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Internet Public Library

Learning paths

Step-by-step study paths built from IPL’s curated resources.

Beginner

  • Computer science foundations

    Build a solid base in computer science, from how machines run programs through algorithms, operating systems and networks, up to the limits of what computation can do.

    13 steps

  • Cybersecurity

    Start with networks and security thinking, then learn cryptography, web application flaws, vulnerability catalogs and system hardening through free hands-on labs and official references.

    14 steps

  • Data analysis

    Learn to find, clean, summarise and visualise data, from spreadsheets and SQL to statistics and dashboards, using free courses, open textbooks and public datasets.

    12 steps

  • Databases

    Learn to store and query data with SQL, design sound schemas and indexes, try document and key-value stores, and manage databases safely as they change.

    13 steps

  • Frontend development

    Build accessible, fast and responsive web interfaces, moving from how browsers render pages through HTML, CSS and JavaScript to modern tooling and frameworks.

    14 steps

  • JavaScript programming

    Learn JavaScript from its core syntax and functions through the browser DOM, asynchronous code and Node.js, ending with modules, TypeScript and UI libraries.

    13 steps

  • Python programming

    Learn Python from your first program to clean, idiomatic code, automate everyday tasks, manage packages and take your first steps into data science.

    12 steps

  • UX design

    Learn how people perceive and use interfaces, then practise research, structure, prototyping and testing until you can design and defend a usable product flow.

    13 steps

Intermediate

  • Backend development

    Build reliable server-side applications by mastering HTTP, a web framework, relational data, API design and the caching, messaging and architecture choices behind scalable systems.

    13 steps

  • DevOps and cloud

    Go from the Linux shell and Git to containers, automated delivery, infrastructure as code and Kubernetes, building the habits that keep software shipping reliably.

    14 steps

  • Machine learning

    Build models that learn from data, from classical algorithms and honest evaluation to neural networks and transformers, using official documentation and free university courses.

    13 steps

  • Mobile app development

    Build native apps for Android with Kotlin and iOS with SwiftUI, then go cross-platform with Flutter or React Native, add backend services, test them and ship them.

    11 steps