Everyone prepping for FAANG interviews knows the pain of juggling multiple scattered resources — YouTube sheets, coding challenges in different languages, and system design guides all over the place. The Complete FAANG Preparation repo tackles this by consolidating popular DSA question sheets and technical topics into a single, structured, and version-controlled Jupyter Notebook knowledge base.
What The Complete FAANG Preparation offers and how it’s built
This repository is essentially a curated meta-index of FAANG interview preparation materials. It aggregates some of the most popular Data Structures & Algorithms (DSA) sheets — including Love Babbar’s 450 questions, Striver’s series, and Apna College’s sheet — and provides solutions across four major programming languages: C++, Python, Java, and JavaScript.
Beyond just algorithm problems, the repo extends into core computer science subjects like Operating Systems, Database Management Systems, SQL, Computer Networks, and Object-Oriented Programming. It also includes system design topics covering both low-level design (LLD) and high-level design (HLD).
Competitive programming resources from platforms such as LeetCode, Code Jam, and Facebook Hackercup are included, alongside sections for aptitude questions and puzzles. This broad scope positions the repo as a one-stop reference for technical interview preparation.
Under the hood, the repo is organized as a collection of Jupyter Notebooks. This choice of stack — notebooks rather than a standalone app or website — reflects its purpose as a navigable knowledge base rather than an executable tool. The notebooks act as both documentation and runnable code containers, allowing users to read explanations and run solutions in their preferred language environment.
The repo also fosters community contributions, featuring contributor guidelines, a list of placed students as social proof, and stargazer charts showing its popularity (nearly 12k stars).
Why this repo stands out for interview preparation
What distinguishes this repo is its role as a curated aggregator rather than original content creator. Instead of reinventing questions or building a complex platform, it focuses on collecting, organizing, and standardizing widely recognized FAANG prep materials in one place.
One of the key tradeoffs here is that the repo is not interactive or dynamically adaptive like some coding platforms. It lacks built-in quizzes, timed tests, or progress tracking. However, the tradeoff is clear: it offers a low-friction, version-controlled, and multi-language solution set that bridges the gap between video-based learning (e.g., YouTube sheets) and hands-on coding practice.
The use of Jupyter Notebooks allows users to explore problems and solutions in an executable format, which is especially helpful for learners who want to run code snippets directly or modify them. The multi-language support is also a big plus, catering to programmers who prefer different languages — this flexibility is often a pain point in other repositories that lock you into one language.
The repo’s code quality is less about application code and more about clear organization and documentation. The directory structure and notebooks are logically segmented by topic and resource, making navigation straightforward. Because it’s a knowledge base, the emphasis is on readability, completeness, and correctness of solutions rather than performance or scalability.
Explore the project
Since there are no installation or runtime commands provided, the best way to get started is to explore the repository directly on GitHub or clone it locally to use with Jupyter Notebook or JupyterLab.
Start by opening the main README, which serves as the index and points to the various sheets and subject areas. From there, you can delve into the folders containing the DSA sheets, each with subfolders for C++, Python, Java, and JavaScript solutions.
The system design notebooks are also clearly marked and provide conceptual frameworks and examples for both low-level and high-level design questions.
If you have Jupyter installed, running the notebooks locally gives you an interactive experience to tweak code, add notes, or test alternative solutions.
Verdict
The Complete FAANG Preparation is a solid resource for anyone preparing for technical interviews at FAANG or similar companies who values a comprehensive, multi-language, and well-organized set of study materials.
Its biggest strength lies in consolidating scattered popular interview prep sheets into a single, version-controlled, and executable knowledge base. The multi-language approach broadens its appeal, and the inclusion of system design and CS fundamentals rounds out the coverage.
However, it is not an interactive platform or a learning management system. It assumes a self-driven learner who is comfortable navigating Jupyter Notebooks and running code locally. If you want quizzes, progress tracking, or an online coding playground, you’ll need to supplement this repo with other tools.
For candidates serious about structured study and who appreciate having multi-language solutions in one place, this repo is worth bookmarking and integrating into your prep workflow.
Related Articles
- Machine-Learning-Interviews: a structured guide for FAANG ML interview prep with agentic AI focus — A curated Jupyter notebook guide for machine learning interview prep at FAANG companies, covering coding, system design,
- DevOps interview prep with hands-on questions and video solutions — A curated collection of 115 practical DevOps interview questions with step-by-step video solutions across AWS, Linux, Do
- Python Data Science Handbook: Exploring the Core Python Data Science Stack Through Executable Notebooks — Explore the Python Data Science Handbook repo offering runnable Jupyter notebooks covering NumPy, Pandas, Matplotlib, an
- go-interview-practice: a Go coding challenge platform with automated scoring and AI interview simulation — Explore go-interview-practice, a Go coding challenge platform with automated testing, performance analytics, and AI-powe
- Hands-On Large Language Models: A practical, visual journey through LLM engineering — Explore the Hands-On Large Language Models repo, a Jupyter notebook-based practical guide from fundamentals to fine-tuni
→ GitHub Repo: AkashSingh3031/The-Complete-FAANG-Preparation ⭐ 11,943 · Jupyter Notebook