This repository is a community-maintained study hub for the GATE Data Science and Artificial Intelligence (DA) exam. It provides syllabus-aligned resources including detailed markdown guides for seven core subjects (Probability & Statistics, Linear Algebra, Calculus & Optimization, Programming & Algorithms, Database Management, Machine Learning, Artificial Intelligence), verified previous-year question papers (2024–2026) with checksums, and focused Jupyter notebooks for practice. The primary delivery mechanism is a Flask web application that renders the markdown guides into an interactive curriculum explorer with topic checklists, progress tracking, presentation mode, and a radial memory map. The webapp can be exported as a static site for GitHub Pages deployment, and a CI/CD pipeline (GitHub Actions) runs tests, link audits, and checksum verification on each push. The repository also includes link validation scripts for pull requests and a set of deterministic example notebooks generated programmatically. While the core is an application, it also contains research-like elements such as exam pattern analysis, stale-link audits, and educational notebooks, but these are secondary to the main purpose of providing a structured, browsable study resource.
This repository is a curated collection of Computer Science fundamentals resources for placement/interview preparation. It contains no executable code, applications, or software components. Instead, it aggregates PDFs, markdown notes, cheatsheets, and interview question banks across 7 core CS domains: Computer Networks, DBMS & SQL, Data Structures & Algorithms, Object-Oriented Programming, Operating Systems, Software Engineering, and System Design. The repository also includes general career resources: a list of companies hiring freshers off-campus, HR interview questions, LeetCode problem sets, a cover letter template, and AI prompts for interview preparation. The content appears sourced from various educational platforms (GeeksforGeeks, Striver's sheets, etc.) and is organized as a reference library for students preparing for technical interviews. The markdown files (README.md, CN-Roadmap.md, Computer-Network-Notes-for-Placements-and-GATE.md, and topic READMEs in each folder) provide structured topic outlines and resource links, while the bulk of the content resides in 50+ PDF/docx files that are not directly readable in this analysis.
This repository is a curated collection of study resources for the Certified Kubernetes Security Specialist (CKSS) exam. It consists of a README.md file containing organized links to official Kubernetes documentation and third-party references covering all exam domains (cluster setup, hardening, system hardening, microservice vulnerabilities, supply chain security, and monitoring/logging/runtime security). A bookmark HTML file provides browser-accessible versions of the same links. A PDF of the CKSS curriculum is also included. There is no executable code, data pipelines, models, or software architecture. The repository functions purely as a curated study guide or knowledge base, not as a functional application or data analysis project.