British Journal of Cancer - Survival Analysis Part III: Multivariate data analysis – choosing a model and assessing its adequacy and fit ...

Whether you’re accessing data from internal sources or purchasing data from vendors, due diligence is essential to ensure your findings aren’t skewed or incomplete.

Data rarely comes in usable form. Data wrangling and exploratory data analysis are the difference between a good data science model and garbage in, garbage out.

Building a strong data analytics team helps in developing data maturity and communicating real-time data insights to the C-suite for effective decision-making.

Predictive analytics and predictive AI can help your organization forecast outcomes based on historical data and analytics techniques.

The rise of AI 'reasoning' models is making benchmarking more expensive, data from Artificial Analysis shows.