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Browse articles on statistics, data science, machine learning, R, Python, open source and AI-assisted software.

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Kumo-Tabular vs XGBoost, CatBoost and LDA: Does Feature Engineering Still Matter?

machine-learning
statistics
ai

Kumo-Tabular vs tuned boosting and LDA: a small benchmark, plus why data preparation, interpretability and serving cost still matter.

Oct 31, 2026
Maciej Nasinski

England and Her Soldiers (1859): The Book That Made Statistics Visual

statistics
dataviz
history

Florence Nightingale’s rose diagram in an 1859 copy of England and Her Soldiers: what it shows, what it cannot prove, and how to recreate it in R.

Aug 31, 2026
Maciej Nasinski

Agents Are Easy. Products Are Still Hard.

programming
ai
agile

Lessons from delivering AI-assisted statistical-programming products: why AI agents make working code easy but the right product is still hard, with a gridify case study.

Aug 30, 2026
Maciej Nasinski

Are Em Dashes Really Suspicious? A Gutenberg Top-100 Check

text-analysis
statistics
programming

Are em dashes a sign of AI-generated text? A Python analysis of 100 top Project Gutenberg books finds em dashes in every book once double hyphens are counted.

Aug 29, 2026
Maciej Nasinski

R Package Downloads: Why CRAN Counts Are Proxies and What to Measure Instead

rprogramming

There is no global CRAN download count. What cranlogs measures and misses, how to get proxy counts in R, and better metrics for R package usage.

Oct 10, 2025
Maciej Nasinski

Understanding LLMs and Transformers: A Deep Dive into Modern AI

nlp
llm
transformers

How Transformers and large language models work, encoders versus decoders, and what you learn in the free Hugging Face LLM course, with pipeline examples.

Sep 13, 2025
Maciej Nasinski

Vibe Coding: Adopting the Exponential Future of AI-Assisted Development

programming

A practical approach to vibe coding with AI assistants: modular design, test-driven validation, recommended tools and models, and what to do when it goes wrong.

Sep 10, 2025
Maciej Nasinski

Visual Studio Code: Toolkit for Data Scientists

programming

Why Visual Studio Code is a complete toolkit for data scientists: Python and R extensions, GitHub Copilot, settings as code and reproducible devcontainers.

Apr 21, 2025
Maciej Nasinski

GLM Residuals in R: Pearson, Deviance and Quantile Residual Diagnostics

statistics

A practical guide to residual diagnostics for logistic and Poisson GLMs in R: raw, Pearson, deviance and quantile residuals, and how to spot misspecification.

Mar 23, 2025
Maciej Nasinski

Curse of Dimensionality: Lessons from the U.S. Air Force Cockpit Design

statistics

Why no U.S. Air Force pilot was average: the 1950s cockpit study as a lesson in the curse of dimensionality, with a multivariate normal simulation in R.

Jan 19, 2025
Maciej Nasinski

Open Source: Given enough eyeballs, all bugs are shallow

programming

What open source means, how projects like Linux work, common open source licenses, governance models, and the different ways you can contribute.

Jan 12, 2025
Maciej Nasinski

Mermaid Diagrams in Markdown: Flowcharts, Sequence Diagrams and Gantt Charts

programming

How to create flowcharts, sequence diagrams, Gantt charts and Sankey diagrams with Mermaid in Markdown, plus tools, alternatives and help from LLMs.

Jan 11, 2025
Maciej Nasinski

WebAssembly for Data Science: webR, Pyodide and Shinylive in the Browser

programming

How WebAssembly runs R and Python in the browser at near-native speed, and how webR, Pyodide, Shinylive and Quarto use it for interactive data science.

Jan 7, 2025
Maciej Nasinski

Python Enhancement Proposals (PEPs)

pythonprogramming

What Python Enhancement Proposals (PEPs) are, how the PEP process works, and the PEPs that shaped Python: PEP 8, PEP 20, PEP 484, PEP 572 and PEP 621.

Jan 2, 2025
Maciej Nasinski

Object-Oriented Programming for Data Science Developers [In-Depth]

programming
in-depth

An in-depth guide to object-oriented programming for data science developers, with classes and design patterns from scikit-learn and pandas in Python.

Jan 1, 2025
Maciej Nasinski

Containers for Data Scientists [In-Depth]

programming
in-depth

An in-depth guide to Docker, Podman and Apptainer containers for data scientists: Dockerfiles, images, CLI commands and reproducible R and Python environments.

Dec 23, 2024
Maciej Nasinski

Conjugate Priors: Harnessing Closed-Form Solutions in Bayesian Inference

statistics

Conjugate priors give closed-form Bayesian posteriors. Beta–Binomial, Gamma–Poisson and Normal–Normal examples in R show how beliefs update with data.

Dec 9, 2024
Maciej Nasinski

Bernstein–von Mises: Bridging Bayesian and Frequentist Worlds

statistics

How the Bernstein–von Mises theorem links Bayesian posteriors and frequentist confidence intervals in large samples, with a worked Normal–Normal example in R.

Dec 8, 2024
Maciej Nasinski

Celebrating a Classic: “How R Searches and Finds Stuff” by Suraj Gupta

rprogramming

Why Suraj Gupta’s How R Searches and Finds Stuff is still essential reading for understanding R environments, namespaces and scoping rules.

Aug 1, 2024
Maciej Nasinski

Graph Databases and GQL: An Introduction to the ISO Graph Query Language

programming

An introduction to graph databases, property graphs versus RDF, common use cases and tools, and GQL, the new ISO standard query language for graphs.

Jul 19, 2024
Maciej Nasinski

Scrum: Just a Framework, Agile: A General Philosophy

programming

Agile is a philosophy and Scrum is one framework for it. A guide to the Agile Manifesto, its twelve principles, and applying Agile beyond Scrum.

Jul 18, 2024
Maciej Nasinski

Exploring the Tinyverse: A New Standard in R Package Development

rprogramming

The tinyverse approach to R package development: why minimal dependencies improve stability, how it compares with the tidyverse, and how to reduce dependencies.

May 19, 2024
Maciej Nasinski

Stack Overflow Developer Survey Insights for Strategic Tech Planning

programming

How to use the annual Stack Overflow Developer Survey for strategic tech planning: reading the demographics, following trends and spotting rising technologies.

Apr 24, 2024
Maciej Nasinski

ggcall: Transparent R Packages with ggplot2

rprogramming

ggcall makes ggplot2 code reproducible inside R packages by recording the plot-building call, using metaprogramming to override ggplot() and the + operator.

Apr 13, 2024
Maciej Nasinski

GitHub Codespaces: Cloud Development Environments for R and Python

programming

How GitHub Codespaces provides ready-to-use cloud development environments, how .devcontainer files keep setups consistent, and example R and Python codespaces.

Apr 2, 2024
Maciej Nasinski

Pre-commit Hooks for R and Python Projects

programming

How to set up pre-commit hooks for R and Python projects to run linters and checks before every Git commit, including automated fixes with pre-commit.ci.

Apr 1, 2024
Maciej Nasinski

GitHub Student Developer Pack: Learn to ship software like a pro

programming

What the free GitHub Student Developer Pack and Teacher Pack include, such as Copilot, Codespaces and GitKraken, and how students and educators can get them.

Apr 1, 2024
Maciej Nasinski

Analyzing the Kasa Romana Lottery with Knapsack Algorithm [Updated]

knapsack
rprogramming

An R knapsack analysis and a modular arithmetic proof show that no subset of 1,002 sampled Kasa Romana coupons reaches the tested targets of 100 to 1,000.

Apr 8, 2023
Maciej Nasinski

Moving Beyond P-Values in Scientific Research: Insights from the ASA Statement and Frank Harrell

p-value
statistics

Why the ASA statement and Frank Harrell call for moving beyond p-values, and why effect sizes and confidence intervals should be reported alongside them.

Apr 7, 2023
Maciej Nasinski

Why Git is Here to Stay

git

Why Git remains the standard for version control, the fewer than 20 commands you need, CLI versus GUI tools, and how Git differs from GitHub.

Apr 6, 2023
Maciej Nasinski
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