Not everyone has the time to do a few hundred LeetCode questions. Here are free and curated technical interview preparation materials for busy engineers, brought to you by me, the author of the Blind 75 list. Over 500,000 people have benefitted from this handbook!
Besides the usual algorithm questions, other awesome stuff includes:
- Best practice questions for coding interviews
- How to prepare for coding interviews
- Coding interview cheatsheet - Straight-to-the-point Do's and Don'ts
- Algorithm tips categorized by topic
- Interview formats of the top tech companies
- Step-by-step resume guide to prepare a FAANG-ready resume
- Behavioral questions asked by the top tech companies
- Good questions to ask your interviewers at the end of the interviews
- Front end interview preparation
Help from you in contributing content would be very much appreciated!
This repository has practical content that covers all phases of a technical interview, from applying for a job to passing the interviews to offer negotiation. Technically competent candidates might still find the non-technical content helpful.
The information in this repository is condensed. Ultimately, the key to succeeding in technical interviews is consistent practice and I don't want to bore you with too many words. I tell you the minimum you need to know on how to go about navigating the interview process, you go and practice and land your dream job.
Anybody who wants to land a job at a tech company but is new to technical interviews, seasoned engineers who have not been on the other side of the interviewing table in a while and want to get back into the game, or anyone who wants to be better at technical interviewing.
💡 Looking for high-quality interview courses? Educative offers a ton of great courses to improve your interview game 💡
There are many awesome books like Cracking the Coding Interview and interview-related repositories out there on GitHub, what makes this repository different? The difference is that many existing interview repositories contain mainly links to external resources whereas this repository contains top-quality curated content directly for your consumption.
Also, existing resources focus mainly on algorithm questions and lack coverage for more domain-specific and non-technical questions. This handbook aims to cover content beyond the typical algorithmic coding questions. 😎
AlgoMonster aims to help you ace the technical interview in the shortest time possible. By Google engineers, AlgoMonster uses a data-driven approach to teach you the most useful key question patterns and has contents to help you quickly revise basic data structures and algorithms. Best of all, AlgoMonster is not subscription-based - pay a one-time fee and get lifetime access. Join today for a 70% discount →
This course by Educative expands upon the questions on the recommended practice questions but approaches the practicing from a questions pattern perspective, which is an approach I also agree with for learning and have personally used to get better at coding interviews. The course allows you to practice selected questions in Java, Python, C++, JavaScript and also provides sample solutions in those languages. Learn and understand patterns, not memorize answers! Join today for a 10% discount →
Front-end-related content has been moved to a separate website - Front End Interview Handbook.
We're still working on System Design content. In the meanwhile, check out Educative's Grokking the System Design Interview course, which in our opinion is one of the most useful resources for getting started on system design interviews preparation.
A Docusaurus website has been created to provide a better reading experience. Check out the website here!
💡 Stop the grind and study with a plan! Developed by Google engineers, AlgoMonster is the fastest way to get a software engineering job. Join today for a 70% discount! 💡
If you are interested in how data structures are implemented, check out Lago, a Data Structures and Algorithms library for JavaScript. It is pretty much still WIP but I intend to make it into a library that can be used in production and also a reference resource for revising Data Structures and Algorithms.
There are no formal contributing guidelines at the moment as things are still in flux and we might find a better approach to structure content as we go along. You are welcome to contribute whatever you think will be helpful to fellow engineers. If you would like to contribute content for different domains, feel free to create an issue or submit a pull request and we can discuss further.
This project exists thanks to all the people who contributed. [Contribute].
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I am providing code in the repository to you under an open source license. Because this is my personal repository, the license you receive to my code is from me and not my employer (Meta).