Climate scientists are confronting a hard truth: some of the most widely used models are struggling to keep up with the pace ...

Heterogeneous graphs organize data with nodes and edges, and have been widely used in various graph-centric applications.

Data modeling has always been a task that seems positioned in the middle of a white-water rapids with a paddle but no canoe. On one side of the data modeling rapids are the raging agilists who are ...

Sparse data can impact the effectiveness of machine learning models. As students and experts alike experiment with diverse datasets, sparse data poses a challenge. The Leeds Master’s in Business ...

Data modeling is the process of defining datapoints and struc­tures at a detailed or abstract level to communicate information about the data shape, content, and relationships to target audiences.

There are data about practically everything these days, and they can be used to try to answer any number of questions. Do clinical trials really show a drug works? Can surveys really signal who’s ...

Data modeling, at its core, is the process of transforming raw data into meaningful insights. It involves creating representations of a database’s structure and organization. These models are often ...

The Covid-19 pandemic reminded us that everyday life is full of interdependencies. The data models and logic for tracking the progress of the pandemic, understanding its spread in the population, ...