Data Modeling Articles

Matthew Urwin

Updated on May 22, 2025

In finance, quantitative modeling is the practice of organizing and interpreting data sets with mathematical formulas to identify trends in the broader markets. Here’s why it’s important, its key characteristics and the tools it involves.

Edoardo Romani

Updated on May 22, 2025

There are a lot of resources to help you become a better data scientist. Here’s my experience with one of them — the good and the bad.

Mike Hyzy and Erik Krisko

Updated on January 02, 2025

By merging the methods of Scrum and CRISP-DM, you can meet the needs of both the data scientist and stakeholder. Here’s how.

Erik Brown

Updated on December 19, 2024

Synthetic data has the potential to speed up and improve the building of AI models. Here’s why it may overshadow real data models by 2030.

Thulasi Rangan Jayakumar

Updated on November 19, 2024

Multicollinearity is a common data analysis and statistics issue that can impact the accuracy and reliability of regression model results. Here’s how to address it.

Ted Lango

Updated on October 16, 2024

Without good data, AI is useless. Our expert explains how to make sure your data will support your AI plans.

Claire D. Costa

Updated on October 15, 2024

Data science is a booming industry. Try your hand at these projects to develop your skills and keep up with the latest trends.

Sara A. Metwalli

Updated on September 05, 2024

The Gamma distribution is one of the most popular and commonly used continuous time distributions because it has many real-world applications. Here are some examples.

Vasu Sattenapalli

Updated on August 19, 2024

Generative AI and large-language models allow non-technical users to work directly with data, but make sure you implement these systems carefully.

Derek Ashmore

Updated on August 19, 2024

The key to optimization might just be automation.