Blog
Developments in automated analysis, new software, research commentary and notes on where the field is going.
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.
Business Process Automation in 2026: The Complete Guide for B2B Teams (With 12 Real Examples)
Business process automation (BPA) in 2026 = using software to execute entire end-to-end business processes, not just the steps between two apps. Where workflow automation connects tools,…
Trend vs Seasonality vs Change Points
How to distinguish trend, seasonality and structural breaks, and why confusing them produces confident nonsense.
Gaussian Process Kernels Explained
A practical guide to GP kernels: SE, periodic, linear, Matérn, changepoint and white noise, and what their products mean.
What Is Automated Exploratory Data Analysis?
Automated EDA explained: what profilers measure, the failure modes, and how to use the output properly.