Diogo Ribeiro

Data Science & Research Lead writing practical notes on statistics, machine learning, forecasting, and data engineering. The useful posts here should add analysis, examples, code, experiments, or field judgment rather than paraphrase search results.

I am Diogo Ribeiro, a Data Science and Research Lead writing about applied statistics, machine learning, forecasting, data engineering, and reproducible analytical workflows.

This site is intended to be a working technical notebook, not a content farm. A post has a reason to exist when it does at least one of these things:

  • explains a decision I have had to make in real analysis work;
  • includes original code, figures, simulations, experiments, or worked examples;
  • compares methods using explicit assumptions and tradeoffs;
  • turns a messy practical problem into a reusable checklist, diagnostic, or tool;
  • documents a case study, failure mode, or implementation detail that is hard to recover from generic tutorials.

Editorial Standards

Every new article should answer three questions before publication:

  1. Who is this for?
  2. What does it add beyond a generic explanation already available elsewhere?
  3. What evidence, example, experiment, or professional judgment supports the claims?

Articles should not be published only to target keywords. Thin summaries, broad paraphrases, and mass-produced topic pages should be revised, merged into a stronger guide, marked noindex, or removed.

When AI assistance is used, it is treated as drafting support. The article still needs human review, technical checking, and a reason to exist on this site. Claims about software, research, standards, and current tools should be checked against primary sources where practical.

Maintenance

Older posts are reviewed opportunistically. If an old article is kept, the goal is to make it more useful by adding examples, clearer assumptions, corrected links, current references, or a short note explaining its limitations.