, suggestions for feature extraction, or early warning of data
abnormalities. The accompanying website for this paper can be accessed at
https://sirius.universalities.com/. All code and supplemental materials can be
accessed at https://osf.io/pdm9r/.
