13 free resources on this topic. Everything here is free and hand-picked. You can also search within this topic.
Newsletter teaching data science fundamentals to self-taught learners, written by David Langer. Free to read.
GitHub's free article library explaining version control, DevOps, CI/CD, security practices and AI-assisted development in plain terms.
From the site: GitHub is where people build software. More than 150 million people use GitHub to discover, fork, and contribute to over 420 million projects.
Why I recommend it: Good plain-English explainers for the terms that show up in job descriptions — read the DevOps and CI/CD ones before an interview.
The official free React docs, including the interactive "Learn React" course that teaches components, state and hooks from scratch with in-browser exercises.
From the site: React is the library for web and native user interfaces. Build user interfaces out of individual pieces called components written in JavaScript. React is designed to let you seamlessly combine components written by independent people, teams, and organizations.
Why I recommend it: If you want to build web interfaces, start with the official tutorial rather than a paid bootcamp — it is free and better maintained.
Free guides explaining PC components — memory, power supplies, cooling, cases — and how to choose and assemble them for a first build.
Why I recommend it: Free and clear on how the parts fit together; it is a manufacturer's site, so compare prices and specs elsewhere before buying.
MIT's combined catalogue of courses, programs and open learning materials across every department, with a filter for the free ones and for those offering certificates.
Why I recommend it: Use the "Free" filter first — there is an enormous amount of MIT teaching material at no cost, including full lecture notes and video.
A large free archive of practitioner-written tutorials and explainers on machine learning, statistics, data engineering and AI, from beginner to advanced.
Why I recommend it: Quality varies by author, but the beginner explainers are among the easiest free routes into data work.
Personal essays from OpenAI's president on engineering careers, hiring, self-teaching and building technical teams.
Why I recommend it: His writing on how he taught himself and how he hires is the useful part — read those posts first.
The official home of Rust, with the free online book, guided tutorials, standard library docs and installer for a memory-safe systems language used across infrastructure and AI tooling.
Why I recommend it: Everything you need to learn Rust is free here — start with "The Book," it is one of the best free programming texts online.
Free downloadable guides, cheat sheets and ebooks on Linux, the command line and open-source software from the It's FOSS team.
Why I recommend it: Good beginner-friendly Linux material — the cheat sheets are worth keeping open while you practise.
Free articles, tutorials and downloadable guides on open-source tools, Linux, containers and community-run projects, written by practitioners.
Why I recommend it: A good habit read — practical how-tos rather than product marketing.
A six-month paid software engineering apprenticeship aimed at career changers, self-taught coders, and bootcamp graduates from non-traditional tech backgrounds.
In plain terms: This five-month software engineering apprenticeship is designed for bootcamp graduates and self-taught coders without a computer science degree. You can apply to work on real coding projects, build your technical skills, and receive direct mentorship from experienced engineers.
Why I recommend it: Small cohort, so apply the day it opens. Self-taught with shipped side projects is exactly the profile they describe.
A step-by-step personal account of self-teaching, portfolio building, and applying until landing a first developer role without a computer science degree.
Why I recommend it: Copy the process, not the timeline. Everyone's runway is different, and comparing yours to a blog post is a fast way to quit.
A long crowd-sourced discussion among working engineers arguing both sides of the degree question, with detail on where formal study helps and where self-teaching is sufficient.
Why I recommend it: Read the disagreements, not just the top comment. The pattern: degrees help with first-job screening, portfolios help with everything after.
