Bayesian Data Science: The What, Why, and How
Bayesian data science offers a powerful framework for incorporating prior knowledge into statistical analysis, improving predictions, and informing decisions in a probabilistic manner.
Recent posts
Julia Robinson: Mathematician and Pioneer in Decision Problems
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.
Introduction to Partial Differential Equations (PDEs) from a Data Science Perspective
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.
Understanding Ordinal Regression: A Comprehensive Guide
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: The Mathematician Who Helped Launch America into Space
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.
The Role of Data Science in Predictive Maintenance
Learn how data science revolutionizes predictive maintenance through key techniques like regression, anomaly detection, and clustering to forecast machine failures and optimize maintenance schedules.
Data Visualization Best Practices
Discover best practices for creating clear and compelling data visualizations that communicate insights effectively.
Applying Hypothesis Testing in the Real World
See how hypothesis testing helps draw meaningful conclusions from data in practical scenarios.
Bayesian Inference Explained
Explore the fundamentals of Bayesian inference and how prior beliefs combine with data to form posterior conclusions.
A Primer on Simple Linear Regression
Understand how simple linear regression models the relationship between two variables using a single predictor.