About

Origin | Values | Source

What’s with the pixels? And who decided that anyway?

What is the Pixel Process?

The pixel process is a powerful method for learning, and not just for data and computer science.

To work effectively with image data, you need to understand what’s in a single pixel: its value range, color representation (grayscale, RGB, RGBA), and image grid. Understanding a pixel is easy, while understanding an image is much more difficult. Even the most advanced computer vision techniques rely on pixel level data.

Pixels may seem small and basic, but they are the foundation for creating stunning visuals and impactful analytics.

Beyond Images

Complex systems often follow the principle: You have to understand the basic components before combining them into something greater.

  • Pixels → Images → Videos

  • Words → Sentences → Books

  • Numbers → Geometry → Calculus

  • Nucleotides → Genes → DNA → Chromosomes

A true understanding of higher level processes relies on an indepth understanding of lower ones.

Set Goals, Focus Effort

The pixel process is about breaking the complex into the manageable. Create a roadmap with achievable goals and notable milestones to master complex systems.

Equally important, is understanding that mastering everything is untenable. Get better results by having a focused objective, so you can efficiently build the skills you need.

Small steps, big impact—that’s the PixelProcess.

Values

Attribution, Non-Commercial, Share Alike License

This project is licensed under the Creative Commons Attribution-NonCommercial-Share Alike 4.0 International License (CC BY-NC-SA 4.0).

What does this mean in plain language?

  • ✅ You may: Share, copy, and adapt this content for any non-commercial purpose

  • ✅ You must: Give appropriate credit, provide a link to the license, and indicate if changes were made

  • ✅ You must: Use the same license for work based on this content and material

  • 🚫 You may not: Use this content for commercial purposes.

In short: learn from it, remix it, improve it—but not for profit

Privacy First

Your privacy matters here. Tracking and analytics are disabled as much as possible. This site does not record user activity beyond basic traffic collected by GitHub Pages related to page visits. Voluntary feedback can optionally contain user information if a response is desired. This site can not control any browser based or computer based tacking information.

This means:

  • No automatic progress tracking

  • **No hidden data collection or ad targeting

If this changes, it will be clearly documented.

Ad-Free and Hassle-Free

This project exists to provide value, not generate income.

  • No ads

  • No pop-ups

  • No paywalls

Why you might ask? Because I get annoyed with those things when I am trying to find resources and work through problems. This site is designed to function how I prefer sites I visit to function.

Maintained by One Person

This is a personal project, designed, and maintained by one human.

What that means:

  • Please be patient with updates or fixes

  • Minor quirks may exist—thank you for understanding

  • No direct moderation or code review is available right now

If you want to improve or extend the project:

➡️ Send Feedback

➡️ Fork the repository and build your version!

Thank you for being here and supporting the vision of accessible, thoughtful content without unnecessary friction.

Meet the Dev

D. Bugz, PhD

Developer

Data scientist with years of experience and a PhD in Psychology. He loves to share knowledge and specializes in automation, reproducibility, and troubleshooting. More than a decade of scientific research and programming with Python, R, and SQL, applied across diverse data types including neuroimaging, biological samples, surveys, and financial data. Skilled in methodologies including computer vision, natural language processing, classification/regression, time-series analysis, and both supervised and unsupervised learning. Proficient with cloud services (AWS, Azure), Git, GitHub, and Jupyter, and dedicated to optimizing workflows for research and industry applications.

🛠 Tech Stack

  • Python
  • R
  • SQL
  • Git
  • SPSS
  • MATLAB
  • Jupyter

🧪 Methods

  • Predictive Modeling
  • Computer Vision
  • Natural Language Processing (NLP)
  • Experimental Design
  • Exploratory Data Analysis (EDA)

⛰ Best Practices

  • Production
  • Stardard Operating Procedures (SOPs)
  • Packaging
  • Documentation
  • Version Control

🗂 Data Types

  • Tabular
  • Time Series
  • Images
  • Text

👯 Extracurricular

  • Reading Fantasy and Sci-Fi
  • Walking my Teammates
  • Cooking
  • Video Games

Teammates

Crash

Chief Security Officer

Tux

Unpaid Intern

My Learning Project, Too

This site isn’t just for sharing what I know — it’s also where I figure things out. Every post reflects a step in my own process: experiments, revisions, and occasional wrong turns.

Share to all — data and tech savvy shouldn’t be paywalled

Learn by doing — every bug, every fix, every refactor teaches something

Keep growing — the goal isn’t mastery, it’s momentum

Welcome to PixelProcess

Hope you find something useful here — or learn from my mistakes and improve from that!

Note: Pixel Process is an actively growing resource. Code examples are tested and reliable, though minor text updates are ongoing. Interactive pages and notebooks may take a moment to load. Have feedback? Share it here.