[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"project-9":3},{"id":4,"name":5,"fullName":6,"owner":7,"repo":5,"description":8,"homepage":9,"htmlUrl":10,"language":9,"languages":9,"totalLinesOfCode":9,"stars":11,"forks":12,"watchers":13,"openIssues":14,"contributorsCount":9,"subscribersCount":15,"size":15,"stars1d":16,"stars7d":17,"stars30d":18,"stars90d":15,"forks30d":15,"starsTrendScore":19,"compositeScore":20,"rankGlobal":9,"rankLanguage":9,"license":9,"archived":21,"fork":21,"defaultBranch":22,"hasWiki":21,"hasPages":21,"topics":23,"createdAt":9,"pushedAt":9,"updatedAt":36,"readmeContent":37,"aiSummary":38,"trendingCount":15,"starSnapshotCount":15,"syncStatus":39,"lastSyncTime":40,"discoverSource":41},9,"coding-interview-university","jwasham\u002Fcoding-interview-university","jwasham","A complete computer science study plan to become a software engineer.",null,"https:\u002F\u002Fgithub.com\u002Fjwasham\u002Fcoding-interview-university",352669,83622,8558,80,0,489,1677,5720,2296,107,false,"main",[24,25,26,27,28,29,30,31,32,33,34,35],"computer-science","interview","programming-interviews","study-plan","data-structures","algorithms","software-engineering","algorithm","coding-interviews","interview-prep","coding-interview","interview-preparation","2026-06-17 04:00:01","# Coding Interview University\n\n> I originally created this as a short to-do list of study topics for becoming a software engineer,\n> but it grew to the large list you see today. After going through this study plan, [I got hired\n> as a Software Development Engineer at Amazon](https:\u002F\u002Fstartupnextdoor.com\u002Five-been-acquired-by-amazon\u002F?src=ciu)!\n> You probably won't have to study as much as I did. Anyway, everything you need is here.\n>\n> I studied about 8-12 hours a day, for several months. This is my story: [Why I studied full-time for 8 months for a Google interview](https:\u002F\u002Fmedium.freecodecamp.org\u002Fwhy-i-studied-full-time-for-8-months-for-a-google-interview-cc662ce9bb13)\n>\n> **Please Note:** You won't need to study as much as I did. I wasted a lot of time on things I didn't need to know. More info about that is below. I'll help you get there without wasting your precious time.\n>\n> The items listed here will prepare you well for a technical interview at just about any software company,\n> including the giants: Amazon, Facebook, Google, and Microsoft.\n>\n> *Best of luck to you!*\n\n\u003Cdetails>\n\u003Csummary>Translations:\u003C\u002Fsummary>\n\n- [Bahasa Indonesia](translations\u002FREADME-id.md)\n- [Bulgarian](translations\u002FREADME-bg.md)\n- [Español](translations\u002FREADME-es.md)\n- [German](translations\u002FREADME-de.md)\n- [Japanese (日本語)](translations\u002FREADME-ja.md)\n- [Marathi](translations\u002FREADME-mr.md)\n- [Polish](translations\u002FREADME-pl.md)\n- [Português Brasileiro](translations\u002FREADME-ptbr.md)\n- [Russian](translations\u002FREADME-ru.md)\n- [Tiếng Việt - Vietnamese](translations\u002FREADME-vi.md)\n- [Urdu - اردو](translations\u002FREADME-ur.md)\n- [Uzbek](translations\u002FREADME-uz.md)\n- [বাংলা - Bangla](translations\u002FREADME-bn.md)\n- [ខ្មែរ - Khmer](translations\u002FREADME-kh.md)\n- [简体中文](translations\u002FREADME-cn.md)\n- [繁體中文](translations\u002FREADME-tw.md)\n\u003C\u002Fdetails>\n\n\u003Cdetails>\n\u003Csummary>Translations in progress:\u003C\u002Fsummary>\n\n- [Afrikaans](https:\u002F\u002Fgithub.com\u002Fjwasham\u002Fcoding-interview-university\u002Fissues\u002F1164)\n- [Arabic](https:\u002F\u002Fgithub.com\u002Fjwasham\u002Fcoding-interview-university\u002Fissues\u002F98)\n- [French](https:\u002F\u002Fgithub.com\u002Fjwasham\u002Fcoding-interview-university\u002Fissues\u002F89)\n- [Greek](https:\u002F\u002Fgithub.com\u002Fjwasham\u002Fcoding-interview-university\u002Fissues\u002F166)\n- [Italian](https:\u002F\u002Fgithub.com\u002Fjwasham\u002Fcoding-interview-university\u002Fissues\u002F1030)\n- [Korean(한국어)](https:\u002F\u002Fgithub.com\u002Fjwasham\u002Fcoding-interview-university\u002Fissues\u002F118)\n- [Malayalam](https:\u002F\u002Fgithub.com\u002Fjwasham\u002Fcoding-interview-university\u002Fissues\u002F239)\n- [Persian - Farsi](https:\u002F\u002Fgithub.com\u002Fjwasham\u002Fcoding-interview-university\u002Fissues\u002F186)\n- [Telugu](https:\u002F\u002Fgithub.com\u002Fjwasham\u002Fcoding-interview-university\u002Fissues\u002F117)\n- [Thai](https:\u002F\u002Fgithub.com\u002Fjwasham\u002Fcoding-interview-university\u002Fissues\u002F156)\n- [Turkish](https:\u002F\u002Fgithub.com\u002Fjwasham\u002Fcoding-interview-university\u002Fissues\u002F90)\n- [Українська](https:\u002F\u002Fgithub.com\u002Fjwasham\u002Fcoding-interview-university\u002Fissues\u002F106)\n- [עברית](https:\u002F\u002Fgithub.com\u002Fjwasham\u002Fcoding-interview-university\u002Fissues\u002F82)\n- [हिन्दी](https:\u002F\u002Fgithub.com\u002Fjwasham\u002Fcoding-interview-university\u002Fissues\u002F81)\n\u003C\u002Fdetails>\n\n\n## What is it?\n\n![Coding at the whiteboard - from HBO's Silicon Valley](https:\u002F\u002Fd3j2pkmjtin6ou.cloudfront.net\u002Fcoding-at-the-whiteboard-silicon-valley.png)\n\nThis is my multi-month study plan for becoming a software engineer for a large company.\n\n**Required:**\n* A little experience with coding (variables, loops, methods\u002Ffunctions, etc)\n* Patience\n* Time\n\nNote this is a study plan for **software engineering**, not frontend engineering or full-stack development. There are really\nsuper roadmaps and coursework for those career paths elsewhere (see https:\u002F\u002Froadmap.sh\u002F for more info).\n\nThere is a lot to learn in a university Computer Science program, but only knowing about 75% is good enough for an interview, so that's what I cover here.\nFor a complete CS self-taught program, the resources for my study plan have been included in Kamran Ahmed's Computer Science Roadmap: https:\u002F\u002Froadmap.sh\u002Fcomputer-science\n\n---\n\n## Table of Contents\n\n### The Study Plan\n\n- [What is it?](#what-is-it)\n- [Why use it?](#why-use-it)\n- [How to use it](#how-to-use-it)\n- [Don't feel you aren't smart enough](#dont-feel-you-arent-smart-enough)\n- [A Note About Video Resources](#a-note-about-video-resources)\n- [Choose a Programming Language](#choose-a-programming-language)\n- [Books for Data Structures and Algorithms](#books-for-data-structures-and-algorithms)\n- [Interview Prep Books](#interview-prep-books)\n- [Don't Make My Mistakes](#dont-make-my-mistakes)\n- [What you Won't See Covered](#what-you-wont-see-covered)\n- [The Daily Plan](#the-daily-plan)\n- [Coding Question Practice](#coding-question-practice)\n- [Coding Problems](#coding-problems)\n\n### Topics of Study\n\n- [Algorithmic complexity \u002F Big-O \u002F Asymptotic analysis](#algorithmic-complexity--big-o--asymptotic-analysis)\n- [Data Structures](#data-structures)\n    - [Arrays](#arrays)\n    - [Linked Lists](#linked-lists)\n    - [Stack](#stack)\n    - [Queue](#queue)\n    - [Hash table](#hash-table)\n- [More Knowledge](#more-knowledge)\n    - [Binary search](#binary-search)\n    - [Bitwise operations](#bitwise-operations)\n- [Trees](#trees)\n    - [Trees - Intro](#trees---intro)\n    - [Binary search trees: BSTs](#binary-search-trees-bsts)\n    - [Heap \u002F Priority Queue \u002F Binary Heap](#heap--priority-queue--binary-heap)\n    - balanced search trees (general concept, not details)\n    - traversals: preorder, inorder, postorder, BFS, DFS\n- [Sorting](#sorting)\n    - selection\n    - insertion\n    - heapsort\n    - quicksort\n    - mergesort\n- [Graphs](#graphs)\n    - directed\n    - undirected\n    - adjacency matrix\n    - adjacency list\n    - traversals: BFS, DFS\n- [Even More Knowledge](#even-more-knowledge)\n    - [Recursion](#recursion)\n    - [Dynamic Programming](#dynamic-programming)\n    - [Design Patterns](#design-patterns)\n    - [Combinatorics (n choose k) & Probability](#combinatorics-n-choose-k--probability)\n    - [NP, NP-Complete and Approximation Algorithms](#np-np-complete-and-approximation-algorithms)\n    - [How computers process a program](#how-computers-process-a-program)\n    - [Caches](#caches)\n    - [Processes and Threads](#processes-and-threads)\n    - [Testing](#testing)\n    - [String searching & manipulations](#string-searching--manipulations)\n    - [Tries](#tries)\n    - [Floating Point Numbers](#floating-point-numbers)\n    - [Unicode](#unicode)\n    - [Endianness](#endianness)\n    - [Networking](#networking)\n- [Final Review](#final-review)\n\n### Getting the Job\n\n- [Update Your Resume](#update-your-resume)\n- [Find a Job](#find-a-job)\n- [Interview Process & General Interview Prep](#interview-process--general-interview-prep)\n- [Be thinking of for when the interview comes](#be-thinking-of-for-when-the-interview-comes)\n- [Have questions for the interviewer](#have-questions-for-the-interviewer)\n- [Once You've Got The Job](#once-youve-got-the-job)\n\n**---------------- Everything below this point is optional ----------------**\n\n### Optional Extra Topics & Resources\n\n- [Additional Books](#additional-books)\n- [System Design, Scalability, Data Handling](#system-design-scalability-data-handling) (if you have 4+ years experience)\n- [Additional Learning](#additional-learning)\n    - [Compilers](#compilers)\n    - [Emacs and vi(m)](#emacs-and-vim)\n    - [Unix command line tools](#unix-command-line-tools)\n    - [Information theory](#information-theory-videos)\n    - [Parity & Hamming Code](#parity--hamming-code-videos)\n    - [Entropy](#entropy)\n    - [Cryptography](#cryptography)\n    - [Compression](#compression)\n    - [Computer Security](#computer-security)\n    - [Garbage collection](#garbage-collection)\n    - [Parallel Programming](#parallel-programming)\n    - [Messaging, Serialization, and Queueing Systems](#messaging-serialization-and-queueing-systems)\n    - [A*](#a)\n    - [Fast Fourier Transform](#fast-fourier-transform)\n    - [Bloom Filter](#bloom-filter)\n    - [HyperLogLog](#hyperloglog)\n    - [Locality-Sensitive Hashing](#locality-sensitive-hashing)\n    - [van Emde Boas Trees](#van-emde-boas-trees)\n    - [Augmented Data Structures](#augmented-data-structures)\n    - [Balanced search trees](#balanced-search-trees)\n        - AVL trees\n        - Splay trees\n        - Red\u002Fblack trees\n        - 2-3 search trees\n        - 2-3-4 Trees (aka 2-4 trees)\n        - N-ary (K-ary, M-ary) trees\n        - B-Trees\n    - [k-D Trees](#k-d-trees)\n    - [Skip lists](#skip-lists)\n    - [Network Flows](#network-flows)\n    - [Disjoint Sets & Union Find](#disjoint-sets--union-find)\n    - [Math for Fast Processing](#math-for-fast-processing)\n    - [Treap](#treap)\n    - [Linear Programming](#linear-programming-videos)\n    - [Geometry, Convex hull](#geometry-convex-hull-videos)\n    - [Discrete math](#discrete-math)\n- [Additional Detail on Some Subjects](#additional-detail-on-some-subjects)\n- [Video Series](#video-series)\n- [Computer Science Courses](#computer-science-courses)\n- [Papers](#papers)\n\n---\n\n## Why use it?\n\nIf you want to work as a software engineer for a large company, these are the things you have to know.\n\nIf you missed out on getting a degree in computer science, like I did, this will catch you up and save four years of your life.\n\nWhen I started this project, I didn't know a stack from a heap, didn't know Big-O anything, or anything about trees, or how to\ntraverse a graph. If I had to code a sorting algorithm, I can tell ya it would have been terrible.\nEvery data structure I had ever used was built into the language, and I didn't know how they worked\nunder the hood at all. I never had to manage memory unless a process I was running would give an \"out of\nmemory\" error, and then I'd have to find a workaround. I used a few multidimensional arrays in my life and\nthousands of associative arrays, but I never created data structures from scratch.\n\nIt's a long plan. It may take you months. If you are familiar with a lot of this already it will take you a lot less time.\n\n**[⬆ back to top](#table-of-contents)**\n\n## How to use it\n\nEverything below is an outline, and you should tackle the items in order from top to bottom.\n\nI'm using GitHub's special markdown flavor, including tasks lists to track progress.\n  - [More about GitHub-flavored markdown](https:\u002F\u002Fguides.github.com\u002Ffeatures\u002Fmastering-markdown\u002F#GitHub-flavored-markdown)\n\n### If you don't want to use git\n\nOn this page, click the Code button near the top, then click \"Download ZIP\". Unzip the file and you can work with the text files.\n\nIf you're open in a code editor that understands markdown, you'll see everything formatted nicely.\n\n![How to download the repo as a zip file](https:\u002F\u002Fd3j2pkmjtin6ou.cloudfront.net\u002Fhow-to-download-as-zip.png)\n\n### If you're comfortable with git\n\nCreate a new branch so you can check items like this, just put an x in the brackets: [x]\n\n1. ***Fork the GitHub repo:*** `https:\u002F\u002Fgithub.com\u002Fjwasham\u002Fcoding-interview-university` by clicking on the Fork button.\n\n    ![Fork the GitHub repo](https:\u002F\u002Fd3j2pkmjtin6ou.cloudfront.net\u002Ffork-button.png)\n\n1. Clone to your local repo:\n\n    ```bash\n    git clone https:\u002F\u002Fgithub.com\u002F\u003CYOUR_GITHUB_USERNAME>\u002Fcoding-interview-university.git\n    cd coding-interview-university\n    git remote add upstream https:\u002F\u002Fgithub.com\u002Fjwasham\u002Fcoding-interview-university.git\n    git remote set-url --push upstream DISABLE  # so that you don't push your personal progress back to the original repo\n    ```\n\n1. Mark all boxes with X after you completed your changes:\n\n    ```bash\n    git commit -am \"Marked personal progress\"\n    git pull upstream main  # keep your fork up-to-date with changes from the original repo\n\n    git push # just pushes to your fork\n    ```\n\n**[⬆ back to top](#table-of-contents)**\n\n## Don't feel you aren't smart enough\n\n- Successful software engineers are smart, but many have an insecurity that they aren't smart enough.\n- The following videos may help you overcome this insecurity:\n    - [The myth of the Genius Programmer](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=0SARbwvhupQ)\n    - [It's Dangerous to Go Alone: Battling the Invisible Monsters in Tech](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=1i8ylq4j_EY)\n\n**[⬆ back to top](#table-of-contents)**\n\n## A Note About Video Resources\n\nSome videos are available only by enrolling in a Coursera or EdX class. These are called MOOCs.\nSometimes the classes are not in session so you have to wait a couple of months, so you have no access.\n\nIt would be great to replace the online course resources with free and always-available public sources,\nsuch as YouTube videos (preferably university lectures), so that you people can study these anytime,\nnot just when a specific online course is in session.\n\n**[⬆ back to top](#table-of-contents)**\n\n## Choose a Programming Language\n\nYou'll need to choose a programming language for the coding interviews you do,\nbut you'll also need to find a language that you can use to study computer science concepts.\n\nPreferably the language would be the same, so that you only need to be proficient in one.\n\n### For this Study Plan\n\nWhen I did the study plan, I used 2 languages for most of it: C and Python\n\n* C: Very low level. Allows you to deal with pointers and memory allocation\u002Fdeallocation, so you feel the data structures\n    and algorithms in your bones. In higher-level languages like Python or Java, these are hidden from you. In day-to-day work, that's terrific,\n    but when you're learning how these low-level data structures are built, it's great to feel close to the metal.\n    - C is everywhere. You'll see examples in books, lectures, videos, *everywhere* while you're studying.\n    - [The C Programming Language, 2nd Edition](https:\u002F\u002Fwww.amazon.com\u002FProgramming-Language-Brian-W-Kernighan\u002Fdp\u002F0131103628)\n        - This is a short book, but it will give you a great handle on the C language and if you practice it a little\n            you'll quickly get proficient. Understanding C helps you understand how programs and memory work.\n        - You don't need to go super deep in the book (or even finish it). Just get to where you're comfortable reading and writing in C.\n* Python: Modern and very expressive, I learned it because it's just super useful and also allows me to write less code in an interview.\n\nThis is my preference. You do what you like, of course.\n\nYou may not need it, but here are some sites for learning a new language:\n- [Exercism](https:\u002F\u002Fexercism.org\u002Ftracks)\n- [Codewars](http:\u002F\u002Fwww.codewars.com)\n- [HackerEarth](https:\u002F\u002Fwww.hackerearth.com\u002Ffor-developers\u002F)\n- [Scaler Topics (Java, C++)](https:\u002F\u002Fwww.scaler.com\u002Ftopics\u002F)\n- [Programiz PRO Community Challenges)](https:\u002F\u002Fprogramiz.pro\u002F)\n\n### For your Coding Interview\n\nYou can use a language you are comfortable in to do the coding part of the interview, but for large companies, these are solid choices:\n\n- C++\n- Java\n- Python\n\nYou could also use these, but read around first. There may be caveats:\n\n- JavaScript\n- Ruby\n\nHere is an article I wrote about choosing a language for the interview:\n[Pick One Language for the Coding Interview](https:\u002F\u002Fstartupnextdoor.com\u002Fimportant-pick-one-language-for-the-coding-interview\u002F).\nThis is the original article my post was based on: [Choosing a Programming Language for Interviews](https:\u002F\u002Fweb.archive.org\u002Fweb\u002F20210516054124\u002Fhttp:\u002F\u002Fblog.codingforinterviews.com\u002Fbest-programming-language-jobs\u002F)\n\nYou need to be very comfortable in the language and be knowledgeable.\n\nRead more about choices:\n- [Choose the Right Language for Your Coding Interview](http:\u002F\u002Fwww.byte-by-byte.com\u002Fchoose-the-right-language-for-your-coding-interview\u002F)\n\n[See language-specific resources here](programming-language-resources.md)\n\n**[⬆ back to top](#table-of-contents)**\n\n## Books for Data Structures and Algorithms\n\nThis book will form your foundation for computer science.\n\nJust choose one, in a language that you will be comfortable with. You'll be doing a lot of reading and coding.\n\n### Python\n\n- [Coding Interview Patterns: Nail Your Next Coding Interview](https:\u002F\u002Fgeni.us\u002Fq7svoz) (**Main Recommendation**)\n    - An insider’s perspective on what interviewers are truly looking for and why.\n    - 101 real coding interview problems with detailed solutions.\n    - Intuitive explanations that guide you through each problem as if you were solving it in a live interview.\n    - 1000+ diagrams to illustrate key concepts and patterns.\t\n\n### C\n\n- [Algorithms in C, Parts 1-5 (Bundle), 3rd Edition](https:\u002F\u002Fwww.amazon.com\u002FAlgorithms-Parts-1-5-Bundle-Fundamentals\u002Fdp\u002F0201756080)\n    - Fundamentals, Data Structures, Sorting, Searching, and Graph Algorithms\n\n### Java\n\nYour choice:\n\n- Goodrich, Tamassia, Goldwasser\n    - [Data Structures and Algorithms in Java](https:\u002F\u002Fwww.amazon.com\u002FData-Structures-Algorithms-Michael-Goodrich\u002Fdp\u002F1118771338\u002F)\n- Sedgewick and Wayne:\n    - [Algorithms](https:\u002F\u002Fwww.amazon.com\u002FAlgorithms-4th-Robert-Sedgewick\u002Fdp\u002F032157351X\u002F)\n    - Free Coursera course that covers the book (taught by the authors!):\n        - [Algorithms I](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Falgorithms-part1)\n        - [Algorithms II](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Falgorithms-part2)\n\n### C++\n\nYour choice:\n\n- Goodrich, Tamassia, and Mount\n    - [Data Structures and Algorithms in C++, 2nd Edition](https:\u002F\u002Fwww.amazon.com\u002FData-Structures-Algorithms-Michael-Goodrich\u002Fdp\u002F0470383275)\n- Sedgewick and Wayne\n    - [Algorithms in C++, Parts 1-4: Fundamentals, Data Structure, Sorting, Searching](https:\u002F\u002Fwww.amazon.com\u002FAlgorithms-Parts-1-4-Fundamentals-Structure\u002Fdp\u002F0201350882\u002F)\n    - [Algorithms in C++ Part 5: Graph Algorithms](https:\u002F\u002Fwww.amazon.com\u002FAlgorithms-Part-Graph-3rd-Pt-5\u002Fdp\u002F0201361183\u002F)\n\n**[⬆ back to top](#table-of-contents)**\n\n## Interview Prep Books\n\nHere are some recommended books to supplement your learning.\n\n- [Coding Interview Patterns: Nail Your Next Coding Interview](https:\u002F\u002Fgeni.us\u002Fq7svoz)\n\n- [Programming Interviews Exposed: Coding Your Way Through the Interview, 4th Edition](https:\u002F\u002Fwww.amazon.com\u002FProgramming-Interviews-Exposed-Through-Interview\u002Fdp\u002F111941847X\u002F)\n    - Answers in C++ and Java\n    - This is a good warm-up for Cracking the Coding Interview\n    - Not too difficult. Most problems may be easier than what you'll see in an interview (from what I've read)\n\n- [Cracking the Coding Interview, 6th Edition](http:\u002F\u002Fwww.amazon.com\u002FCracking-Coding-Interview-6th-Programming\u002Fdp\u002F0984782850\u002F)\n    - answers in Java\n\n### If you have tons of extra time:\n\nChoose one:\n\n- [Elements of Programming Interviews (C++ version)](https:\u002F\u002Fwww.amazon.com\u002FElements-Programming-Interviews-Insiders-Guide\u002Fdp\u002F1479274836)\n- [Elements of Programming Interviews in Python](https:\u002F\u002Fwww.amazon.com\u002FElements-Programming-Interviews-Python-Insiders\u002Fdp\u002F1537713949\u002F)\n- [Elements of Programming Interviews (Java version)](https:\u002F\u002Fwww.amazon.com\u002FElements-Programming-Interviews-Java-Insiders\u002Fdp\u002F1517435803\u002F)\n        - [Companion Project - Method Stub and Test Cases for Every Problem in the Book](https:\u002F\u002Fgithub.com\u002Fgardncl\u002Felements-of-programming-interviews)\n\n**[⬆ back to top](#table-of-contents)**\n\n## Don't Make My Mistakes\n\nThis list grew over many months, and yes, it got out of hand.\n\nHere are some mistakes I made so you'll have a better experience. And you'll save months of time.\n\n### 1. You Won't Remember it All\n\nI watched hours of videos and took copious notes, and months later there was much I didn't remember. I spent 3 days going\nthrough my notes and making flashcards, so I could review. I didn't need all of that knowledge.\n\nPlease, read so you won't make my mistakes:\n\n[Retaining Computer Science Knowledge](https:\u002F\u002Fstartupnextdoor.com\u002Fretaining-computer-science-knowledge\u002F).\n\n### 2. Use Flashcards\n\nTo solve the problem, I made a little flashcard site where I could add flashcards of 2 types: general and code.\nEach card has a different formatting. I made a mobile-first website, so I could review on my phone or tablet, wherever I am.\n\nMake your own for free:\n\n- [Flashcards site repo](https:\u002F\u002Fgithub.com\u002Fjwasham\u002Fcomputer-science-flash-cards)\n\n**I DON'T RECOMMEND using my flashcards.** There are too many and most of them are trivia that you don't need.\n\nBut if you don't want to listen to me, here you go:\n- [My flash cards database (1200 cards)](https:\u002F\u002Fgithub.com\u002Fjwasham\u002Fcomputer-science-flash-cards\u002Fblob\u002Fmain\u002Fcards-jwasham.db):\n- [My flash cards database (extreme - 1800 cards)](https:\u002F\u002Fgithub.com\u002Fjwasham\u002Fcomputer-science-flash-cards\u002Fblob\u002Fmain\u002Fcards-jwasham-extreme.db):\n\nKeep in mind I went overboard and have cards covering everything from assembly language and Python trivia to machine learning and statistics.\nIt's way too much for what's required.\n\n**Note on flashcards:** The first time you recognize you know the answer, don't mark it as known. You have to see the\nsame card and answer it several times correctly before you really know it. Repetition will put that knowledge deeper in\nyour brain.\n\nAn alternative to using my flashcard site is [Anki](http:\u002F\u002Fankisrs.net\u002F), which has been recommended to me numerous times.\nIt uses a repetition system to help you remember. It's user-friendly, available on all platforms, and has a cloud sync system.\nIt costs $25 on iOS but is free on other platforms.\n\nMy flashcard database in Anki format: https:\u002F\u002Fankiweb.net\u002Fshared\u002Finfo\u002F25173560 (thanks [@xiewenya](https:\u002F\u002Fgithub.com\u002Fxiewenya)).\n\nSome students have mentioned formatting issues with white space that can be fixed by doing the following: open the deck, edit the card, click cards, select the \"styling\" radio button, and add the member \"white-space: pre;\" to the card class.\n\n### 3. Do Coding Interview Questions While You're Learning\n\nTHIS IS VERY IMPORTANT.\n\nStart doing coding interview questions while you're learning data structures and algorithms.\n\nYou need to apply what you're learning to solve problems, or you'll forget. I made this mistake.\n\nOnce you've learned a topic, and feel somewhat comfortable with it, for example, **linked lists**:\n1. Open one of the [coding interview books](#interview-prep-books) (or coding problem websites, listed below)\n1. Do 2 or 3 questions regarding linked lists.\n1. Move on to the next learning topic.\n1. Later, go back and do another 2 or 3 linked list problems.\n1. Do this with each new topic you learn.\n\n**Keep doing problems while you're learning all this stuff, not after.**\n\nYou're not being hired for knowledge, but how you apply the knowledge.\n\nThere are many resources for this, listed below. Keep going.\n\n### 4. Focus\n\nThere are a lot of distractions that can take up valuable time. Focus and concentration are hard. Turn on some music\nwithout lyrics and you'll be able to focus pretty well.\n\n**[⬆ back to top](#table-of-contents)**\n\n## What you won't see covered\n\nThese are prevalent technologies but not part of this study plan:\n\n- Javascript\n- HTML, CSS, and other front-end technologies\n- SQL\n\n**[⬆ back to top](#table-of-contents)**\n\n## The Daily Plan\n\nThis course goes over a lot of subjects. Each will probably take you a few days, or maybe even a week or more. It depends on your schedule.\n\nEach day, take the next subject in the list, watch some videos about that subject, and then write an implementation\nof that data structure or algorithm in the language you chose for this course.\n\nYou can see my code here:\n - [C](https:\u002F\u002Fgithub.com\u002Fjwasham\u002Fpractice-c)\n - [C++](https:\u002F\u002Fgithub.com\u002Fjwasham\u002Fpractice-cpp)\n - [Python](https:\u002F\u002Fgithub.com\u002Fjwasham\u002Fpractice-python)\n\nYou don't need to memorize every algorithm. You just need to be able to understand it enough to be able to write your own implementation.\n\n**[⬆ back to top](#table-of-contents)**\n\n## Coding Question Practice\n\n    Why is this here? I'm not ready to interview.\n\n[Then go back and read this.](#3-do-coding-interview-questions-while-youre-learning)\n\nWhy you need to practice doing programming problems:\n- Problem recognition, and where the right data structures and algorithms fit in\n- Gathering requirements for the problem\n- Talking your way through the problem like you will in the interview\n- Coding on a whiteboard or paper, not a computer\n- Coming up with time and space complexity for your solutions (see Big-O below)\n- Testing your solutions\n\nThere is a great intro for methodical, communicative problem-solving in an interview. You'll get this from the programming\ninterview books, too, but I found this outstanding:\n[Algorithm design canvas](http:\u002F\u002Fwww.hiredintech.com\u002Falgorithm-design\u002F)\n\nWrite code on a whiteboard or paper, not a computer. Test with some sample inputs. Then type it and test it out on a computer.\n\nIf you don't have a whiteboard at home, pick up a large drawing pad from an art store. You can sit on the couch and practice.\nThis is my \"sofa whiteboard\". I added the pen in the photo just for scale. If you use a pen, you'll wish you could erase.\nGets messy quickly. **I use a pencil and eraser.**\n\n![my sofa whiteboard](https:\u002F\u002Fd3j2pkmjtin6ou.cloudfront.net\u002Fart_board_sm_2.jpg)\n\n**Coding question practice is not about memorizing answers to programming problems.**\n\n**[⬆ back to top](#table-of-contents)**\n\n## Coding Problems\n\nDon't forget your key coding interview books [here](#interview-prep-books).\n\nSolving Problems:\n- [How to Find a Solution](https:\u002F\u002Fwww.topcoder.com\u002Fthrive\u002Farticles\u002FHow%20To%20Find%20a%20Solution)\n- [How to Dissect a Topcoder Problem Statement](https:\u002F\u002Fwww.topcoder.com\u002Fthrive\u002Farticles\u002FHow%20To%20Dissect%20a%20Topcoder%20Problem%20Statement%20Content)\n\nCoding Interview Question Videos:\n- [IDeserve (88 videos)](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLamzFoFxwoNjPfxzaWqs7cZGsPYy0x_gI)\n- [Tushar Roy (5 playlists)](https:\u002F\u002Fwww.youtube.com\u002Fuser\u002Ftusharroy2525\u002Fplaylists?shelf_id=2&view=50&sort=dd)\n    - Super for walkthroughs of problem solutions\n- [Nick White - LeetCode Solutions (187 Videos)](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLU_sdQYzUj2keVENTP0a5rdykRSgg9Wp-)\n    - Good explanations of the solution and the code\n    - You can watch several in a short time\n- [FisherCoder - LeetCode Solutions](https:\u002F\u002Fyoutube.com\u002FFisherCoder)\n\nChallenge\u002FPractice sites:\n- [LeetCode](https:\u002F\u002Fleetcode.com\u002F)\n    - My favorite coding problem site. It's worth the subscription money for the 1-2 months you'll likely be preparing.\n    - See Nick White and FisherCoder Videos above for code walk-throughs.\n- [HackerRank](https:\u002F\u002Fwww.hackerrank.com\u002F)\n- [TopCoder](https:\u002F\u002Fwww.topcoder.com\u002F)\n- [Codeforces](https:\u002F\u002Fcodeforces.com\u002F)\n- [Codility](https:\u002F\u002Fcodility.com\u002Fprogrammers\u002F)\n- [Geeks for Geeks](https:\u002F\u002Fpractice.geeksforgeeks.org\u002Fexplore\u002F?page=1)\n- [AlgoExpert](https:\u002F\u002Fwww.algoexpert.io\u002Fproduct)\n    - Created by Google engineers, this is also an excellent resource to hone your skills.\n- [Project Euler](https:\u002F\u002Fprojecteuler.net\u002F)\n    - very math-focused, and not really suited for coding interviews\n\n**[⬆ back to top](#table-of-contents)**\n\n## Let's Get Started\n\nAlright, enough talk, let's learn!\n\nBut don't forget to do coding problems from above while you learn!\n\n## Algorithmic complexity \u002F Big-O \u002F Asymptotic analysis\n\n- Nothing to implement here, you're just watching videos and taking notes! Yay!\n- There are a lot of videos here. Just watch enough until you understand it. You can always come back and review.\n- Don't worry if you don't understand all the math behind it.\n- You just need to understand how to express the complexity of an algorithm in terms of Big-O.\n- [ ] [Harvard CS50 - Asymptotic Notation (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=iOq5kSKqeR4)\n- [ ] [Big O Notations (general quick tutorial) (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=V6mKVRU1evU)\n- [ ] [Big O Notation (and Omega and Theta) - best mathematical explanation (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ei-A_wy5Yxw&index=2&list=PL1BaGV1cIH4UhkL8a9bJGG356covJ76qN)\n- [ ] [Skiena (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=z1mkCe3kVUA)\n- [ ] [UC Berkeley Big O (video)](https:\u002F\u002Farchive.org\u002Fdetails\u002Fucberkeley_webcast_VIS4YDpuP98)\n- [ ] [Amortized Analysis (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=B3SpQZaAZP4&index=10&list=PL1BaGV1cIH4UhkL8a9bJGG356covJ76qN)\n- [ ] TopCoder (includes recurrence relations and master theorem):\n    - [Computational Complexity: Section 1](https:\u002F\u002Fwww.topcoder.com\u002Fthrive\u002Farticles\u002FComputational%20Complexity%20part%20one)\n    - [Computational Complexity: Section 2](https:\u002F\u002Fwww.topcoder.com\u002Fthrive\u002Farticles\u002FComputational%20Complexity%20part%20two)\n- [ ] [Cheat sheet](http:\u002F\u002Fbigocheatsheet.com\u002F)\n- [ ] [[Review] Analyzing Algorithms (playlist) in 18 minutes (video)](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL9xmBV_5YoZMxejjIyFHWa-4nKg6sdoIv)\n\nWell, that's about enough of that.\n\nWhen you go through \"Cracking the Coding Interview\", there is a chapter on this, and at the end there is a quiz to see\nif you can identify the runtime complexity of different algorithms. It's a super review and test.\n\n**[⬆ back to top](#table-of-contents)**\n\n## Data Structures\n\n- ### Arrays\n    - [ ] About Arrays:\n    \t- [Arrays CS50 Harvard University](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=tI_tIZFyKBw&t=3009s)\n        - [Arrays (video)](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Fdata-structures\u002Farrays-OsBSF)\n        - [UC Berkeley CS61B - Linear and Multi-Dim Arrays (video)](https:\u002F\u002Farchive.org\u002Fdetails\u002Fucberkeley_webcast_Wp8oiO_CZZE) (Start watching from 15m 32s)\n        - [Dynamic Arrays (video)](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Fdata-structures\u002Fdynamic-arrays-EwbnV)\n        - [Jagged Arrays (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=1jtrQqYpt7g)\n    - [ ] Implement a vector (mutable array with automatic resizing):\n        - [ ] Practice coding using arrays and pointers, and pointer math to jump to an index instead of using indexing.\n        - [ ] New raw data array with allocated memory\n            - can allocate int array under the hood, just not use its features\n            - start with 16, or if the starting number is greater, use power of 2 - 16, 32, 64, 128\n        - [ ] size() - number of items\n        - [ ] capacity() - number of items it can hold\n        - [ ] is_empty()\n        - [ ] at(index) - returns the item at a given index, blows up if index out of bounds\n        - [ ] push(item)\n        - [ ] insert(index, item) - inserts item at index, shifts that index's value and trailing elements to the right\n        - [ ] prepend(item) - can use insert above at index 0\n        - [ ] pop() - remove from end, return value\n        - [ ] delete(index) - delete item at index, shifting all trailing elements left\n        - [ ] remove(item) - looks for value and removes index holding it (even if in multiple places)\n        - [ ] find(item) - looks for value and returns first index with that value, -1 if not found\n        - [ ] resize(new_capacity) \u002F\u002F private function\n            - when you reach capacity, resize to double the size\n            - when popping an item, if the size is 1\u002F4 of capacity, resize to half\n    - [ ] Time\n        - O(1) to add\u002Fremove at end (amortized for allocations for more space), index, or update\n        - O(n) to insert\u002Fremove elsewhere\n    - [ ] Space\n        - contiguous in memory, so proximity helps performance\n        - space needed = (array capacity, which is >= n) * size of item, but even if 2n, still O(n)\n\n- ### Linked Lists\n    - [ ] Description:\n    \t- [ ] [Linked Lists CS50 Harvard University](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=2T-A_GFuoTo&t=650s) - this builds the intuition.\n        - [ ] [Singly Linked Lists (video)](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Fdata-structures\u002Fsingly-linked-lists-kHhgK)\n        - [ ] [CS 61B - Linked Lists 1 (video)](https:\u002F\u002Farchive.org\u002Fdetails\u002Fucberkeley_webcast_htzJdKoEmO0)\n        - [ ] [CS 61B - Linked Lists 2 (video)](https:\u002F\u002Farchive.org\u002Fdetails\u002Fucberkeley_webcast_-c4I3gFYe3w)\n        - [ ] [[Review] Linked lists in 4 minutes (video)](https:\u002F\u002Fyoutu.be\u002FF8AbOfQwl1c)\n    - [ ] [C Code (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=QN6FPiD0Gzo)\n            - not the whole video, just portions about Node struct and memory allocation\n    - [ ] Linked List vs Arrays:\n        - [Core Linked Lists Vs Arrays (video)](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Fdata-structures-optimizing-performance\u002Fcore-linked-lists-vs-arrays-rjBs9)\n        - [In The Real World Linked Lists Vs Arrays (video)](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Fdata-structures-optimizing-performance\u002Fin-the-real-world-lists-vs-arrays-QUaUd)\n    - [ ] [Why you should avoid linked lists (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=YQs6IC-vgmo)\n    - [ ] Gotcha: you need pointer to pointer knowledge:\n        (for when you pass a pointer to a function that may change the address where that pointer points)\n        This page is just to get a grasp on ptr to ptr. I don't recommend this list traversal style. Readability and maintainability suffer due to cleverness.\n        - [Pointers to Pointers](https:\u002F\u002Fwww.eskimo.com\u002F~scs\u002Fcclass\u002Fint\u002Fsx8.html)\n    - [ ] Implement (I did with tail pointer & without):\n        - [ ] size() - returns the number of data elements in the list\n        - [ ] empty() - bool returns true if empty\n        - [ ] value_at(index) - returns the value of the nth item (starting at 0 for first)\n        - [ ] push_front(value) - adds an item to the front of the list\n        - [ ] pop_front() - remove the front item and return its value\n        - [ ] push_back(value) - adds an item at the end\n        - [ ] pop_back() - removes end item and returns its value\n        - [ ] front() - get the value of the front item\n        - [ ] back() - get the value of the end item\n        - [ ] insert(index, value) - insert value at index, so the current item at that index is pointed to by the new item at the index\n        - [ ] erase(index) - removes node at given index\n        - [ ] value_n_from_end(n) - returns the value of the node at the nth position from the end of the list\n        - [ ] reverse() - reverses the list\n        - [ ] remove_value(value) - removes the first item in the list with this value\n    - [ ] Doubly-linked List\n        - [Description (video)](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Fdata-structures\u002Fdoubly-linked-lists-jpGKD)\n        - No need to implement\n\n- ### Stack\n    - [ ] [Stacks (video)](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Fdata-structures\u002Fstacks-UdKzQ)\n    - [ ] [[Review] Stacks in 3 minutes (video)](https:\u002F\u002Fyoutu.be\u002FKcT3aVgrrpU)\n    - [ ] Will not implement. Implementing with the array is trivial\n\n- ### Queue\n    - [ ] [Queue (video)](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Fdata-structures\u002Fqueues-EShpq)\n    - [ ] [Circular buffer\u002FFIFO](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FCircular_buffer)\n    - [ ] [[Review] Queues in 3 minutes (video)](https:\u002F\u002Fyoutu.be\u002FD6gu-_tmEpQ)\n    - [ ] Implement using linked-list, with tail pointer:\n        - enqueue(value) - adds value at a position at the tail\n        - dequeue() - returns value and removes least recently added element (front)\n        - empty()\n    - [ ] Implement using a fixed-sized array:\n        - enqueue(value) - adds item at end of available storage\n        - dequeue() - returns value and removes least recently added element\n        - empty()\n        - full()\n    - [ ] Cost:\n        - a bad implementation using a linked list where you enqueue at the head and dequeue at the tail would be O(n)\n            because you'd need the next to last element, causing a full traversal of each dequeue\n        - enqueue: O(1) (amortized, linked list and array [probing])\n        - dequeue: O(1) (linked list and array)\n        - empty: O(1) (linked list and array)\n\n- ### Hash table\n    - [ ] Videos:\n        - [ ] [Hashing with Chaining (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=0M_kIqhwbFo&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=8)\n        - [ ] [Table Doubling, Karp-Rabin (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=BRO7mVIFt08&index=9&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb)\n        - [ ] [Open Addressing, Cryptographic Hashing (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=rvdJDijO2Ro&index=10&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb)\n        - [ ] [PyCon 2010: The Mighty Dictionary (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=C4Kc8xzcA68)\n        - [ ] [PyCon 2017: The Dictionary Even Mightier (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=66P5FMkWoVU)\n        - [ ] [(Advanced) Randomization: Universal & Perfect Hashing (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=z0lJ2k0sl1g&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp&index=11)\n        - [ ] [(Advanced) Perfect hashing (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=N0COwN14gt0&list=PL2B4EEwhKD-NbwZ4ezj7gyc_3yNrojKM9&index=4)\n        - [ ] [[Review] Hash tables in 4 minutes (video)](https:\u002F\u002Fyoutu.be\u002FknV86FlSXJ8)\n\n    - [ ] Online Courses:\n        - [ ] [Core Hash Tables (video)](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Fdata-structures-optimizing-performance\u002Fcore-hash-tables-m7UuP)\n        - [ ] [Data Structures (video)](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fdata-structures\u002Fhome\u002Fweek\u002F4)\n        - [ ] [Phone Book Problem (video)](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Fdata-structures\u002Fphone-book-problem-NYZZP)\n        - [ ] distributed hash tables:\n            - [Instant Uploads And Storage Optimization In Dropbox (video)](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Fdata-structures\u002Finstant-uploads-and-storage-optimization-in-dropbox-DvaIb)\n            - [Distributed Hash Tables (video)](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Fdata-structures\u002Fdistributed-hash-tables-tvH8H)\n\n    - [ ] Implement with array using linear probing\n        - hash(k, m) - m is the size of the hash table\n        - add(key, value) - if the key already exists, update value\n        - exists(key)\n        - get(key)\n        - remove(key)\n\n**[⬆ back to top](#table-of-contents)**\n\n## More Knowledge\n\n- ### Binary search\n    - [ ] [Binary Search (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=D5SrAga1pno)\n    - [ ] [Binary Search (video)](https:\u002F\u002Fwww.khanacademy.org\u002Fcomputing\u002Fcomputer-science\u002Falgorithms\u002Fbinary-search\u002Fa\u002Fbinary-search)\n    - [ ] [detail](https:\u002F\u002Fwww.topcoder.com\u002Fthrive\u002Farticles\u002FBinary%20Search)\n    - [ ] [blueprint](https:\u002F\u002Fleetcode.com\u002Fdiscuss\u002Fgeneral-discussion\u002F786126\u002Fpython-powerful-ultimate-binary-search-template-solved-many-problems)\n    - [ ] [[Review] Binary search in 4 minutes (video)](https:\u002F\u002Fyoutu.be\u002FfDKIpRe8GW4)\n    - [ ] Implement:\n        - binary search (on a sorted array of integers)\n        - binary search using recursion\n\n- ### Bitwise operations\n    - [ ] [Bits cheat sheet](https:\u002F\u002Fgithub.com\u002Fjwasham\u002Fcoding-interview-university\u002Fblob\u002Fmain\u002Fextras\u002Fcheat%20sheets\u002Fbits-cheat-sheet.pdf)\n        - you should know many of the powers of 2 from (2^1 to 2^16 and 2^32)\n    - [ ] Get a really good understanding of manipulating bits with: &, |, ^, ~, >>, \u003C\u003C\n        - [ ] [words](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FWord_(computer_architecture))\n        - [ ] Good intro:\n            [Bit Manipulation (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=7jkIUgLC29I)\n        - [ ] [C Programming Tutorial 2-10: Bitwise Operators (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=d0AwjSpNXR0)\n        - [ ] [Bit Manipulation](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FBit_manipulation)\n        - [ ] [Bitwise Operation](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FBitwise_operation)\n        - [ ] [Bithacks](https:\u002F\u002Fgraphics.stanford.edu\u002F~seander\u002Fbithacks.html)\n        - [ ] [The Bit Twiddler](https:\u002F\u002Fbits.stephan-brumme.com\u002F)\n        - [ ] [The Bit Twiddler Interactive](https:\u002F\u002Fbits.stephan-brumme.com\u002Finteractive.html)\n        - [ ] [Bit Hacks (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ZusiKXcz_ac)\n\t\t- [ ] [Practice Operations](https:\u002F\u002Fpconrad.github.io\u002Fold_pconrad_cs16\u002Ftopics\u002FbitOps\u002F)\n    - [ ] 2s and 1s complement\n        - [Binary: Plusses & Minuses (Why We Use Two's Complement) (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=lKTsv6iVxV4)\n        - [1s Complement](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FOnes%27_complement)\n        - [2s Complement](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FTwo%27s_complement)\n    - [ ] Count set bits\n        - [4 ways to count bits in a byte (video)](https:\u002F\u002Fyoutu.be\u002FHzuzo9NJrlc)\n        - [Count Bits](https:\u002F\u002Fgraphics.stanford.edu\u002F~seander\u002Fbithacks.html#CountBitsSetKernighan)\n        - [How To Count The Number Of Set Bits In a 32 Bit Integer](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F109023\u002Fhow-to-count-the-number-of-set-bits-in-a-32-bit-integer)\n    - [ ] Swap values:\n        - [Swap](https:\u002F\u002Fbits.stephan-brumme.com\u002Fswap.html)\n    - [ ] Absolute value:\n        - [Absolute Integer](https:\u002F\u002Fbits.stephan-brumme.com\u002FabsInteger.html)\n\n**[⬆ back to top](#table-of-contents)**\n\n## Trees\n\n- ### Trees - Intro\n    - [ ] [Intro to Trees (video)](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Fdata-structures\u002Ftrees-95qda)\n    - [ ] [Tree Traversal (video)](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Fdata-structures\u002Ftree-traversal-fr51b)\n    - [ ] [BFS(breadth-first search) and DFS(depth-first search) (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=uWL6FJhq5fM)\n        - BFS notes:\n           - level order (BFS, using queue)\n           - time complexity: O(n)\n           - space complexity: best: O(1), worst: O(n\u002F2)=O(n)\n        - DFS notes:\n            - time complexity: O(n)\n            - space complexity:\n                best: O(log n) - avg. height of tree\n                worst: O(n)\n            - inorder (DFS: left, self, right)\n            - postorder (DFS: left, right, self)\n            - preorder (DFS: self, left, right)\n    - [ ] [[Review] Breadth-first search in 4 minutes (video)](https:\u002F\u002Fyoutu.be\u002FHZ5YTanv5QE)\n    - [ ] [[Review] Depth-first search in 4 minutes (video)](https:\u002F\u002Fyoutu.be\u002FUrx87-NMm6c)\n    - [ ] [[Review] Tree Traversal (playlist) in 11 minutes (video)](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL9xmBV_5YoZO1JC2RgEi04nLy6D-rKk6b)\n\n- ### Binary search trees: BSTs\n    - [ ] [Binary Search Tree Review (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=x6At0nzX92o&index=1&list=PLA5Lqm4uh9Bbq-E0ZnqTIa8LRaL77ica6)\n    - [ ] [Introduction (video)](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fdata-structures\u002Flecture\u002FE7cXP\u002Fintroduction)\n    - [ ] [MIT (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=76dhtgZt38A&ab_channel=MITOpenCourseWare)\n    - C\u002FC++:\n        - [ ] [Binary search tree - Implementation in C\u002FC++ (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=COZK7NATh4k&list=PL2_aWCzGMAwI3W_JlcBbtYTwiQSsOTa6P&index=28)\n        - [ ] [BST implementation - memory allocation in stack and heap (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=hWokyBoo0aI&list=PL2_aWCzGMAwI3W_JlcBbtYTwiQSsOTa6P&index=29)\n        - [ ] [Find min and max element in a binary search tree (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Ut90klNN264&index=30&list=PL2_aWCzGMAwI3W_JlcBbtYTwiQSsOTa6P)\n        - [ ] [Find the height of a binary tree (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=_pnqMz5nrRs&list=PL2_aWCzGMAwI3W_JlcBbtYTwiQSsOTa6P&index=31)\n        - [ ] [Binary tree traversal - breadth-first and depth-first strategies (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=9RHO6jU--GU&list=PL2_aWCzGMAwI3W_JlcBbtYTwiQSsOTa6P&index=32)\n        - [ ] [Binary tree: Level Order Traversal (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=86g8jAQug04&index=33&list=PL2_aWCzGMAwI3W_JlcBbtYTwiQSsOTa6P)\n        - [ ] [Binary tree traversal: Preorder, Inorder, Postorder (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=gm8DUJJhmY4&index=34&list=PL2_aWCzGMAwI3W_JlcBbtYTwiQSsOTa6P)\n        - [ ] [Check if a binary tree is a binary search tree or not (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=yEwSGhSsT0U&index=35&list=PL2_aWCzGMAwI3W_JlcBbtYTwiQSsOTa6P)\n        - [ ] [Delete a node from Binary Search Tree (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=gcULXE7ViZw&list=PL2_aWCzGMAwI3W_JlcBbtYTwiQSsOTa6P&index=36)\n        - [ ] [Inorder Successor in a binary search tree (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=5cPbNCrdotA&index=37&list=PL2_aWCzGMAwI3W_JlcBbtYTwiQSsOTa6P)\n    - [ ] Implement:\n        - [ ] [insert    \u002F\u002F insert value into tree](https:\u002F\u002Fleetcode.com\u002Fproblems\u002Finsert-into-a-binary-search-tree\u002Fsubmissions\u002F987660183\u002F)\n        - [ ] get_node_count \u002F\u002F get count of values stored\n        - [ ] print_values \u002F\u002F prints the values in the tree, from min to max\n        - [ ] delete_tree\n        - [ ] is_in_tree \u002F\u002F returns true if a given value exists in the tree\n        - [ ] [get_height \u002F\u002F returns the height in nodes (single node's height is 1)](https:\u002F\u002Fwww.geeksforgeeks.org\u002Ffind-the-maximum-depth-or-height-of-a-tree\u002F)\n        - [ ] get_min   \u002F\u002F returns the minimum value stored in the tree\n        - [ ] get_max   \u002F\u002F returns the maximum value stored in the tree\n        - [ ] [is_binary_search_tree](https:\u002F\u002Fleetcode.com\u002Fproblems\u002Fvalidate-binary-search-tree\u002F)\n        - [ ] delete_value\n        - [ ] get_successor \u002F\u002F returns the next-highest value in the tree after given value, -1 if none\n\n- ### Heap \u002F Priority Queue \u002F Binary Heap\n    - visualized as a tree, but is usually linear in storage (array, linked list)\n    - [ ] [Heap](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FHeap_(data_structure))\n    - [ ] [Introduction (video)](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Fdata-structures\u002Fintroduction-2OpTs)\n    - [ ] [Binary Trees (video)](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fdata-structures\u002Flecture\u002FGRV2q\u002Fbinary-trees)\n    - [ ] [Tree Height Remark (video)](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fdata-structures\u002Fsupplement\u002FS5xxz\u002Ftree-height-remark)\n    - [ ] [Basic Operations (video)](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fdata-structures\u002Flecture\u002F0g1dl\u002Fbasic-operations)\n    - [ ] [Complete Binary Trees (video)](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fdata-structures\u002Flecture\u002Fgl5Ni\u002Fcomplete-binary-trees)\n    - [ ] [Pseudocode (video)](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Fdata-structures\u002Flecture\u002FHxQo9\u002Fpseudocode)\n    - [ ] [Heap Sort - jumps to start (video)](https:\u002F\u002Fyoutu.be\u002FodNJmw5TOEE?list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&t=3291)\n    - [ ] [Heap Sort (video)](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Fdata-structures\u002Fheap-sort-hSzMO)\n    - [ ] [Building a heap (video)](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Fdata-structures\u002Fbuilding-a-heap-dwrOS)\n    - [ ] [MIT 6.006 Introduction to Algorithms: Binary Heaps](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Xnpo1atN-Iw&list=PLUl4u3cNGP63EdVPNLG3ToM6LaEUuStEY&index=12)\n    - [ ] [CS 61B Lecture 24: Priority Queues (video)](https:\u002F\u002Farchive.org\u002Fdetails\u002Fucberkeley_webcast_yIUFT6AKBGE)\n    - [ ] [Linear Time BuildHeap (max-heap)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=MiyLo8adrWw)\n    - [ ] [[Review] Heap (playlist) in 13 minutes (video)](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL9xmBV_5YoZNsyqgPW-DNwUeT8F8uhWc6)\n    - [ ] Implement a max-heap:\n        - [ ] insert\n        - [ ] sift_up - needed for insert\n        - [ ] get_max - returns the max item, without removing it\n        - [ ] get_size() - return number of elements stored\n        - [ ] is_empty() - returns true if the heap contains no elements\n        - [ ] extract_max - returns the max item, removing it\n        - [ ] sift_down - needed for extract_max\n        - [ ] remove(x) - removes item at index x\n        - [ ] heapify - create a heap from an array of elements, needed for heap_sort\n        - [ ] heap_sort() - take an unsorted array and turn it into a sorted array in place using a max heap or min heap\n\n**[⬆ back to top](#table-of-contents)**\n\n## Sorting\n\n- [ ] Notes:\n    - Implement sorts & know best case\u002Fworst case, average complexity of each:\n        - no bubble sort - it's terrible - O(n^2), except when n \u003C= 16\n    - [ ] Stability in sorting algorithms (\"Is Quicksort stable?\")\n        - [Sorting Algorithm Stability](https:\u002F\u002Fen.wikipedia.org\u002Fwiki\u002FSorting_algorithm#Stability)\n        - [Stability In Sorting Algorithms](http:\u002F\u002Fstackoverflow.com\u002Fquestions\u002F1517793\u002Fstability-in-sorting-algorithms)\n        - [Stability In Sorting Algorithms](http:\u002F\u002Fwww.geeksforgeeks.org\u002Fstability-in-sorting-algorithms\u002F)\n        - [Sorting Algorithms - Stability](http:\u002F\u002Fhomepages.math.uic.edu\u002F~leon\u002Fcs-mcs401-s08\u002Fhandouts\u002Fstability.pdf)\n    - [ ] Which algorithms can be used on linked lists? Which on arrays? Which of both?\n        - I wouldn't recommend sorting a linked list, but merge sort is doable.\n        - [Merge Sort For Linked List](http:\u002F\u002Fwww.geeksforgeeks.org\u002Fmerge-sort-for-linked-list\u002F)\n\n- For heapsort, see the Heap data structure above. Heap sort is great, but not stable\n\n- [ ] [Sedgewick - Mergesort (5 videos)](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Falgorithms-part1\u002Fhome\u002Fweek\u002F3)\n    - [ ] [1. Mergesort](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Falgorithms-part1\u002Fmergesort-ARWDq)\n    - [ ] [2. Bottom-up Mergesort](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Falgorithms-part1\u002Flecture\u002FPWNEl\u002Fbottom-up-mergesort)\n    - [ ] [3. Sorting Complexity](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Falgorithms-part1\u002Fsorting-complexity-xAltF)\n    - [ ] [4. Comparators](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Falgorithms-part1\u002Fcomparators-9FYhS)\n    - [ ] [5. Stability](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Falgorithms-part1\u002Flecture\u002FpvvLZ\u002Fstability)\n\n- [ ] [Sedgewick - Quicksort (4 videos)](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Falgorithms-part1\u002Fhome\u002Fweek\u002F3)\n    - [ ] [1. Quicksort](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Falgorithms-part1\u002Fquicksort-vjvnC)\n    - [ ] [2. Selection](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Falgorithms-part1\u002Fselection-UQxFT)\n    - [ ] [3. Duplicate Keys](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Falgorithms-part1\u002Fduplicate-keys-XvjPd)\n    - [ ] [4. System Sorts](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Falgorithms-part1\u002Fsystem-sorts-QBNZ7)\n\n- [ ] UC Berkeley:\n    - [ ] [CS 61B Lecture 29: Sorting I (video)](https:\u002F\u002Farchive.org\u002Fdetails\u002Fucberkeley_webcast_EiUvYS2DT6I)\n    - [ ] [CS 61B Lecture 30: Sorting II (video)](https:\u002F\u002Farchive.org\u002Fdetails\u002Fucberkeley_webcast_2hTY3t80Qsk)\n    - [ ] [CS 61B Lecture 32: Sorting III (video)](https:\u002F\u002Farchive.org\u002Fdetails\u002Fucberkeley_webcast_Y6LOLpxg6Dc)\n    - [ ] [CS 61B Lecture 33: Sorting V (video)](https:\u002F\u002Farchive.org\u002Fdetails\u002Fucberkeley_webcast_qNMQ4ly43p4)\n    - [ ] [CS 61B 2014-04-21: Radix Sort(video)](https:\u002F\u002Farchive.org\u002Fdetails\u002Fucberkeley_webcast_pvbBMd-3NoI)\n\n- [ ] [Bubble Sort (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=P00xJgWzz2c&index=1&list=PL89B61F78B552C1AB)\n- [ ] [Analyzing Bubble Sort (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ni_zk257Nqo&index=7&list=PL89B61F78B552C1AB)\n- [ ] [Insertion Sort, Merge Sort (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Kg4bqzAqRBM&index=3&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb)\n- [ ] [Insertion Sort (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=c4BRHC7kTaQ&index=2&list=PL89B61F78B552C1AB)\n- [ ] [Merge Sort (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=GCae1WNvnZM&index=3&list=PL89B61F78B552C1AB)\n- [ ] [Quicksort (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=y_G9BkAm6B8&index=4&list=PL89B61F78B552C1AB)\n- [ ] [Selection Sort (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=6nDMgr0-Yyo&index=8&list=PL89B61F78B552C1AB)\n\n- [ ] Merge sort code:\n    - [ ] [Using output array (C)](http:\u002F\u002Fwww.cs.yale.edu\u002Fhomes\u002Faspnes\u002Fclasses\u002F223\u002Fexamples\u002Fsorting\u002Fmergesort.c)\n    - [ ] [Using output array (Python)](https:\u002F\u002Fgithub.com\u002Fjwasham\u002Fpractice-python\u002Fblob\u002Fmaster\u002Fmerge_sort\u002Fmerge_sort.py)\n    - [ ] [In-place (C++)](https:\u002F\u002Fgithub.com\u002Fjwasham\u002Fpractice-cpp\u002Fblob\u002Fmaster\u002Fmerge_sort\u002Fmerge_sort.cc)\n- [ ] Quick sort code:\n    - [ ] [Implementation (C)](http:\u002F\u002Fwww.cs.yale.edu\u002Fhomes\u002Faspnes\u002Fclasses\u002F223\u002Fexamples\u002Frandomization\u002Fquick.c)\n    - [ ] [Implementation (C)](https:\u002F\u002Fgithub.com\u002Fjwasham\u002Fpractice-c\u002Fblob\u002Fmaster\u002Fquick_sort\u002Fquick_sort.c)\n    - [ ] [Implementation (Python)](https:\u002F\u002Fgithub.com\u002Fjwasham\u002Fpractice-python\u002Fblob\u002Fmaster\u002Fquick_sort\u002Fquick_sort.py)\n\n- [ ] [[Review] Sorting (playlist) in 18 minutes](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL9xmBV_5YoZOZSbGAXAPIq1BeUf4j20pl)\n    - [ ] [Quick sort in 4 minutes (video)](https:\u002F\u002Fyoutu.be\u002FHoixgm4-P4M)\n    - [ ] [Heap sort in 4 minutes (video)](https:\u002F\u002Fyoutu.be\u002F2DmK_H7IdTo)\n    - [ ] [Merge sort in 3 minutes (video)](https:\u002F\u002Fyoutu.be\u002F4VqmGXwpLqc)\n    - [ ] [Bubble sort in 2 minutes (video)](https:\u002F\u002Fyoutu.be\u002Fxli_FI7CuzA)\n    - [ ] [Selection sort in 3 minutes (video)](https:\u002F\u002Fyoutu.be\u002Fg-PGLbMth_g)\n    - [ ] [Insertion sort in 2 minutes (video)](https:\u002F\u002Fyoutu.be\u002FJU767SDMDvA)\n\n- [ ] Implement:\n    - [ ] Mergesort: O(n log n) average and worst case\n    - [ ] Quicksort O(n log n) average case\n    - Selection sort and insertion sort are both O(n^2) average and worst-case\n    - For heapsort, see Heap data structure above\n\n- [ ] Not required, but I recommended them:\n    - [ ] [Sedgewick - Radix Sorts (6 videos)](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Falgorithms-part2\u002Fhome\u002Fweek\u002F3)\n        - [ ] [1. Strings in Java](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Falgorithms-part2\u002Flecture\u002FvGHvb\u002Fstrings-in-java)\n        - [ ] [2. Key Indexed Counting](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Falgorithms-part2\u002Fkey-indexed-counting-2pi1Z)\n        - [ ] [3. Least Significant Digit First String Radix Sort](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Falgorithms-part2\u002Flecture\u002Fc1U7L\u002Flsd-radix-sort)\n        - [ ] [4. Most Significant Digit First String Radix Sort](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Falgorithms-part2\u002Flecture\u002FgFxwG\u002Fmsd-radix-sort)\n        - [ ] [5. 3 Way Radix Quicksort](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Falgorithms-part2\u002F3-way-radix-quicksort-crkd5)\n        - [ ] [6. Suffix Arrays](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Falgorithms-part2\u002Flecture\u002FTH18W\u002Fsuffix-arrays)\n    - [ ] [Radix Sort](http:\u002F\u002Fwww.cs.yale.edu\u002Fhomes\u002Faspnes\u002Fclasses\u002F223\u002Fnotes.html#radixSort)\n    - [ ] [Radix Sort (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=xhr26ia4k38)\n    - [ ] [Radix Sort, Counting Sort (linear time given constraints) (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Nz1KZXbghj8&index=7&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb)\n    - [ ] [Randomization: Matrix Multiply, Quicksort, Freivalds' algorithm (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=cNB2lADK3_s&index=8&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp)\n    - [ ] [Sorting in Linear Time (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=pOKy3RZbSws&list=PLUl4u3cNGP61hsJNdULdudlRL493b-XZf&index=14)\n\nAs a summary, here is a visual representation of [15 sorting algorithms](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=kPRA0W1kECg).\nIf you need more detail on this subject, see the \"Sorting\" section in [Additional Detail on Some Subjects](#additional-detail-on-some-subjects)\n\n**[⬆ back to top](#table-of-contents)**\n\n## Graphs\n\nGraphs can be used to represent many problems in computer science, so this section is long, like trees and sorting.\n\n- Notes:\n    - There are 4 basic ways to represent a graph in memory:\n        - objects and pointers\n        - adjacency matrix\n        - adjacency list\n        - adjacency map\n    - Familiarize yourself with each representation and its pros & cons\n    - BFS and DFS - know their computational complexity, their trade-offs, and how to implement them in real code\n    - When asked a question, look for a graph-based solution first, then move on if none\n\n- [ ] MIT(videos):\n    - [ ] [Breadth-First Search](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=oFVYVzlvk9c&t=14s&ab_channel=MITOpenCourseWare)\n    - [ ] [Depth-First Search](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=IBfWDYSffUU&t=32s&ab_channel=MITOpenCourseWare)\n\n- [ ] Skiena Lectures - great intro:\n    - [ ] [CSE373 2020 - Lecture 10 - Graph Data Structures (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Sjk0xqWWPCc&list=PLOtl7M3yp-DX6ic0HGT0PUX_wiNmkWkXx&index=10)\n    - [ ] [CSE373 2020 - Lecture 11 - Graph Traversal (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ZTwjXj81NVY&list=PLOtl7M3yp-DX6ic0HGT0PUX_wiNmkWkXx&index=11)\n    - [ ] [CSE373 2020 - Lecture 12 - Depth First Search (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=KyordYB3BOs&list=PLOtl7M3yp-DX6ic0HGT0PUX_wiNmkWkXx&index=12)\n    - [ ] [CSE373 2020 - Lecture 13 - Minimum Spanning Trees (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=oolm2VnJUKw&list=PLOtl7M3yp-DX6ic0HGT0PUX_wiNmkWkXx&index=13)\n    - [ ] [CSE373 2020 - Lecture 14 - Minimum Spanning Trees (con't) (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=RktgPx0MarY&list=PLOtl7M3yp-DX6ic0HGT0PUX_wiNmkWkXx&index=14)\n    - [ ] [CSE373 2020 - Lecture 15 - Graph Algorithms (con't 2) (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=MUe5DXRhyAo&list=PLOtl7M3yp-DX6ic0HGT0PUX_wiNmkWkXx&index=15)\n\n- [ ] Graphs (review and more):\n\n    - [ ] [6.006 Single-Source Shortest Paths Problem (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Aa2sqUhIn-E&index=15&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb)\n    - [ ] [6.006 Dijkstra (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=NSHizBK9JD8&t=1731s&ab_channel=MITOpenCourseWare)\n    - [ ] [6.006 Bellman-Ford (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=f9cVS_URPc0&ab_channel=MITOpenCourseWare)\n    - [ ] [6.006 Speeding Up Dijkstra (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=CHvQ3q_gJ7E&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=18)\n    - [ ] [Aduni: Graph Algorithms I - Topological Sorting, Minimum Spanning Trees, Prim's Algorithm -  Lecture 6 (video)]( https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=i_AQT_XfvD8&index=6&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm)\n    - [ ] [Aduni: Graph Algorithms II - DFS, BFS, Kruskal's Algorithm, Union Find Data Structure - Lecture 7 (video)]( https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=ufj5_bppBsA&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&index=7)\n    - [ ] [Aduni: Graph Algorithms III: Shortest Path - Lecture 8 (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=DiedsPsMKXc&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&index=8)\n    - [ ] [Aduni: Graph Alg. IV: Intro to geometric algorithms - Lecture 9 (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=XIAQRlNkJAw&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&index=9)\n    - [ ] [CS 61B 2014: Weighted graphs (video)](https:\u002F\u002Farchive.org\u002Fdetails\u002Fucberkeley_webcast_zFbq8vOZ_0k)\n    - [ ] [Greedy Algorithms: Minimum Spanning Tree (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=tKwnms5iRBU&index=16&list=PLUl4u3cNGP6317WaSNfmCvGym2ucw3oGp)\n    - [ ] [Strongly Connected Components Kosaraju's Algorithm Graph Algorithm (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=RpgcYiky7uw)\n    - [ ] [[Review] Shortest Path Algorithms (playlist) in 16 minutes (video)](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL9xmBV_5YoZO-Y-H3xIC9DGSfVYJng9Yw)\n    - [ ] [[Review] Minimum Spanning Trees (playlist) in 4 minutes (video)](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PL9xmBV_5YoZObEi3Hf6lmyW-CBfs7nkOV)\n\n- Full Coursera Course:\n    - [ ] [Algorithms on Graphs (video)](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Falgorithms-on-graphs\u002Fhome\u002Fwelcome)\n\n- I'll implement:\n    - [ ] DFS with adjacency list (recursive)\n    - [ ] DFS with adjacency list (iterative with stack)\n    - [ ] DFS with adjacency matrix (recursive)\n    - [ ] DFS with adjacency matrix (iterative with stack)\n    - [ ] BFS with adjacency list\n    - [ ] BFS with adjacency matrix\n    - [ ] single-source shortest path (Dijkstra)\n    - [ ] minimum spanning tree\n    - DFS-based algorithms (see Aduni videos above):\n        - [ ] check for a cycle (needed for topological sort, since we'll check for the cycle before starting)\n        - [ ] topological sort\n        - [ ] count connected components in a graph\n        - [ ] list strongly connected components\n        - [ ] check for bipartite graph\n\n**[⬆ back to top](#table-of-contents)**\n\n## Even More Knowledge\n\n- ### Recursion\n    - [ ] Stanford lectures on recursion & backtracking:\n        - [ ] [Lecture 8 | Programming Abstractions (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=gl3emqCuueQ&list=PLFE6E58F856038C69&index=8)\n        - [ ] [Lecture 9 | Programming Abstractions (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=uFJhEPrbycQ&list=PLFE6E58F856038C69&index=9)\n        - [ ] [Lecture 10 | Programming Abstractions (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=NdF1QDTRkck&index=10&list=PLFE6E58F856038C69)\n        - [ ] [Lecture 11 | Programming Abstractions (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=p-gpaIGRCQI&list=PLFE6E58F856038C69&index=11)\n    - When it is appropriate to use it?\n    - How is tail recursion better than not?\n        - [ ] [What Is Tail Recursion Why Is It So Bad?](https:\u002F\u002Fwww.quora.com\u002FWhat-is-tail-recursion-Why-is-it-so-bad)\n        - [ ] [Tail Recursion (video)](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Fprogramming-languages\u002Ftail-recursion-YZic1)\n    - [ ] [5 Simple Steps for Solving Any Recursive Problem(video)](https:\u002F\u002Fyoutu.be\u002FngCos392W4w)\n\n\tBacktracking Blueprint: [Java](https:\u002F\u002Fleetcode.com\u002Fproblems\u002Fcombination-sum\u002Fdiscuss\u002F16502\u002FA-general-approach-to-backtracking-questions-in-Java-(Subsets-Permutations-Combination-Sum-Palindrome-Partitioning))\n\t[Python](https:\u002F\u002Fleetcode.com\u002Fproblems\u002Fcombination-sum\u002Fdiscuss\u002F429538\u002FGeneral-Backtracking-questions-solutions-in-Python-for-reference-%3A)\n- ### Dynamic Programming\n    - You probably won't see any dynamic programming problems in your interview, but it's worth being able to recognize a\n    problem as being a candidate for dynamic programming.\n    - This subject can be pretty difficult, as each DP soluble problem must be defined as a recursion relation, and coming up with it can be tricky.\n    - I suggest looking at many examples of DP problems until you have a solid understanding of the pattern involved.\n    - [ ] Videos:\n        - [ ] [Skiena: CSE373 2020 - Lecture 19 - Introduction to Dynamic Programming (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=wAA0AMfcJHQ&list=PLOtl7M3yp-DX6ic0HGT0PUX_wiNmkWkXx&index=18)\n        - [ ] [Skiena: CSE373 2020 - Lecture 20 - Edit Distance (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=T3A4jlHlhtA&list=PLOtl7M3yp-DX6ic0HGT0PUX_wiNmkWkXx&index=19)\n        - [ ] [Skiena: CSE373 2020 - Lecture 20 - Edit Distance (continued) (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=iPnPVcZmRbE&list=PLOtl7M3yp-DX6ic0HGT0PUX_wiNmkWkXx&index=20)\n        - [ ] [Skiena: CSE373 2020 - Lecture 21 - Dynamic Programming (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=2xPE4Wq8coQ&list=PLOtl7M3yp-DX6ic0HGT0PUX_wiNmkWkXx&index=21)\n        - [ ] [Skiena: CSE373 2020 - Lecture 22 - Dynamic Programming and Review (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=Yh3RzqQGsyI&list=PLOtl7M3yp-DX6ic0HGT0PUX_wiNmkWkXx&index=22)\n        - [ ] [Simonson: Dynamic Programming 0 (starts at 59:18) (video)](https:\u002F\u002Fyoutu.be\u002FJ5aJEcOr6Eo?list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&t=3558)\n        - [ ] [Simonson: Dynamic Programming I - Lecture 11 (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=0EzHjQ_SOeU&index=11&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm)\n        - [ ] [Simonson: Dynamic programming II - Lecture 12 (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=v1qiRwuJU7g&list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&index=12)\n        - [ ] List of individual DP problems (each is short):\n            [Dynamic Programming (video)](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLrmLmBdmIlpsHaNTPP_jHHDx_os9ItYXr)\n    - [ ] Yale Lecture notes:\n        - [ ] [Dynamic Programming](http:\u002F\u002Fwww.cs.yale.edu\u002Fhomes\u002Faspnes\u002Fclasses\u002F223\u002Fnotes.html#dynamicProgramming)\n    - [ ] Coursera:\n        - [ ] [The RNA secondary structure problem (video)](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Falgorithmic-thinking-2\u002Flecture\u002F80RrW\u002Fthe-rna-secondary-structure-problem)\n        - [ ] [A dynamic programming algorithm (video)](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Falgorithmic-thinking-2\u002Fa-dynamic-programming-algorithm-PSonq)\n        - [ ] [Illustrating the DP algorithm (video)](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Falgorithmic-thinking-2\u002Fillustrating-the-dp-algorithm-oUEK2)\n        - [ ] [Running time of the DP algorithm (video)](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Falgorithmic-thinking-2\u002Flecture\u002FnfK2r\u002Frunning-time-of-the-dp-algorithm)\n        - [ ] [DP vs. recursive implementation (video)](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Falgorithmic-thinking-2\u002Flecture\u002FM999a\u002Fdp-vs-recursive-implementation)\n        - [ ] [Global pairwise sequence alignment (video)](https:\u002F\u002Fwww.coursera.org\u002Flecture\u002Falgorithmic-thinking-2\u002Fglobal-pairwise-sequence-alignment-UZ7o6)\n        - [ ] [Local pairwise sequence alignment (video)](https:\u002F\u002Fwww.coursera.org\u002Flearn\u002Falgorithmic-thinking-2\u002Flecture\u002FWnNau\u002Flocal-pairwise-sequence-alignment)\n\n- ### Design patterns\n    - [ ] [Quick UML review (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=3cmzqZzwNDM&list=PLGLfVvz_LVvQ5G-LdJ8RLqe-ndo7QITYc&index=3)\n    - [ ] Learn these patterns:\n        - [ ] strategy\n        - [ ] singleton\n        - [ ] adapter\n        - [ ] prototype\n        - [ ] decorator\n        - [ ] visitor\n        - [ ] factory, abstract factory\n        - [ ] facade\n        - [ ] observer\n        - [ ] proxy\n        - [ ] delegate\n        - [ ] command\n        - [ ] state\n        - [ ] memento\n        - [ ] iterator\n        - [ ] composite\n        - [ ] flyweight\n    - [ ] [Series of videos (27 videos)](https:\u002F\u002Fwww.youtube.com\u002Fplaylist?list=PLF206E906175C7E07)\n    - [ ] [Book: Head First Design Patterns](https:\u002F\u002Fwww.amazon.com\u002FHead-First-Design-Patterns-Freeman\u002Fdp\u002F0596007124)\n        - I know the canonical book is \"Design Patterns: Elements of Reusable Object-Oriented Software\", but Head First is great for beginners to OO.\n    - [Handy reference: 101 Design Patterns & Tips for Developers](https:\u002F\u002Fsourcemaking.com\u002Fdesign-patterns-and-tips)\n\n- ### Combinatorics (n choose k) & Probability\n    - [ ] [Math Skills: How to find Factorial, Permutation, and Combination (Choose) (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=8RRo6Ti9d0U)\n    - [ ] [Make School: Probability (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=sZkAAk9Wwa4)\n    - [ ] [Make School: More Probability and Markov Chains (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=dNaJg-mLobQ)\n    - [ ] Khan Academy:\n        - Course layout:\n            - [ ] [Basic Theoretical Probability](https:\u002F\u002Fwww.khanacademy.org\u002Fmath\u002Fprobability\u002Fprobability-and-combinatorics-topic)\n        - Just the videos - 41 (each are simple and each are short):\n            - [ ] [Probability Explained (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=uzkc-qNVoOk&list=PLC58778F28211FA19)\n\n- ### NP, NP-Complete and Approximation Algorithms\n    - Know about the most famous classes of NP-complete problems, such as the traveling salesman and the knapsack problem,\n        and be able to recognize them when an interviewer asks you them in disguise.\n    - Know what NP-complete means.\n    - [ ] [Computational Complexity (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=moPtwq_cVH8&list=PLUl4u3cNGP61Oq3tWYp6V_F-5jb5L2iHb&index=23)\n    - [ ] Simonson:\n        - [ ] [Greedy Algs. II & Intro to NP-Completeness (video)](https:\u002F\u002Fyoutu.be\u002FqcGnJ47Smlo?list=PLFDnELG9dpVxQCxuD-9BSy2E7BWY3t5Sm&t=2939)\n        - [ ] [NP Completeness II & Reductions (video)](https:\u002F\u002Fwww.youtube.com\u002Fwatch?v=e0tGC","该项目是一个全面的计算机科学学习计划，旨在帮助人们成为软件工程师。它涵盖了数据结构、算法、软件工程等核心内容，并提供详细的面试准备指南。项目特别适合那些希望进入如亚马逊、Facebook、谷歌和微软等大型科技公司的求职者，以及任何希望系统性提升编程和技术面试能力的人士。通过遵循这一学习路径，用户可以高效地掌握必备技能，避免不必要的学习弯路。",2,"2026-06-17 02:30:08","trending"]