Pixel Process

Start Small | Iterate Often | Improve Always

Master the pieces to master the whole. From pixels to data points to lines of code, learn how to break down complexity, practice transferable skills, and build toward seeing the full picture.

Welcome to PxP!

Paths, Not Courses

This isn’t a standard course — it’s a playground and toolkit.

PixelProcess is designed for exploration, not checklists. You won’t always find step-by-step lessons or a strict progression. Each section offers building blocks: beginner to advanced, theory to implementation.

As with real-world work, you’ll need to spot your own gaps, follow your curiosity, and piece together what matters most to your workflow.

PxP offers guideposts and landmarks to help you chart your own course.

Hot Topics

Free to Use | Private by Design | Open for All

Every resource is open source, totally free, and never requires sign-in or tracking.

Project Navigation

Quickly find all relevant content with a search term using the 🔍 in the top right of every page. This will search the contents of the entire project. A quick and easy tool if you are not sureif content is avilable or where to look.

Three main content types are used each with a unique icon:

  • Web page for general content
  • Jupyter Notebook (NB) for code based tutorials and examples
  • Interactive Notebook (INB) runs code directly in your browser, no downloads needed

Jump In

Code Freely | Fail Safely | Start Quickly

Interactive quick start content to get new users reading, editing, and running code in minutes. Covers basic programming concepts in Python and R including datatypes, operators, variables, and more.

Check out the FAQs for career path information and answers to common questions when getting started like “Is Python a good language to learn?” and “How much math is required to program?”.

Jump In: Python

Jump In: Python → No downloads, no setup-jump into basic Python
Datatypes and Operators → Jump into Python - Part 1
Variables and Functions → Jump into Python - Part 2
Iteration and Flow Control → Jump into Python - Part 3
Errors and Experimentation → Jump into Python - Part 4
Hello World Challenge (NB) → Challenge Notebook for string formatting and variable usage
Expert Debugger Challenge (NB) → Challenge Notebook for string formatting and variable usage

Jump In: R

Jump In: R → No downloads, no setup-jump into basic R

Foundations

Learn Patterns | Develop Habits | Grow Confident

This section focuses on strong data analysis fundamentals for building automated, useful, and reproducible workflows. Learn about key packages and code for data input/output (I/O), cleaning, summarizing, analyzing, and visualizing data.

Check out the FAQs for information on why strong foundations are essential.

Datasets

Dataset Exploration → Guide to evaluating tabluar datasets
Dataset Exploration (NB) → Verify data dimensions, datatypes, and quality

Data Visualization

Data Visualization Basics → Turn data into stories with top visualization packages
Data Visualization Basics (NB) → Start turning data into stories
Data Viz Building Blocks → Core features behind data viz
Data Viz Building Blocks (NB) → Code examples for core features behind data viz

Advanced Data

Image Basics (NB) → Notebook on basic image properties

Machine Learning

Understand Data | Fit Models | Test Performance

Understand how machines learn with deep-dive, code based algorithm examples and complete analysis workflows that cover data I/O, splits, training, testing, and evaluation.

Check out the FAQs for common ML usage, metrics, and terminology.

Models & Metrics

What can ML do? → Overview of data and algorithms for ML
Random Forest (NB) → Learn how random forests work with a full notebook tutorial

Classification

Iris Classification (NB) → Complete classification pipeline based on Iris dataset

Regression

Housing Regression (NB) → Complete regression pipeline based on housing dataset

Workflow

Save Time | Avoid Errors | Create Flow

Create the right work environment and leverage the right tools for efficient workflow. From command line aliases, to project standards, to pro-tips for debugging, this section is designed to make your life easier.

Tools

Tools Overview → Editors, linters, and formatters, oh Py!
Troubleshooting Basics → Checklist for trouble-shooting and debugging
Scripts and Notebooks → When and why to use notebooks vs scripts
How to: Jupyter Notebooks → Intro to using and navigating notebooks

Customization

Command Line Interface → Don’t just learn CLI, optimize it
Start Your Journey

From experience, I can attest learning everything is overwhelming, exhausting, and unnecessary. Focus on what you want to build or understand. Learn what you need as you go. And don’t be afraid to change course — that’s part of the process, too.

Start small — expertise doesn’t happen in a day

Iterate often — progress comes through refactoring, revisiting, and rethinking

Improve always — focus on what matters now, and let your skills grow with your goals

Start where you are. Use what you have. Learn what you need

Programming is more about problem-solving than memorization. Looking up syntax, reading the docs, and debugging are required. Experience and expertise grow with time and effort. Start where it makes sense for you — revisit what you need, skip what you don’t. The best path is the one that fits your needs.

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.