A deep dive into using Kernel Density Estimation (KDE) for identifying traffic accident hotspots and improving road safety, including practical applications and case studies from Japan.
Bayesian data science offers a powerful framework for incorporating prior knowledge into statistical analysis, improving predictions, and informing decisions in a probabilistic manner.
Julia Robinson was a trailblazing mathematician known for her work on decision problems and number theory. She played a crucial role in solving Hilbert’s Tenth Problem and became the first woman elected to the National Academy of Sciences.
PDEs offer a powerful framework for understanding complex systems in fields like physics, finance, and environmental science. Discover how data scientists can integrate PDEs with modern machine learning techniques to create robust predictive models.
Explore the architecture of ordinal regression models, their applications in real-world data, and how marginal effects enhance the interpretability of complex models using Python.
Katherine Johnson was a trailblazing mathematician at NASA whose calculations for the Mercury and Apollo missions helped guide U.S. space exploration. Learn about her groundbreaking contributions to applied mathematics.
Learn how data science revolutionizes predictive maintenance through key techniques like regression, anomaly detection, and clustering to forecast machine failures and optimize maintenance schedules.
A comparison between machine learning models and univariate time series models for predicting emergency department visit volumes, focusing on predictive accuracy.
Leveraging customer behavior through predictive modeling, the BG/NBD model offers a more accurate approach to demand forecasting in the supply chain compared to traditional time-series models.
The log-rank test is a key tool in survival analysis, commonly used to compare survival curves between groups in medical research. Learn how it works and how to interpret its results.