![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 is an interesting link that can make all the difference in your journey. It's 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/) **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) - [Pro CS](#pro-cs) - [Final project](#final-project) See also: [Prerequisites](#prerequisites) --- ## Intro CS Use the first course, CS50, to determine if Computer Science is right for you. Only proceed in the curriculum if it really excites you. If it does, use the second and third courses to gain the fundamental skills you need to excel at teaching yourself Computer Science. **Topics covered**: imperative programming; procedural programming; C; basic data structures and algorithms; basic Python; SQL; basic HTML, CSS, JavaScript; learning skills; cardinality; and more. 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 [Learning How to Learn](https://www.coursera.org/learn/learning-how-to-learn) | 4 weeks | 2 hours/week | none [Effective Thinking Through Mathematics](https://www.edx.org/course/effective-thinking-through-mathematics-utaustinx-ut-9-01x-0) | 4 weeks | 2 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; 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 :-- | :--: | :--: | :--: [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; 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 [Introduction to Computer Networking](https://lagunita.stanford.edu/courses/Engineering/Networking-SP/SelfPaced/about)| - | 4–12 hours/week | algebra, probability, basic CS **Note**: 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/). ### Core theory The Princeton Algorithms courses are highly recommended as a more practical, implementation-focused complement to the more theory-focused Stanford Algorithms courses. Ideally, students would do both sets of courses since they complement each other nicely. However, Part II of Princeton Algorithms is rarely available, so Stanford Algorithms is the recommended choice if you cannot do both. Another difference is that Stanford Algorithms assignments can use any programming language; Princeton Algorithms assignments use Java but don't require extensive Java experience. **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; and more. #### Stanford Algorithms 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 #### Princeton Algorithms Courses | Duration | Effort | Prerequisites :-- | :--: | :--: | :--: [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 ### Core applications **Topics covered**: relational databases; transaction processing; data modeling; neural networks; supervised learning; unsupervised learning; OpenGL; raytracing; block ciphers; authentication; public key encryption; and more. Courses | Duration | Effort | Prerequisites :-- | :--: | :--: | :--: [Databases](https://lagunita.stanford.edu/courses/DB/2014/SelfPaced/about)| 12 weeks | 8-12 hours/week | some programming, basic CS [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 #### Optional 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 II is recommended to anyone who wants to learn more about zero knowledge systems and other advanced topics in cryptography. Unfortunately, the latter two courses are rarely available. Courses | Duration | Effort | Prerequisites :-- | :--: | :--: | :--: [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 [Cryptography II](https://www.coursera.org/course/crypto2)| 6 weeks | 6-8 hours/week | Cryptography I ## Advanced CS Unfortunately, advanced topics in computer science generally have less coverage in online courses. (This is because seldom few make it past beginner-level courses, there is low demand.) Therefore, some of these courses may not be available regularly. After completing every **single course** in Core CS, students should choose a subset of courses from Advanced CS based on interest. The Advanced CS study should then end with one of the Specializations under [Advanced applications](#advanced-applications). A Specialization's Capstone, if taken, may act as the [Final project](#final-project), if permitted by the Honor Code of the course. If not, or if a student chooses not to take the Capstone, then a separate Final project will need to be done to complete this curriculum. ### Advanced programming **Topics covered**: code coverage; random testing; debugging theory and practice; goal-oriented programming; 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 [LAFF: Programming for Correctness](https://www.edx.org/course/laff-programming-correctness-utaustinx-ut-p4c-14-01x) | 7 weeks | 6 hours/week | linear algebra [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 systems Courses | Duration | Effort | Prerequisites :-- | :--: | :--: | :--: [Electricity and Magnetism, Part 1](https://www.edx.org/course/electricity-magnetism-part-1-ricex-phys102-1x-0) | 7 weeks | 8-10 hours/week | calculus, basic mechanics [Electricity and Magnetism, Part 2](https://www.edx.org/course/electricity-magnetism-part-2-ricex-phys102-2x-0) | 7 weeks | 8-10 hours/week | Part 1 [Computation Structures 1: Digital Circuits](https://www.edx.org/course/computation-structures-part-1-digital-mitx-6-004-1x-0) | 10 weeks | 6 hours/week | electricity, magnetism [Computation Structures 2: Computer Architecture](https://www.edx.org/course/computation-structures-2-computer-mitx-6-004-2x) | 10 weeks | 6 hours/week | previous course [Computation Structures 3: Computer Organization](https://www.edx.org/course/computation-structures-3-computer-mitx-6-004-3x-0) | 10 weeks | 6 hours/week | previous course [ops-class.org - Hack the Kernel](https://www.ops-class.org/) | 15 weeks | 6 hours/week | algorithms **Note 1**: The Computation Structures courses assume prior knowledge of basic physics, mechanics in particular. The relevant material will be reviewed in the Rice University 'Electricity and Magnetism' course, but not systematically. If you are struggling with the Rice courses, you can find a physics MOOC or utilize the materials from Khan Academy: [Khan Academy - Physics](https://www.khanacademy.org/science/physics) **Note 2**: The Computation Structures courses are very, very long, and very hands-on. A less hands-on alternative is here (note that the rerequisite physics knowledge is still the same): [Computer Architecture](https://www.coursera.org/learn/comparch) **Note 3**: ops-class.org is very, very hands-on. A completely passive alternative, totally lacking assignments or exams, is here: [Operating Systems](https://www.youtube.com/view_play_list?p=-XXv-cvA_iBDyz-ba4yDskqMDY6A1w_c) ### Advanced theory **Topics covered**: real analysis; formal languages; Turing machines; computability; computational geometry theory; propositional logic; relational logic; Herbrand logic; concept lattices; game trees; 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 [Automata Theory](https://lagunita.stanford.edu/courses/course-v1:ComputerScience+Automata+Fall2016/about) | 8 weeks | 10 hours/week | discrete mathematics [Computational Geometry](https://www.edx.org/course/computational-geometry-tsinghuax-70240183x) | 16 weeks | 8 hours/week | algorithms, C++ [Introduction to Logic](https://www.coursera.org/learn/logic-introduction) | 10 weeks | 4-8 hours/week | set theory [Introduction to Formal Concept Analysis](https://www.coursera.org/learn/formal-concept-analysis) | 6 weeks | 4-6 hours/week | logic, probability [Game Theory](https://www.coursera.org/learn/game-theory-1) | 8 weeks | x hours/week | mathematical thinking, probability, calculus ### Advanced applications These Coursera Specializations all end with a Capstone project. Depending on the course, you may be able to utilize the Capstone as your Final Project for this Computer Science curriculum. Note that doing a Specialization with the Capstone at the end always costs money. So if you don't wish to spend money or use the Capstone as your Final, it may be possible to take the courses in the Specialization for free by manually searching for them, but not all allow this. Courses | Duration | Effort | Prerequisites :-- | :--: | :--: | :--: [Robotics (Specialization)](https://www.coursera.org/specializations/robotics) | 26 weeks | 2-5 hours/week | linear algebra, calculus, programming, probability [Data Mining (Specialization)](https://www.coursera.org/specializations/data-mining) | 30 weeks | 2-5 hours/week | machine learning [Big Data (Specialization)](https://www.coursera.org/specializations/big-data) | 30 weeks | 3-5 hours/week | none [Internet of Things (Specialization)](https://www.coursera.org/specializations/internet-of-things) | 30 weeks | 1-5 hours/week | strong programming [Cloud Computing (Specialization)](https://www.coursera.org/specializations/cloud-computing) | 30 weeks | 2-6 hours/week | C++ programming [Full Stack Web Development (Specialization)](https://www.coursera.org/specializations/full-stack) | 27 weeeks | 2-6 hours/week | programming, databases [Data Science (Specialization)](https://www.coursera.org/specializations/jhu-data-science) | 43 weeks | 1-6 hours/week | none ## Pro CS After completing the requirements of 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. Many of these courses are graduate-level. Choose one or more of the following specializations: - [Mastering Software Development in R Specialization](https://www.coursera.org/specializations/r) by Johns Hopkins University - [Artificial Intelligence Engineer Nanodegree](https://www.udacity.com/ai) by IBM, Amazon, and Didi - [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 - [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: `