# Algorithms

### Websites

*Websites you should use to learn classic algorithms*

* [A Visual Guide to Graph Traversal Algorithms](https://workshape.github.io/visual-graph-algorithms/) - Interactive visualisations for learning how graph traversal algorithms work
* [W3School](https://www.w3schools.in/data-structures-tutorial/intro/) - Data Structures tutorial.
* [CodeChef](https://www.codechef.com/LEARNDSA/) - Learning DSA by practice on Codechef
* [Algorithm Visualizer](http://algo-visualizer.jasonpark.me) - Dozens of animated algorithms (with code), and you can also create your own.
* [Algorithms Visualization](http://bost.ocks.org/mike/algorithms/) - A dense article on Algorithms Visualization.
* [Big-O Cheat Sheet](http://bigocheatsheet.com) - Big-O complexities of common algorithms used in Computer Science.
* [Code-Drills](https://code-drills.com/tools/comparator) - Practice problems recommender (includes Codeforces, Codechef and Spoj).
* [CP-Algorithms](https://cp-algorithms.com) - Algorithms and data structures especially popular in field of competitive programming.
* [Data Structure Visualizations](http://www.cs.usfca.edu/~galles/visualization/Algorithms.html) - Visualize the behavior of Data Structures and play with its operations.
* [Geeks for Geeks](http://www.geeksforgeeks.org/fundamentals-of-algorithms/) - Lots and lots of well explained and implemented algorithms.
* [Path Finding](https://qiao.github.io/PathFinding.js/visual/) - A visual representation on how algorithms such as A\*, IDA\*, Breadth-First-Search, Best-First-Search and others describe a path between two points A and B.
* [Programiz](https://www.programiz.com/dsa) - Easy to follow tutorials on data structures and algorithms along with suitable examples.
* [Rosetta Code](http://rosettacode.org/wiki/Rosetta_Code) - A programming chrestomathy site which aims to present implementations of many algorithms and data structures in different programming languages.
* [Sorting Algorithms](http://www.sorting-algorithms.com) - Nice and simple animations of sorting algorithms. With short codes and discussions.
* [Stoimen's web log](http://www.stoimen.com) - Some algorithms nicely explained.
* [The Sound of Sorting](http://panthema.net/2013/sound-of-sorting/) - The Sound of Sorting - "Audibilization" and Visualization of Sorting Algorithms
* [VisuAlgo](http://visualgo.net) - Visualising data structures and algorithms through animation.
* [Wikipedia - Algorithms](https://en.wikipedia.org/wiki/List_of_algorithms) - Of course!!
* [Wikipedia - Data Structures](https://en.wikipedia.org/wiki/List_of_data_structures) - and why not ?!!
* [Learnersbucket](https://learnersbucket.com) - Tutorials on data sturctures and algorithms in Javascript.
* [redblobgames](https://www.redblobgames.com) - interactive visual explanations of math and algorithms, using motivating examples from computer games.

### Online Courses

*Free and High Quality Courses Online*

* [Algorithms: Divide and Conquer, Sorting and Searching, and Randomized Algorithms](https://www.coursera.org/learn/algorithms-divide-conquer) - The primary topics are: asymptotic ("Big-oh") notation, sorting and searching, divide and conquer, and randomized algorithms.
* [Algorithms: Graph Search, Shortest Paths, and Data Structures](https://www.coursera.org/learn/algorithms-graphs-data-structures) - The primary topics are: data structures, graph primitives, and their applications.
* [Algorithms: Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming](https://www.coursera.org/learn/algorithms-greedy) - The primary topics are: greedy algorithms and dynamic programming.
* [Algorithms: Shortest Paths Revisited, NP-Complete Problems and What To Do About Them](https://www.coursera.org/learn/algorithms-npcomplete) - The primary topics are: shortest paths, NP-completeness and what it means for the algorithm designer, and strategies for coping with computationally intractable problems.
* [Algorithms, Part I](https://www.coursera.org/learn/algorithms-part1/home/welcome) - This course covers the essential information that every serious programmer needs to know about algorithms and data structures.Part I covers elementary data structures, sorting, and searching algorithms.
* [Algorithms, Part II](https://www.coursera.org/learn/algorithms-part2) - Part II focuses on graph- and string-processing algorithms.
* [Khan Academy Algorithms](https://www.khanacademy.org/computing/computer-science/algorithms) - Algorithm course ministred by Tomas Cormen and Devin Balkcom.
* [MIT - 6-006](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/lecture-videos/) - Well explained algorithms.
* [MIT - 6-046j](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-introduction-to-algorithms-sma-5503-fall-2005/video-lectures/) - Similar to the previous one, but with different algorithms.
* [MIT - 6-00sc](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-00sc-introduction-to-computer-science-and-programming-spring-2011/index.htm) - An easy and well explained introduction to algorithms.
* [Udacity Intro to Algorithms](https://www.udacity.com/course/intro-to-algorithms--cs215) - Python-based Algorithms course.
* [Algorithms in Motion](https://www.manning.com/livevideo/algorithms-in-motion) - Beginner's algorithms course with fun illustrations, based on the book Grokking Algorithms
* [~~YogiBearian YouTube Channel~~](https://www.youtube.com/channel/UCv3Kd0guxD5KWQtP---9D6g) ~~- Lots of well explained vidoes on various computer science subjects.~~ *Account terminated due to violations of Youtube Policies.*

### Books

*The most highly regarded books to learn algorithms*

#### Algorithms and Data structures

* [Algorithm Design](https://www.pearsonhighered.com/program/Kleinberg-Algorithm-Design/PGM319216.html) - Pretty straight-forward.
* [Algorithms](http://algs4.cs.princeton.edu/home/) - Problems explained with Java, OO good practices, visualizations, and free online resources.
* [Algorithms and Data Structures in JavaScript](https://gum.co/dsajs) - Classical algorithms and data structures implemented and explained using JavaScript.
* [Algorithms in a Nutshell](https://www.amazon.com/Algorithms-Nutshell-In-OReilly/dp/059651624X) - by George T. Heineman.
* [Classic Computer Science Problems in Python](https://www.manning.com/books/classic-computer-science-problems-in-python) -This great book presents dozens of coding challenges, ranging from simple tasks to clustering data using k-means.
* [Data Structures and Algorithms Made Easy](https://www.amazon.in/Data-Structures-Algorithms-Made-Easy/dp/819324527X) - A great way to implement algorithms with their specific programmable tasks.
* [Data Structures Using C](http://www.amazon.com/Data-Structures-Using-Aaron-Tenenbaum/dp/0131997467) - The basic concepts and usages of data structures.
* [Elementary Algorithms](https://github.com/liuxinyu95/AlgoXY) - An awesome book about algorithms and data structures.
* [Grokking Algorithms](http://www.manning.com/bhargava) - An illustrated book on algorithms with practical examples.
* [Introduction to Algorithms](http://mitpress.mit.edu/books/introduction-algorithms) - Essential!
* [Real World Algorithms: A Beginner's Guide](https://mitpress.mit.edu/books/real-world-algorithms) - An introduction to algorithms for readers with no background in advanced mathematics or computer science.
* [Swift Algorithms & Data Structures](http://shop.waynewbishop.com) - A practical guide to concepts, theory and code.
* [The Algorithm Design Manual](http://www.algorist.com) - Easy to read and full of real world examples.
* [The Art of Computer Programming](http://www-cs-faculty.stanford.edu/~uno/taocp.html) - The Book.
* [Structure and Interpretation of Computer Programs](https://mitpress.mit.edu/books/structure-and-interpretation-computer-programs-second-edition)
* [Algorithms and Data Structures in Action](https://www.manning.com/books/algorithms-and-data-structures-in-action) - A different and a great way to introduce algorithms and data structures that can be used at work.
* [Algorithmic Puzzles](https://www.amazon.com/Algorithmic-Puzzles-Anany-Levitin/dp/0199740445) - A very accessible illustration of algorithms in forms of puzzles. No programming experience is required!

#### Algorithm Analysis

* [Sedgewick & Flajolet. An Introduction to the Analysis of Algorithm](https://www.amazon.com/Introduction-Analysis-Algorithms-Introdu-Algori_p2-ebook/dp/B00B3TB7IQ) - Am advanced complete survey, intended only for the mathematically matured reader.
* [McConnell. Analysis of Algorithms](https://www.amazon.com/Analysis-Algorithms-Jeffrey-McConnell/dp/0763707821) - A very accessible and brief book on algorithms analysis, with implemented code included.
* [Vrajitoru & Knight. Practical Analysis of Algorithms](https://www.amazon.com/Practical-Analysis-Algorithms-Undergraduate-Computer/dp/331909887X) - A very accessible and brief book on algorithms analysis, with implemented code included.

#### Randomized Algorithms

* [Motwani & Raghavan. Randomized Algorithms](https://www.amazon.com/Randomized-Algorithms-Rajeev-Motwani/dp/0521474655) - A standard classic book.
* [Mitzenmacher & Upfal. Probability and Computing: Randomization and Probabilistic Techniques in Algorithms and Data Analysis](https://www.amazon.com/Probability-Computing-Randomization-Probabilistic-Techniques-ebook/dp/B06X9YBMFK) - Standard text for probability methods and their applications on randomized algorithms.

### Cheat Sheets

* [The Technical Interview Cheat Sheet](https://gist.github.com/TSiege/cbb0507082bb18ff7e4b)
* [Princeton DS Cheat Sheet](https://algs4.cs.princeton.edu/cheatsheet/)
* [CLRS in short](https://sinon.org/algorithms/#data-structures)
* [Rice university DS course in short](https://www.clear.rice.edu/comp160/data1.html)
* [Useful Reddit thread](https://www.reddit.com/r/learnprogramming/comments/3gpvyx/algorithms_and_data_structures_cheat_sheets/)
* [Algo Deck](https://github.com/teivah/algodeck/) - An open-source collection of +200 algorithmic cards.

### Github Libraries

*Implementations of the most classic algorithms in a wide variety of programming languages*

* C
  * [by @fragglet](https://github.com/fragglet/c-algorithms)
  * [by @TheAlgorithms](https://github.com/TheAlgorithms/C)
* [CoffeeScript](https://github.com/BrunoRB/algorithms.coffee)
* C#
  * [by @shkolovy](https://github.com/shkolovy/classic-algorithms)
  * [by @aalhour](https://github.com/aalhour/C-Sharp-Algorithms)
  * [by @justcoding121](https://github.com/justcoding121/Advanced-Algorithms)
* C++
  * [by @xtaci](https://github.com/xtaci/algorithms)
  * [by @PetarV-](https://github.com/PetarV-/Algorithms)
  * [by @faheel](https://github.com/faheel/Algos)
  * [by @sslotin](http://github.com/sslotin/algo)
* [Erlang](https://github.com/aggelgian/erlang-algorithms)
* Go
  * [by @arnauddri](https://github.com/arnauddri/algorithms)
  * [by @floyernick](https://github.com/floyernick/Data-Structures-and-Algorithms)
* Java
  * [by @jpa99](https://github.com/jpa99/Algorithms)
  * [by @phishman3579](https://github.com/phishman3579/java-algorithms-implementation)
  * [by @asmolich](https://github.com/asmolich/algorithms)
  * [by @psjava](https://github.com/psjava/psjava)
  * [by @jeandersonbc](https://github.com/jeandersonbc/algorithms-and-ds)
  * [by @pedrovgs](https://github.com/pedrovgs/Algorithms)
  * [by @Erdos-Graph-Framework](https://github.com/Erdos-Graph-Framework/Erdos)
  * [by @deepak-malik](https://github.com/deepak-malik/Data-Structures-In-Java)
  * [by @yusufcakal](https://github.com/yusufcakal/algorithms)
  * [by @FarheenB](https://github.com/FarheenB/Data-Structures-and-Algorithms)
* JavaScript
  * [by @jiayihu](https://github.com/jiayihu/pretty-algorithms)
  * [by @felipernb](https://github.com/felipernb/algorithms.js)
  * [by @nzakas](https://github.com/nzakas/computer-science-in-javascript)
  * [by @duereg](https://github.com/duereg/js-algorithms)
  * [by @mgechev](https://github.com/mgechev/javascript-algorithms)
  * [by @trekhleb](https://github.com/trekhleb/javascript-algorithms)
  * [by @ManrajGrover](https://github.com/ManrajGrover/algorithms-js)
  * [by @amejiarosario](https://github.com/amejiarosario/dsa.js)
  * [by @zonayedpca](https://github.com/zonayedpca/AlgoDS.js)
* Lua
  * [by @evandrolg](https://github.com/EvandroLG/computer_science_in_lua)
* Objective-C
  * [by @ EvgenyKarkan](https://github.com/EvgenyKarkan/EKAlgorithms)
* PHP
  * [by @TheAlgorithms](https://github.com/TheAlgorithms/PHP)
* Python
  * [by @nryoung](https://github.com/nryoung/algorithms)
  * [by @prakhar1989](https://github.com/prakhar1989/Algorithms)
  * [by @laurentluce](https://github.com/laurentluce/python-algorithms)
  * [by @nbro](https://github.com/nbro/ands)
  * [by @keon](https://github.com/keon/algorithms)
  * [by @vinta](https://github.com/vinta/fuck-coding-interviews)
* Ruby
  * [by @kanwei](https://github.com/kanwei/algorithms)
  * [by @sagivo](https://github.com/sagivo/algorithms)
  * [by @kumar91gopi](https://github.com/kumar91gopi/Algorithms-and-Data-Structures-in-Ruby)
* [Scala](https://github.com/vkostyukov/scalacaster)
* Swift
  * [by @kingreza](https://github.com/kingreza/Swift-Algorithms-Strings-)
  * [by @waynewbishop](https://github.com/waynewbishop/SwiftStructures)
  * [by @hollance](https://github.com/hollance/swift-algorithm-club)
* Language agnostic
  * [by @kennyledet](https://github.com/kennyledet/Algorithm-Implementations)
  * [by @indy256](https://github.com/indy256/codelibrary)
  * [by @sagivo](https://github.com/sagivo/algorithms)
  * [by @patmorin](https://github.com/patmorin/ods)
  * [by @btjanaka](https://github.com/btjanaka/algorithm-problems)

### Online Judges

*Online Judges to practice what you learned above*

* [A2 Online Judge](https://a2oj.com) - Online Judge and problem archive.
* [ACM-ICPC Live Archive](https://icpcarchive.ecs.baylor.edu) - Hundreds of problems from previous ACM-ICPC Regionals and World Finals.
* [AIZU ONLINE JUDGE](http://judge.u-aizu.ac.jp/onlinejudge/) - Japanese Online Judge.
* [Algo Muse](http://www.algomuse.appspot.com) - Research based algorithmic problems.
* [AtCoder](https://atcoder.jp) - Japanese programming contest website.
* [Baekjoon Online Judge](https://www.acmicpc.net) - Korean Online Judge. 10000+ problems. Supports 60+ languages.
* [CS Academy](https://csacademy.com) - Holds online contests and IOI practice contests
* [CodeChef](https://www.codechef.com) - More problems and monthly online contests.
* [Codeforces ](http://codeforces.com)- The only programming contests Web 2.0 platform
* [Codefights](https://codefights.com) - Practive programming and tackle out your next tech interview
* [CodeMarshal](https://algo.codemarshal.org) - Real world contests online!
* [CodeWars](http://www.codewars.com) - A website that houses support to solve algorithms in many languages in varying difficulty.
* [CoderByte](http://www.coderbyte.com) - A decent website with algorithm challenges from beginner to advanced levels. Supports most of the popular languages like C++, python, javascript, ruby.
* [Firecode](https://www.firecode.io) - Firecode.io uses machine learning algorithms along with curated real-world interview questions, solutions & a vibrant social community of learners to get you ready for your next coding interview.
* [Coding Blocks](https://hack.codingblocks.com/app/) - Website that have problems based on Maths, Data Structures, Various Algorithm and also conducts Coding Competition.
* [HackerEarth ](https://www.hackerearth.com)- Practice alogrithmic problems & challenges and participate in hiring challenges.
* [HackerRank](https://www.hackerrank.com) - Featured algorithm and functional programming online judges
* [HiHoCoder](http://hihocoder.com) - Chinese and English problem solving practice and recruitment challenge site.
* [Infoarena](http://www.infoarena.ro) - Romanian Online Judge. 1500+ algorithmic problems
* [Interviewbit](https://www.interviewbit.com) - Learn, practice and prepare for interviews.
* [Kattis](https://open.kattis.com)- Online judge and problem archive
* [LavidaOnlineJudge](http://judge.lavida.us) - Korean Online Judge(Half English). 1300+ problems.
* [Learneroo Algorithms Tutorials](https://www.learneroo.com/subjects/8) - Learn and practice algorithms by solving challenges online.
* [LeetCode](https://leetcode.com) - Learn algorithms and prepare for interviews.
* [PKU JudgeOnline](http://poj.org) - Chinese Online Judge.
* [ProjectEuler](https://projecteuler.net) - Mathematical problems that can be solved using algorithms (or just a pencil, depends on how much you already know).
* [Rosalind](http://rosalind.info/problems/locations/) - A platform for learning bioinformatics and programming through problem solving.
* [ShareCode.io ](https://sharecode.io)- Online Judge and contest host with a lot of algorithmic problems in the archive to practice.
* [Snakify](https://snakify.org) - An introductory Python course with 100+ algorithmic problems and a step-by-step debugger (from Russia).
* [SPOJ](http://www.spoj.com) - More problems.
* [TopCoder](https://www.topcoder.com) - Lots of problems and real world/money worthy problems in Graphic Design, Data Science and Development.
* [Toph](https://toph.co) - Bangladeshi Online Judge. Holds online contests on a regular basis.
* [URI](https://www.urionlinejudge.com.br/judge/login) - Brazilian Online Judge. Not so much problems, but it's growing and it has online contests.
* [UVA](https://uva.onlinejudge.org) - Hundreds of problems (from previous ACM-ICPC Regionals, World Finals and others).
* [Codility](https://app.codility.com/programmers/challenges/) - Compete to land coding jobs at top companies

### Blogs

*Awesome list of blogs, mainly for competitive programming but you can refer to these when learning a new topic/algorithm*

* [An awesome list for competitive programming!](https://codeforces.com/blog/entry/23054) - Awesome blog for all the resources and list of books and algorithms.
* [Algorithms Weekly](https://petr-mitrichev.blogspot.com) - A good blog by Petr Mitrichev, mainly in Java.
* [Sport of Programming](https://www.hackerearth.com/practice/notes/getting-started-with-the-sport-of-programming/) - Really informative blog for starting with the sport of programming.
* [Algorithms and Data Structures](http://www.allisons.org/ll/AlgDS/) - For getting deeper knowledge of algorithms and how to think in right direction.
* [Algorithm Tutorials by Tanuj Khattar](https://tanujkhattar.wordpress.com) - Excellent blog by Tanuj Khattar. Covers tutorials on some interesting data structures along with example problems to solve.

### Tools

*Some tools that can help you in the learning of algorithms*

* [interactive-coding-challenges](https://github.com/donnemartin/interactive-coding-challenges) - Interactive, test-driven coding challenges (algorithms and data structures).

### Related Awesome Lists

* [Math](https://github.com/rossant/awesome-math#readme) - Freely available lecture notes on mathematics.
* [Theoretical Computer Science](https://github.com/mostafatouny/awesome-theoretical-computer-science/blob/main/README.md) - The interdisciplinary of Mathematics and Computer Science, Distinguished by its emphasis on mathemtical technique and rigour.