![Open Source Society University (OSSU)](http://i.imgur.com/GjEbgIJ.png)
Path to a self-taught education in Computer Science!
# Contents - [About](#about) - [Motivation & Preparation](#motivation--preparation) - [Curriculum](#curriculum) - [How to use this guide](#how-to-use-this-guide) - [Prerequisites](#prerequisites) - [Changelog](#changelog) - [How to collaborate](#how-to-collaborate) - [Community](#community) - [Team](#team) - [References](#references) # About This is a **solid path** for those of you who want to complete a **Computer Science** curriculum on your own time, at **little to no cost**, with courses from the **best universities** in the world. In our curriculum, we give preference to MOOC (Massive Open Online Course) style courses because these courses were created with our style of learning in mind. The curriculum then concludes with a **final project** to show off your skills to your friends and future employers. # Motivation & Preparation Here are two interesting links that can make all the difference in your journey. The first one is a motivational video that shows a guy that went through the "MIT Challenge", which consists of learning the entire **4-year** MIT curriculum for Computer Science in **1 year**. - [MIT Challenge](https://www.scotthyoung.com/blog/myprojects/mit-challenge-2/) The second link is a MOOC that will teach you learning techniques used by experts in art, music, literature, math, science, sports, and many other disciplines. These are **fundamental abilities** to succeed in our journey. - [Learning How to Learn](https://www.coursera.org/learn/learning-how-to-learn) **Are you ready to get started?** # Curriculum - [Intro CS](#intro-cs) - [Core CS](#core-cs) - [Core programming](#core-programming) - [Core math](#core-math) - [Core systems](#core-systems) - [Core theory](#core-theory) - [Core applications](#core-applications) - [Advanced CS](#advanced-cs) - [Advanced programming](#advanced-programming) - [Advanced math](#advanced-math) - [Advanced systems](#advanced-systems) - [Advanced theory](#advanced-theory) - [Advanced applications](#advanced-applications) - [Electives](#electives) - [Pro CS](#pro-cs) - [Final project](#final-project) See also: [Prerequisites](#prerequisites) --- ## Intro CS Use this course to figure out if Computer Science is right for you. Only proceed in the curriculum if it really excites you. **Topics covered**: imperative programming; procedural programming; C; basic data structures and algorithms; basic Python; SQL; basic HTML, CSS, JavaScript; Courses | Duration | Effort | Prerequisites :-- | :--: | :--: | :--: [Introduction to Computer Science - CS50](https://www.edx.org/course/introduction-computer-science-harvardx-cs50x#!) | 12 weeks | 10-20 hours/week | none ## Core CS ### Core programming **Topics covered**: basic testing; functional program composition; object-oriented program design; static typing; dynamic typing; common design patterns; ML-family languages (via Standard ML); Lisp-family languages (via Racket); Ruby; and more. Courses | Duration | Effort | Prerequisites :-- | :--: | :--: | :--: [How to Code: Systematic Program Design (XSeries)](https://www.edx.org/xseries/how-code-systematic-program-design) | 15 weeks | 5 hours/week | none [Object Oriented Programming in Java](https://www.coursera.org/learn/object-oriented-java) | 6 weeks | 4-6 hours/week | some programming [Programming Languages, Part A](https://www.coursera.org/learn/programming-languages) | 4 weeks | 8-16 hours/week | recommended: Java, C [Programming Languages, Part B](https://www.coursera.org/learn/programming-languages-part-b) | 3 weeks | 8-16 hours/week | Programming Languages, Part A [Programming Languages, Part C](https://www.coursera.org/learn/programming-languages-part-c) | 3 weeks | 8-16 hours/week | Programming Languages, Part B **Note**: The Object-Oriented Programming in Java class is intended for students who have already taken a basic Java course, but it can still be completed by those who have only studied basic programming before in a different, Java-like language (e.g., C). The learning curve will be steep, however, so for those who find it too difficult, looking over the material in this course is recommended: [Introduction to Programming in Java](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-092-introduction-to-programming-in-java-january-iap-2010/index.htm). ### Core math **Topics covered**: mathematical proofs; number theory; real analysis; differential calculus; integral calculus; sequences and series; probability theory; basic statistics; O-notation; graph theory; linear transformations; matrices; vectors; and more. Courses | Duration | Effort | Prerequisites :-- | :--: | :--: | :--: [Introduction to Mathematical Thinking](https://www.coursera.org/learn/mathematical-thinking) | 10 weeks | 10 hours/week | high school math [Linear Algebra - Foundations to Frontiers](https://www.edx.org/course/linear-algebra-foundations-frontiers-utaustinx-ut-5-04x#!)| 15 weeks | 8 hours/week | high school math [Calculus One](https://www.coursera.org/learn/calculus1)| 16 weeks | 8-10 hours/week | pre-calculus [Calculus Two: Sequences and Series](https://www.coursera.org/learn/advanced-calculus)| 7 weeks | 9-10 hours/week | Calculus One [Mathematics for Computer Science](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-spring-2015/index.htm) | 13 weeks | 5 hours/week | Calculus Two [Introduction to Probability - The Science of Uncertainty](https://www.edx.org/course/introduction-probability-science-mitx-6-041x-2) | 18 weeks | 12 hours/week | calculus ### Core systems **Topics covered**: boolean algebra; gate logic; memory; machine language; computer architecture; assembly; machine language; virtual machines; high-level languages; compilers; operating systems; relational databases; transaction processing; data modeling; network protocols; and more. Courses | Duration | Effort | Prerequisites :-- | :--: | :--: | :--: [Build a Modern Computer from First Principles: From Nand to Tetris](https://www.coursera.org/learn/build-a-computer) | 6 weeks | 7-13 hours/week | none [Build a Modern Computer from First Principles: Nand to Tetris Part II ](https://www.coursera.org/learn/nand2tetris2) | 6 weeks | 12-18 hours/week | Part I [Databases](https://lagunita.stanford.edu/courses/DB/2014/SelfPaced/about)| 12 weeks | 8-12 hours/week | some programming, basic CS [Introduction to Computer Networking](https://lagunita.stanford.edu/courses/Engineering/Networking-SP/SelfPaced/about)| - | 4–12 hours/week | algebra, probability, basic CS **Note 1**: The 'From Nand to Tetris' course, in part I, will have you create an entire computer architecture from scratch, but is missing key elements from computer architecture such as pipelining and memory hierarchy. A supplemental textbook is recommended for those who wish to go deeper into the hardware: [Computer Organization and Design](https://smile.amazon.com/Computer-Organization-Design-Fifth-Architecture/dp/0124077269). **Note 2**: Part II of the same course has you build the very lowest levels of an operating system on top of the computer architecture you built, however it does not go very deep into operating systems. For those interested in this subject, this free supplemental textbook is strongly recommended: [Operating Systems: Three Easy Pieces](http://pages.cs.wisc.edu/~remzi/OSTEP/). Both of the above textbooks should be considered a requirement for anyone who intends to become a *[systems programmer](https://en.wikipedia.org/wiki/System_programming)*. ### Core theory **Topics covered**: divide and conquer; sorting and searching; randomized algorithms; graph search; shortest paths; data structures; greedy algorithms; minimum spanning trees; dynamic programming; NP-completeness; formal languages; Turing machines; computability; and more. Courses | Duration | Effort | Prerequisites :-- | :--: | :--: | :--: [Algorithms (1/4)](https://www.coursera.org/learn/algorithms-divide-conquer) | 4 weeks | 4-8 hours/week | one programming language; proofs; probability [Algorithms (2/4)](https://www.coursera.org/learn/algorithms-graphs-data-structures) | 4 weeks | 4-8 hours/week | previous algorithms course [Algorithms (3/4)](https://www.coursera.org/learn/algorithms-greedy) | 4 weeks | 4-8 hours/week | previous algorithms course [Algorithms (4/4)](https://www.coursera.org/learn/algorithms-npcomplete) | 4 weeks | 4-8 hours/week | previous algorithms course [Automata Theory](https://lagunita.stanford.edu/courses/course-v1:ComputerScience+Automata+Fall2016/about) | 8 weeks | 10 hours/week | discrete mathematics ### Core applications **Topics covered**: neural networks; supervised learning; unsupervised learning; OpenGL; raytracing; block ciphers; authentication; public key encryption; and more. Courses | Duration | Effort | Prerequisites :-- | :--: | :--: | :--: [Machine Learning](https://www.coursera.org/learn/machine-learning)| 11 weeks | 4-6 hours/week | linear algebra [Computer Graphics](https://www.edx.org/course/computer-graphics-uc-san-diegox-cse167x)| 6 weeks | 12 hours/week | C++ or Java, linear algebra [Cryptography I](https://www.coursera.org/course/crypto)| 6 weeks | 5-7 hours/week | linear algebra; probability ## Advanced CS ### Advanced programming **Topics covered**: code coverage; random testing; debugging theory and practice; GPU programming; CUDA; parallel computing; object-oriented analysis and design; UML; large-scale software architecture and design; and more. Courses | Duration | Effort | Prerequisites :-- | :--: | :--: | :--: [Software Testing](https://www.udacity.com/course/software-testing--cs258)| 4 weeks | 6 hours/week | some programming [Software Debugging](https://www.udacity.com/course/software-debugging--cs259)| 8 weeks | 6 hours/week | Python, object-oriented programming [Introduction to Parallel Programming](https://www.udacity.com/course/intro-to-parallel-programming--cs344) | 12 weeks | - | C, algorithms [Software Architecture & Design](https://www.udacity.com/course/software-architecture-design--ud821)| 8 weeks | 6 hours/week | Java programming ### Advanced math ### Advanced systems ### Advanced theory ### Advanced applications ## Electives Some of these courses are offered less frequently, but you are encouraged to take them whenever they are available if you're interested. - Compilers is recommended to any student who took a strong interest in the Programming Languages courses. - Natural Language Processing is recommended to anyone who thinks they want to specialize in machine learning, artificial intelligence, etc. - Cryptography is recommended to anyone who wants to learn more about zero knowledge systems and other advanced topics in cryptography. - The Princeton Algorithms courses are highly recommended as a more practical, implementation-focused complement to the Stanford Algorithms courses recommended as part of Core CS. However, Part II is rarely available, so they are electives at this time. Note that the assignments are in Java, but don't require extensive Java experience. Courses | Duration | Effort | Prerequisites :-- | :--: | :--: | :--: [Cryptography II](https://www.coursera.org/course/crypto2)| 6 weeks | 6-8 hours/week | Cryptography I [Compilers](https://lagunita.stanford.edu/courses/Engineering/Compilers/Fall2014/about)| 9 weeks | 6-8 hours/week | none [Introduction to Natural Language Processing](https://www.coursera.org/learn/natural-language-processing)| 12 weeks | - | Python programming [Algorithms, Part I](https://www.coursera.org/learn/algorithms-part1) | 6 weeks | 6-12 hours/week | some programming [Algorithms, Part II](https://www.coursera.org/learn/algorithms-part2) | 6 weeks | 6-12 hours/week | Algorithms, Part I ## Pro CS After finishing the curriculum above, you will have completed close to a full bachelor's degree in Computer Science. You can stop here, but if you really want to make yourself valuable, the next step to completing your studies is to develop skills and knowledge in a specific domain. Choose one or more of the following specializations: - [Artificial Intelligence Engineer Nanodegree](https://www.udacity.com/ai) by IBM, Amazon, and Didi - [Data Mining Specialization](https://www.coursera.org/specializations/data-mining) by the University of Illinois at Urbana-Champaign - [Big Data Specialization](https://www.coursera.org/specializations/big-data) by the University of California at San Diego - [Data Analyst Nanodegree](https://www.udacity.com/course/data-analyst-nanodegree--nd002) by Facebook and mongoDB - [Applied Data Science with Python Specialization](https://www.coursera.org/specializations/data-science-python) by the University of Michigan - [Data Science Specialization](https://www.coursera.org/specializations/jhu-data-science) by Johns Hopkins University - [Mastering Software Development in R Specialization](https://www.coursera.org/specializations/r) by Johns Hopkins University - [Machine Learning Engineer Nanodegree](https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009) by kaggle - [Cybersecurity MicroMasters](https://www.edx.org/micromasters/ritx-cybersecurity) by the Rochester Institute of Technology - [Cloud Computing Specialization](https://www.coursera.org/specializations/cloud-computing) by the University of Illinois at Urbana-Champaign - [Internet of Things Specialization](https://www.coursera.org/specializations/internet-of-things) by the University of California at San Diego - [Full Stack Web Development Specialization](https://www.coursera.org/specializations/full-stack) by the Hong Kong University of Science and Technology - [Android Developer Nanodegree](https://www.udacity.com/course/android-developer-nanodegree-by-google--nd801) by Google These aren't the only specializations you can choose. Check the following websites for more options: ### edX: [xSeries](https://www.edx.org/xseries) ### Coursera: [Specializations](https://www.coursera.org/specializations) ### Udacity: [Nanodegree](https://www.udacity.com/nanodegree) ### FutureLearn: [Collections](https://www.futurelearn.com/courses/collections) ## Final project **OSS University** is **project-focused**. You are encouraged to do the assignments and exams for each course, but what really matters is whether you can *use* your knowledge to solve a real world problem. > "What does it mean?" After you finish the curriculum, you should think about a problem that you can solve using the knowledge you've acquired. Not only does real project work look great on a resume, the project will **validate** and **consolidate** your knowledge. The final projects of all students will be listed in [this](PROJECTS.md) file. **Submit your project's information in that file after you conclude it**. Put the OSSU-CS badge in the README of your repository! [![Open Source Society University - Computer Science](https://img.shields.io/badge/OSSU-computer--science-blue.svg)](https://github.com/open-source-society/computer-science) - Markdown: `[![Open Source Society University - Computer Science](https://img.shields.io/badge/OSSU-computer--science-blue.svg)](https://github.com/open-source-society/computer-science)` - HTML: `