awesome-awesomeness/terminal/algorithms3

                                                                                      Awesome Algorithms

A curated list of awesome places to learn and/or practice algorithms.
Inspired by awesome-awesomeness (https://github.com/bayandin/awesome-awesomeness) and all the other awesome Awesome libraries.

If you want to contribute, please read the contribution guidelines (https://github.com/tayllan/awesome-algorithms/blob/master/CONTRIBUTING.md).

- Awesome Algorithms (#awesome-algorithms)
- **Websites** (#websites)                  
- **YouTube Playlists** (#youtube-playlists)
- **Online Courses** (#online-courses)      
- **Books** (#books)                        
- **Cheat Sheets** (#cheat-sheets)          
- **Github Libraries** (#github-libraries)  
- **Online Judges** (#online-judges)        
- **Tools** (#tools)                        

Websites

Websites you should use to learn classic algorithms

⟡ A Visual Guide to Graph Traversal Algorithms (https://workshape.github.io/visual-graph-algorithms/) - Interactive visualizations 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 are especially popular in the 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 of 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 that 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 structures and algorithms in Javascript.
⟡ redblobgames (https://www.redblobgames.com/) - interactive visual explanations of math and algorithms, using motivating examples from computer games.

Youtube Playlists

High Quality Courses and tutorials on youtube

⟡ FreeCodeCamp - Algorithms and Data Structures Tutorial - Full Course for Beginners
 (https://www.youtube.com/watch?v=8hly31xKli0) - Complete beginner friendly Algorithms and Data Structures Tutorial with mindblowing animation.

⟡ Abdul Bari - Introduction to Algorithm (https://www.youtube.com/watch?v=0IAPZzGSbME&list=PLDN4rrl48XKpZkf03iYFl-O29szjTrs_O) - This playlist is so much informative and provide simulation 
with code example. As like as you are in a class.

⟡ Jenny's Lectures- Algorithm
 (https://www.youtube.com/watch?v=AT14lCXuMKI&list=PLdo5W4Nhv31bbKJzrsKfMpo_grxuLl8LU) - Another complete alogrithm playlist from basic level to intermediate. Easy explanation and simulation.

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 ministered by Tomas Cormen and Devin Balkcom.
⟡ MIT - 6-006 (https://ocw.mit.edu/courses/6-006-introduction-to-algorithms-fall-2011/video_galleries/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.
⟡ MIT 18-409 - Topics in Theoretical Computer Science: An Algorithmist's Toolkit 
(https://ocw.mit.edu/courses/18-409-topics-in-theoretical-computer-science-an-algorithmists-toolkit-fall-2009/) - It covers a collection of geometric techniques that apply broadly in modern 
algorithm design.
⟡ 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 videos 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 straightforward.
⟡ 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 the forms of puzzles. No programming experience is required!
⟡ Standford CS166 (https://web.stanford.edu/class/cs166/) - Standford CS166, a course in the design, analysis, and implementation of data structures.

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
⟡ Tech Interview Cheat Sheet (https://github.com/TSiege/Tech-Interview-Cheat-Sheet)
⟡ 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/) - Practice programming and tackle 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 has problems based on Maths, Data Structures, Various Algorithm and also conducts Coding Competition.
⟡ HackerEarth  (https://www.hackerearth.com/) - Practice algorithmic 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, depending 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 regularly.
⟡ URI (https://www.urionlinejudge.com.br/judge/login) - Brazilian Online Judge. Not so many 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/) - Informative blog for starting with the sport of programming.
⟡ Algorithms and Data Structures (http://www.allisons.org/ll/AlgDS/) - For getting a deeper knowledge of algorithms and how to think in the 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 mathematical technique and rigour.


License

And for the sake of copyleft, here's our license:

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This work is licensed under a Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).