What Data Leaders Must Fix Before AI Can Scale In 2026

In 2026, data leaders must focus on enhancing data foundations rather than feature expansion. That means prioritizing data ...
The Lancet

When to and when not to use machine learning in risk prediction models

Machine learning for health data science, fuelled by proliferation of data and reduced computational costs, has garnered ...
TechRepublic

How to use Excel’s Data Model to turn related data into meaningful information

How to use Excel’s Data Model to turn related data into meaningful information Your email has been sent Excel's Data Model feature allows you to build relationships between data sets for easier ...
Computer Weekly

DWP rejigs operating model for data transformation by 2030

The Department for Work and Pensions (DWP) has published a “data strategy” document that sets out what it believes it will ...
CMS Wire

Statistical Modeling and Machine Learning Coexist, Not Compete

Statistical modeling continues to deliver distinct value to businesses both independent of, and in concert with, machine learning. “Artificial intelligence” (AI) and “machine learning” are among the ...
GEN

New Statistical Model Improves Predictive Quality of Patient Genomic Data

A range of genetic factors can influence the onset of diseases like high blood pressure, heart disease, and type 2 diabetes, according to scientists. If we were to know how the DNA influences the risk ...
Nature

Count Data Modeling and Statistical Distributions

Count data modelling occupies a central role in statistical applications across diverse disciplines including epidemiology, econometrics and engineering. Traditionally, the Poisson distribution has ...
TechRepublic

Data Modeling vs. Data Analysis: An In-Depth Comparison

Data modeling refers to the architecture that allows data analysis to use data in decision-making processes. A combined approach is needed to maximize data insights. While the terms data analysis and ...