Information Geometry for Data Science: Curvature, Models, and Learning
Information geometry treats probability models as geometric objects, making it easier to reason about distance, curvature, uncertainty, and learning.
Recent posts
Discrete Mathematics for Data Science: States, Constraints, and Algorithms
Discrete mathematics is the part of mathematics that explains how data systems make decisions, count possibilities, represent relationships, and enforce constraints.
Bayesian Decision Theory for Data Science: From Uncertainty to Action
Bayesian decision theory connects statistical uncertainty to action by asking not only what is likely, but what decision is best under uncertainty.
Evaluating the ROI of Predictive Maintenance: A Practical Measurement Framework
Predictive maintenance only creates value when better predictions change maintenance decisions. This article explains how to measure that value without confusing model performance with business impact.
Data Visualization and Dashboards for Predictive Maintenance
Predictive maintenance dashboards should not merely display sensor data. They should help teams decide what to inspect, when to act, and which risks matter most.
Cloud Computing and Edge Analytics in Predictive Maintenance
Predictive maintenance systems rarely live entirely in the cloud or entirely at the edge. Effective architectures split work across sensors, gateways, plant systems, and cloud platforms.
Decision Curve Analysis: Measuring Whether Predictive Models Are Worth Acting On
Decision curve analysis evaluates predictive models by asking whether acting on their predictions produces better decisions than simple alternatives.
Fourier Analysis for Data Science: From Signals to Features
Fourier analysis is more than a signal-processing trick. It is a way to ask which cycles, rhythms, and scales explain variation in data.
Prevalence Shift and Base-Rate Drift in Machine Learning
Prevalence shift occurs when the base rate of the outcome changes, breaking thresholds, workloads, and probability interpretation even when the model ranking still looks good.
Label Noise in Supervised Learning: When the Target Cannot Be Trusted
Label noise is one of the most damaging data quality problems in supervised learning because it corrupts the target the model is trained to imitate.