Self-Taught Computer Science Curriculum

This repository is a structured educational resource designed for mastering data structures and algorithms using the Java programming language. It functions as a comprehensive curriculum and study roadmap, providing the materials necessary to build proficiency in core computer science fundamentals for technical interview preparation. The project organizes its content through a hierarchical directory structure that maps to a logical progression of topics. It utilizes a standardized format for coding exercises and documentation, ensuring that learners can follow a consistent syllabus while practicing idiomatic Java syntax and algorithmic implementation techniques. The repository covers a broad range of computer science skill development, including lecture notes, coding assignments, and problem-solving exercises. These resources are maintained within a version-controlled environment to support systematic learning and progress tracking for software engineering candidates.

This project is a technical interview preparation guide and resource kit designed for software engineering job placement. It functions as a markdown resource repository that provides a structured curriculum for computer science fundamentals and a dedicated learning roadmap for data structures and algorithms. The repository organizes study materials into a sequential path, guiding users from basic arrays through to advanced dynamic programming. It includes curated collections of coding practice links, interview puzzles, and strategic notes focused on optimizing time and space complexity. Beyond coding practice, the project covers core academic domains including operating systems, database management systems, and computer networking. It integrates these theoretical reviews with practical roadmaps to assist in transitioning from academic study to professional career planning.

This project is a comprehensive educational roadmap designed to guide software engineers through the mastery of computer science fundamentals and technical interview preparation. It provides a structured, dependency-aware learning path that organizes complex computing concepts into a hierarchical curriculum, enabling users to build a professional engineering foundation through iterative study and practical implementation. The curriculum distinguishes itself by integrating theoretical knowledge with professional development, offering a unified index of cross-referenced resources including books, academic papers, and video tutorials. It emphasizes the standardization of algorithmic efficiency through asymptotic complexity analysis and provides granular, modular topic decomposition to facilitate focused, incremental learning across vast technical domains. Beyond core algorithms and data structures, the repository covers a broad capability surface including system architecture design, distributed systems, computer security, and advanced mathematical modeling. It also provides strategic guidance for the entire hiring lifecycle, from resume optimization and behavioral interview preparation to long-term career growth. The entire knowledge base is maintained as a version-controlled, markdown-driven repository, allowing for a platform-agnostic and collaborative approach to technical education.

This project is a comprehensive technical knowledge base and study guide focused on data structures, algorithms, and computer science fundamentals. It provides a curated collection of tutorials and educational resources designed to support technical growth and academic learning. The repository distinguishes itself through a heavy emphasis on visual learning, utilizing mind maps, diagrams, and illustrated breakdowns to explain complex algorithmic logic. It further supports career readiness by providing a repository of company-specific interview questions and real-world candidate experiences. The content covers a broad range of computer science topics, including linear and non-linear data structures, operating system fundamentals, and a library of open-source electronic books. These resources are organized into structured learning paths that bridge theoretical foundations with practical implementation guides.

4noobs is an open-source educational resource index that organizes structured, beginner-friendly learning paths across a wide range of technology domains. It delivers all content as static Markdown files managed under Git version control, with a curated-index-based navigation system that groups tutorials and roadmaps by technology domain into separate silos. The project provides step-by-step learning roadmaps for programming languages from Assembly to TypeScript, along with framework guides for tools like Angular, Vue, Django, and Spring. It includes certification preparation guides aligned with industry credentials such as the Linux Professional Institute certification, and offers tutorials for operating systems, database management, and development tools like Git, Docker, and Vim. The resource also covers automation testing frameworks including Selenium, Cypress, and Playwright, as well as UI/UX design principles and software quality topics. The content is structured to allow non-linear exploration through internal hyperlinks connecting related roadmaps and tutorials. The entire curriculum is version-controlled, enabling collaborative updates and historical tracking of changes to the learning materials.

CS-Xmind-Note is a collection of structured mind maps and conceptual diagrams serving as a comprehensive knowledge base for computer science fundamentals. It functions as an academic reference and study guide, organizing core subjects into a visual mapping of interdependent technical concepts. The project utilizes an XMind-compatible schema to model complex domains through hierarchical nodes and relational concept mapping. This approach allows for the visual representation of technical layers, linking hardware specifications to software abstractions. The knowledge base covers several primary academic areas, including computer architecture, operating systems, and computer networking. It also provides detailed references for data structures, database system theory, and information security concepts.

This project is a curated technical resource directory and software engineering learning roadmap. It serves as a computer science study curriculum and professional development framework, providing staged progressions for mastering programming languages, data structures, and full-stack development. The repository functions as a career preparation guide, offering strategic frameworks for resume building, technical interview practice, and internship application targeting. It includes a system for identifying income opportunities and managing a professional social presence to increase visibility. The project covers a broad range of capability areas, including detailed learning paths for cybersecurity, backend development, and system design. It further provides guidance on job application strategies, such as extracting hiring leads and performing strategic outreach, alongside instructions for building and deploying full-stack projects.

This project is an interactive programming curriculum and educational system designed to teach computer science and software engineering. It provides a structured set of courses and professional roadmaps focused on backend engineering, DevOps, and systems fundamentals. The platform is distinguished by an AI-powered coding tutor that provides Socratic guidance and contextual hints to help students find solutions independently. It features a browser-based code sandbox using WebAssembly to eliminate local environment setup, alongside automated test-based grading and spaced-repetition logic to reinforce difficult concepts. The curriculum covers a broad range of technical domains, including programming languages such as Go, Python, and TypeScript, as well as relational database design, container orchestration with Kubernetes, and cloud operations. It also includes professional development resources for technical interview preparation and portfolio construction. Learning engagement is managed through gamified incentives like experience points and leaderboards, while progress is tracked via sequenced learning paths and AI-generated coding challenges.

This project is a comprehensive curriculum for mastering computer science fundamentals and preparing for technical interviews. It provides over 120 interactive Python coding challenges that focus on algorithmic skill development, data structure implementation, and logical problem solving. The learning experience is delivered through a series of executable notebooks that combine instructional content with hands-on coding exercises. Each challenge is self-contained and relies on automated unit tests to verify the correctness of user-implemented solutions against predefined constraints and edge cases. To support long-term retention, the repository also includes a set of digital flashcards designed for spaced-repetition study of core programming concepts and design patterns. The curriculum covers a broad range of topics, including arrays, strings, linked lists, stacks, queues, graphs, trees, recursion, dynamic programming, and bit manipulation. All solutions are implemented using the Python standard library to ensure portability and focus on fundamental language features.

This project is a centralized repository and academic resource aggregator designed to guide students through a structured computer science curriculum. It provides a comprehensive roadmap of foundational courses and technical materials, helping learners navigate the transition from introductory programming to advanced software engineering proficiency. The repository distinguishes itself through a community-driven approach, where study paths and resource collections are refined and expanded via peer feedback and collaborative contributions. By organizing high-quality lecture notes, assignments, and reading lists from top-tier university programs into a logical progression, it enables self-directed learners to bridge technical skill gaps and optimize their academic performance. The content is maintained as a version-controlled collection of markdown files, ensuring that the learning path remains transparent and accessible. This documentation is compiled into a static format, allowing users to navigate complex academic sequences and track their progress across platforms without the need for dynamic backends.

This project is a visual study guide and educational resource for linear algebra. It consists of a collection of graphic course notes and image-based presentations designed to simplify the study of vector and matrix operations. The content is structured as a series of graphic summaries and visual aids that follow the curriculum and teachings of Gilbert Strang. It translates abstract algebraic operations, matrix algorithms, and factorizations into intuitive geometric diagrams and spatial representations. The repository functions as a mathematics course supplement, providing modular slides and figures that map to specific academic chapters and lessons.

This repository is a curated collection of practical software development challenges designed to help developers practice coding skills and build functional applications. It functions as a structured curriculum that guides learners through building real-world software across various technical domains and programming languages. The project serves as a resource for both skill development and portfolio building, allowing developers to demonstrate their technical capabilities and problem-solving experience. By implementing a consistent set of challenges, users can master core syntax, explore advanced design patterns, and prepare for technical interviews through hands-on coding practice. The repository utilizes a standardized approach to content management, employing markdown-based specifications and declarative data structuring to maintain its collection. These project definitions are tracked via version control and processed through static site generation to ensure a consistent and accessible format for all users.

This project is a comprehensive educational platform designed to facilitate the mastery of computer science algorithms and data structures. It provides a structured learning curriculum, a library of practice problems, and an integrated toolkit that supports both academic study and competitive programming preparation. By combining theoretical roadmaps with practical implementation exercises, the system enables users to build a deep understanding of core computational concepts. The platform distinguishes itself through its focus on integrated learning and visual clarity. It offers AI-powered guidance and editor-native plugins for popular development environments, allowing users to access algorithmic templates and conceptual references directly within their coding workflow. To assist with the comprehension of complex logic, the project includes an interactive visualization suite that renders recursive processes and data structure operations, such as graph connectivity and search strategies, in real-time. Beyond its core educational content, the project provides specialized utilities for competitive programming, including standardized input-output bridging and environment configuration tools. These features ensure that users can efficiently translate their algorithmic knowledge into solutions for assessment platforms. The repository serves as a centralized resource for technical skill acquisition, offering a systematic approach to navigating advanced topics and refining problem-solving methodologies.

This project is a comprehensive, community-curated knowledge base designed to support software engineers in mastering both fundamental computer science principles and practical industry methodologies. It serves as a centralized reference library that aggregates technical resources, academic literature, and professional guidance to facilitate systematic skill acquisition across the entire software development lifecycle. What distinguishes this repository is its holistic approach to the engineering profession, which bridges the gap between theoretical knowledge and career-oriented development. Beyond core technical topics like system architecture, distributed systems, and algorithmic design, the project provides extensive guidance on professional growth, including resume optimization, soft skills, and strategies for maintaining mental health and productivity in demanding technical environments. The repository covers a broad capability surface, ranging from low-level system concerns such as memory management and data structures to high-level practices in platform engineering and software craftsmanship. It also incorporates resources for collaborative development, security protocols, and interactive learning, ensuring that developers have access to authoritative information for both daily problem-solving and long-term career advancement. The content is structured as a hierarchical collection of markdown files, maintained through a version-controlled, community-driven workflow that ensures the information remains accurate and relevant as industry standards evolve.

This project is a comprehensive educational framework and curriculum designed to transition beginners into proficient security engineers. It provides a self-taught hacking curriculum centered on mastering system internals, programming, and attack techniques through structured pedagogical paths and recursive learning. The framework distinguishes itself by integrating a productivity system specifically for engineers, which combines block-based time scheduling and incremental task management to prevent burnout and overcome procrastination. It further connects technical growth to professional advancement through strategies for public knowledge sharing, portfolio integration, and the monetization of security expertise. The technical scope covers foundational computer science, network protocols, and low-level system study. It guides users through the development of original security tools and automation scripts while providing a methodology for selecting specialized domains such as malware analysis or digital forensics.

This project provides a comprehensive mobile development curriculum designed to guide learners through the technical milestones required to build cross-platform applications. It functions as a structured software engineering learning path, organizing essential programming concepts and technologies into a logical sequence that spans from foundational knowledge to advanced proficiency. The roadmap utilizes a non-linear, hyperlink-based knowledge map to connect related development topics, allowing users to navigate complex technical domains at their own pace. By structuring educational content into hierarchical markdown files, the project enables community-driven updates and version-controlled evolution of the learning material. The curriculum covers the full spectrum of mobile software engineering education, focusing on the core practices necessary to master the Flutter framework. It translates these structured text files into a navigable web interface to provide a consistent and accessible learning experience for developers building applications for both iOS and Android.

Developer Roadmap is a community-driven platform that provides structured, graph-based learning paths for software engineering. It serves as a comprehensive knowledge repository where technical domains are organized into visual sequences to guide professional skill acquisition and career growth. The project distinguishes itself through a collaborative ecosystem that enables users to contribute roadmaps, curate industry best practices, and maintain professional profiles. It integrates diagnostic assessment frameworks to evaluate technical proficiency, helping developers identify knowledge gaps and prepare for professional interviews through targeted learning sequences. Beyond its core mapping capabilities, the platform offers practical project ideas and interactive tutoring to reinforce engineering concepts. It provides a centralized space for the community to share resources, track progressive skill development, and navigate complex technical landscapes.

This project is a pedagogical implementation of a hash table in C, built from scratch using open addressing and linear probing for collision resolution. It serves as a computer science algorithm demo, demonstrating how to construct a fundamental key-value store at a low level. The implementation covers the core operations of an associative array: inserting a key-value pair, looking up a value by its key, and deleting a pair. It uses a hash function to compute storage locations, maps hash values to array indices with the modulo operator, and resolves collisions by scanning sequentially through a contiguous memory block for the next available slot. The project walks through building an open-addressed hash map that stores all key-value pairs directly in a single, pre-allocated array, rejecting duplicate keys on insert and doing nothing when attempting to delete a non-existent key.

This project is a professional development repository that provides structured learning paths for individuals pursuing careers in data-centric engineering and artificial intelligence. It functions as a competency benchmarking framework, defining the core knowledge areas and technical milestones required to achieve proficiency in specialized domains. The repository distinguishes itself through hierarchical knowledge graphing, which organizes complex technical subjects into nested tree structures to create clear, progressive learning sequences. By centralizing curated educational resources and industry-standard curricula, it streamlines the process of self-directed study for roles ranging from data engineering to deep learning. The content is maintained using markdown-based storage, allowing for version control and consistent updates across multiple technical roadmaps. These roadmaps cover a broad capability surface, including the design of scalable data systems, the application of statistical models, and the mastery of foundational mathematical and database principles.

This project is an educational resource and reference library designed to teach fundamental data structures and algorithmic problem-solving. It provides a structured pedagogical framework that organizes complex technical concepts into a logical progression, helping learners understand how data is organized, stored, and processed to solve computational problems efficiently. The repository distinguishes itself through a multi-language codebase that maintains parallel, consistent implementations of core algorithms and data structures across various programming languages. It bridges the gap between abstract theory and concrete execution by utilizing visual-conceptual mapping, including diagrams and step-by-step walkthroughs, alongside complexity-driven design analysis to evaluate the time and space efficiency of different approaches. The content covers a broad spectrum of computer science fundamentals, ranging from linear structures like arrays, linked lists, stacks, and queues to complex hierarchical models such as trees, graphs, and hash tables. It also provides deep dives into advanced algorithmic paradigms, including systematic search strategies like backtracking and optimization techniques using dynamic programming. The materials are designed to serve both as a foundational curriculum for students and as a practical tool for software engineering practitioners preparing for technical assessments. The documentation is structured to allow users to navigate from basic definitions to advanced implementation details, making it a versatile resource for building a strong conceptual foundation in computer science.