Prioritizing A/B testing projects in Data Science involves a smart resource estimation process: Identify Resources: Pinpoint what you'll need; time, budget, data, tools, and people are key players.

Design thinking is critical for developing data-driven business tools that surpass end-user expectations. Here's how to apply the five stages of design thinking in your data science projects.

Reframe projects to focus not on what you can learn from data but on how you can use data. Identify, define and prioritize specific use cases related to your business’s key challenges.

The business world is experiencing significantly increased interest in leveraging data and analytics to solve problems, and with good reason. According to Gartner, “by 2024 organizations that ...

Amanda E. Cravens is a social-science researcher at the US Geological Survey’s Forest and Rangeland Ecosystem Science Center in Corvallis, Oregon. Rebecca L. Nelson is an environmental and ...

This paper explores the strengths and weaknesses of CRISP-DM when used for data science projects. The paper then explores what key actions data science teams using CRISP-DM should consider that ...