avinash201199/DSA-KIT: A curated collection of DSA resources, problem sheets, roadmaps, and tools to help you go from zero to interview-ready.

A curated collection of DSA resources, problem sheets, roadmaps, and tools to help you go from zero to interview-ready. - avinash201199/DSA-KIT

Summary

DSA-KIT is a curated Markdown index of Data Structures and Algorithms learning resources, organized by topic and skill level. It links out to roadmaps, courses, books, problem sheets, visualizers, and communities rather than providing its own teaching material. Engineers use it as a single starting point when they need to find a resource for a specific DSA topic or interview-prep goal.

What it is useful for

• Finding a path for a specific area without searching the web, e.g. sliding window patterns under Arrays & Strings or segment trees and Fenwick trees under their own section.
• Discovering problem sheets for different levels: the Blind 75 and NeetCode 150 for beginners, Striver's SDE Sheet (191 problems) for intermediate, and CSES or AtCoder Educational DP Contest for advanced/competitive work.
• Locating visualizers such as Visualgo, the USFCA DS Visualizations, or the Recursion Visualizer when a concept is hard to grasp from text.
• Comparing free and paid options, from MIT 6.006 on OCW and CS50, to AlgoExpert, Educative, and NeetCode.io.
• Pointing self-taught engineers toward structured interview tracks, including behavioral prep (STAR method), system design (Alex Xu, Grokking System Design), and company-tagged LeetCode sets.

How engineers can use it

• Open the README on GitHub and use the Table of Contents to jump directly to the topic you need, like Trees & Graphs, DP, or Bit Manipulation.
• Pick one roadmap (NeetCode, Striver, roadmap.sh) and follow it sequentially instead of jumping between sources, which is the most common way people get stuck.
• For a recurring problem type, copy a template from the Cheat Sheets section, for example the "Sliding Window" or "Powerful Ultimate Binary Search" templates, into your personal notes.
• Bookmark the Visualizers & Tools links for quick reference when an algorithm isn't clicking from reading alone.
• Use it as a directory, not a course. The README is a list of links; the actual learning happens on the linked platforms.

Documented limitations to keep in mind: the list is a curated index, not a runnable tool, so there is no installation step. External links can rot over time, coverage skews toward widely-known resources and may miss niche topics, and the kit does not teach DSA on its own; you still need to solve problems on the platforms it points to. For the full and current list, read the README directly.