Choropleth Mapping in Python: A Practical Guide to Geographic Data Science
The geometry is the easy part of a choropleth map. Classification is where most maps actually go wrong, here's how to get it right in Python.
Machine Learning in Logistics: Real Benefits, Use Cases, and Lessons from the Field
Demand forecasting, route optimization, predictive maintenance: five ways ML is used in logistics, and what actually determines success in practice.
How to Analyze Research Papers Faster with AI: A Data Scientist’s Workflow
AI can't replace reading the papers that matter, but it's an excellent filter for deciding which ones do. Here's my actual triage workflow.
F1 Score in Machine Learning: Formula, Range, and How to Interpret It
F1 score, precision, and recall: what they mean, when to use each, and why F1 beats accuracy on imbalanced classification problems.
Artificial Intelligence vs. Machine Learning vs. Data Science: What’s the Real Difference?
AI, ML, and data science get used interchangeably, but they're not the same thing. Here's the practical distinction that actually matters for hiring and scoping.
How AI Can Generate Statistical Reports
How generated statistical reports should be produced: from fitted structure to sentences, with checkable claims.
Python Plotting for Data Science: A Practical Introduction to Matplotlib
From messy CSV to publication-ready figure: a practical walkthrough of Python plotting for data science using matplotlib and pandas.
Bayesian Model Selection Explained
Bayesian model selection and the marginal likelihood: how complexity is penalised without an added penalty term.
Seaborn Color Palettes: A Practical Guide for Data Science in Python
Choosing the wrong Seaborn color palette can undermine a good chart. Here's a practical, code-first guide to sequential, diverging, and qualitative palettes.
Explainable AutoML vs Black-Box AutoML
The difference between AutoML that reports a structure and AutoML that reports a score, and when each is appropriate.