# The-Complete-FAANG-Preparation: A Curated Study Repository for Coding Interviews > A review of AkashSingh3031's MIT-licensed interview-prep repository, covering its DSA sheets, technical-subject folders and notebook-based solutions, plus the gaps a self-directed candidate should expect. **AkashSingh3031/The-Complete-FAANG-Preparation** — Dive into this repository, a comprehensive resource covering Data Structures, Algorithms, 450 DSA by Love Babbar, Striver DSA sheet, Apna College DSA Sheet, and FAANG Questions! 🚀 That's not all! We've got Technical Subjects like Operating Systems, DBMS, SQL, Computer Networks, and Object-Oriented Programming, all waiting for you. - Repository: https://github.com/AkashSingh3031/The-Complete-FAANG-Preparation - Website: https://www.sparklmind.com - Stars: 12,256 · Forks: 2,628 - Language: Jupyter Notebook - License: MIT - Published: 2026-09-21 · Updated: 2026-09-21 · Language: en - Canonical page: https://hysenlabs.com/projects/akashsingh3031-the-complete-faang-preparation ## What The-Complete-FAANG-Preparation Actually Solves Interview preparation material is scattered. A candidate typically ends up with one PDF of DSA problems, a second list from a different creator, a set of hand-written notes on operating systems, and a folder of half-finished code. Nothing shares a directory structure, and nothing agrees on ordering. This repository is an attempt to put those pieces under one roof. The README describes it as a resource covering Data Structures, Algorithms, 450 DSA by Love Babbar, Striver DSA sheet, Apna College DSA Sheet, and FAANG Questions, with Technical Subjects like Operating Systems, DBMS, SQL, Computer Networks, and Object-Oriented Programming. The audience is the self-taught candidate or the student preparing without a bootcamp or a paid cohort. It is not a judge, not a course platform, and not a spaced-repetition tool. It is a GitHub repository you clone, and the primary language listed is Jupyter Notebook, which tells you the solutions are largely presented as notebooks rather than as a compiled, testable project. ## How the Repository Is Organised on Disk The layout is the mechanism. There is no build system, no package manifest and no test runner. What you get is a numbered top-level structure: 1]. DSA + CP/, 2]. Technical Subjects/, 3]. System Design/, 4]. Projects/, 5]. Important Books and Resources/, 6]. Behavioural Interview/, plus a 0]. Miscellaneous Stuff/ folder, README.md, CONTRIBUTING.md, CODE_OF_CONDUCT.md, Learn.md, Repository_Status.md and images/. The numbered prefixes are deliberate: they impose an order on the browser view of the repository, so a newcomer sees DSA before system design before behavioural material. The DSA folder is where the named sheets live. The Technical Subjects folder holds the notes for OS, DBMS, SQL, networks and OOP. System Design and Behavioural Interview are separate because they are assessed differently in interviews. That separation is the whole architecture. Because the content is notebooks and markdown rather than modules, there is nothing to import and nothing to execute as a suite; the repository is a reading and practice surface. The trade-off is honest: you get breadth and a clear directory map, but you also get no automated check that a solution in a notebook still compiles or produces the expected output. ## Cloning It and Opening a First Notebook There is no package to install. The README does not publish an install command, a pip package or a release artifact, and the repository has no tagged releases. The route in is to clone the repository from the URL given in the README, then open a notebook in whatever environment you already use for Python work. The README's own link points at the master branch, which is the default branch listed for the repository. Run the clone from a directory where you keep study material. ## A First Real Use: Pick One Sheet and One Subject The repository contains several overlapping problem lists, and working through all of them is redundant. A more practical first pass is to choose one DSA sheet and one technical subject, then treat the rest as reference. Once the clone is on disk, open the repository in your file browser or editor and confirm the numbered folders are present. What you should see is the set of directories described in the repository layout: 0]. Miscellaneous Stuff/, 1]. DSA + CP/, 2]. Technical Subjects/, 3]. System Design/, 4]. Projects/, 5]. Important Books and Resources/, 6]. Behavioural Interview/, alongside LICENSE and README.md. From there, open a notebook inside 1]. DSA + CP/ in Jupyter and run the cells to see the solution for that problem. The repository publishes no commands for this step, so the exact invocation depends on your local Jupyter setup. ## Where the Repository Stops Short The clearest limitation is that this is a collection, not a curriculum. Nothing in the repository enforces that you finish the 450 DSA by Love Babbar list before touching the Striver DSA sheet, and nothing tracks which problems you have solved. If you need a schedule, streak tracking or graded feedback, this is the wrong tool; a platform that runs your code against hidden tests will serve you better. The second limitation is verification. With Jupyter Notebook as the primary language and no test harness in the tree, a notebook solution is only as trustworthy as the last person who ran it, and the repository does not document a review process for correctness. The third is that the material is broad by design, which means depth varies by folder. The repository does not publish per-folder coverage statistics, so you cannot tell from the README alone how complete the Computer Networks notes are relative to the DBMS notes. Finally, the README does not document rollback, versioning or a changelog, and there are no releases; if a folder is reorganised, you have no stable tag to fall back to. ## How It Compares with LeetCode and NeetCode LeetCode is an execution and judging platform: you submit code, it runs against hidden test cases, and it reports pass or fail with runtime and memory figures. NeetCode is a curated problem list with video walkthroughs, structured as a path. This repository differs on both axes. It does not execute your code, and it does not gate content behind a subscription or an account. Its value is that it aggregates several named sheets, the 450 DSA by Love Babbar, the Striver DSA sheet and the Apna College DSA Sheet, into one MIT-licensed tree that you can clone, fork and read offline. The practical consequence is that you would typically use this repository for the reading and the reference notes, and a judge platform for the practice loop. Using it as a substitute for a judge means you write solutions with no external confirmation that they are correct. ## Licence, Maintenance and Upgrade Cost The repository is MIT licensed, and a LICENSE file sits at the top level. MIT permits reuse, modification and redistribution with the copyright notice and permission notice retained. That matters here because the repository bundles third-party study material, including named problem sheets from other creators, and it also links to a folder called 5]. Important Books and Resources/. The licence covers the repository's own contents; it does not automatically settle the status of every bundled PDF or every external list, and that is a question for whoever owns those works, not something the LICENSE file answers. On maintenance, the last push was on 2026-09-10, which is recent. There are no tagged releases, so the upgrade path is a fresh clone or a pull against master. Because the content is notebooks and markdown, an upgrade is cheap to take and cheap to ignore: there is no dependency graph to resolve and no migration script. The cost is that a pull can move files you have bookmarked, and without tags you cannot pin a known-good state except by recording a commit hash yourself. ## Conclusion Adopt this repository if you are a self-directed candidate who wants one MIT-licensed place to hold the 450 DSA by Love Babbar, the Striver DSA sheet, the Apna College DSA Sheet, technical-subject notes and notebook solutions, and you are willing to pick your own path through it. Do not adopt it if you need a scheduled curriculum with progress tracking, graded feedback or maintained per-problem explanations; the README points to the author's own site rather than promising any of that, and the repository is a collection, not a course. Before you invest weeks, verify three things yourself: that the specific sheet you intend to follow is present and complete in the branch you cloned, that the solutions open and run in your local tooling, and that the folders for the technical subjects you are weakest in contain more than a placeholder. The last push was on 2026-09-10, so the tree is recent; the absence of tagged releases means you should pin a commit hash rather than assume a stable snapshot. ## FAQ ### How do I prepare for FAANG interviews using The-Complete-FAANG-Preparation? The repository gathers several DSA sheets, technical-subject notes and behavioural material under one tree, so the practical approach is to clone it, pick one DSA sheet rather than working through all of them, and pair it with a platform that executes your code, since this repository does not run or grade solutions. ### Is Blind 75 enough for FAANG, or do I need The-Complete-FAANG-Preparation? The repository does not discuss Blind 75, so it offers no answer on whether that list alone is sufficient. What it does provide is several named sheets, including 450 DSA by Love Babbar, the Striver DSA sheet and the Apna College DSA Sheet, alongside technical-subject notes. ### Can I prepare for FAANG in 3 months with The-Complete-FAANG-Preparation? The repository does not publish a schedule, a timeline or progress tracking, so it cannot tell you whether three months is enough. It does let you choose a single sheet and a single technical subject, which is the only pacing decision the repository structure supports. ## Sources - [AkashSingh3031/The-Complete-FAANG-Preparation on GitHub](https://github.com/AkashSingh3031/The-Complete-FAANG-Preparation) - [Issues](https://github.com/AkashSingh3031/The-Complete-FAANG-Preparation/issues) - [License: MIT](https://github.com/AkashSingh3031/The-Complete-FAANG-Preparation/blob/master/LICENSE) - [Project website](https://www.sparklmind.com) - [README](https://github.com/AkashSingh3031/The-Complete-FAANG-Preparation/blob/master/README.md) --- Hysen Labs editorial analysis, written from the project's own repository and release notes. Cite the canonical page: https://hysenlabs.com/projects/akashsingh3031-the-complete-faang-preparation