![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; but we include readings where appropriate. 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) - [Final project](#final-project) - [Pro CS](#pro-cs) 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**: functional programming; design for testing; program requirements; common design patterns; unit testing; object-oriented design; Java; static typing; dynamic typing; ML-family languages (via Standard ML); Lisp-family languages (via Racket); Ruby; and more. Courses | Duration | Effort | Prerequisites :-- | :--: | :--: | :--: Programming Languages ([A](https://www.coursera.org/learn/programming-languages), [B](https://www.coursera.org/learn/programming-languages-part-b), [C](https://www.coursera.org/learn/programming-languages-part-c)) | 10 weeks | 8-16 hours/week | introductory programming [How to Code - Simple Data](https://www.edx.org/course/how-code-simple-data-ubcx-htc1x) | 7 weeks | 8-10 hours/week | none [How to Code - Complex Data](https://www.edx.org/course/how-code-complex-data-ubcx-htc2x) | 6 weeks | 8-10 hours/week | How to Code: Simple Data [Software Construction - Data Abstraction](https://www.edx.org/course/software-construction-data-abstraction-ubcx-softconst1x) | 6 weeks | 8-10 hours/week | How to Code - Complex Data [Software Construction - Object-Oriented Design](https://www.edx.org/course/software-construction-object-oriented-ubcx-softconst2x) | 6 weeks | 8-10 hours/week | Software Construction - Data Abstraction #### Readings - **Required** to learn about monads, laziness, purity: [Learn You a Haskell for a Great Good!](http://learnyouahaskell.com/) - **Required**, to learn about logic programming, backtracking, unification, any resource on Prolog covering these topics, such as: - [Prolog Programming for Artificial Intelligence](https://www.amazon.com/Programming-Artificial-Intelligence-International-Computer/dp/0321417461) - [Learn Prolog Now](http://www.learnprolognow.org/) - [Art of Prolog](https://mitpress.mit.edu/books/art-prolog) ### Core math **Topics covered**: linear transformations; matrices; vectors; mathematical proofs; number theory; differential calculus; integral calculus; sequences and series; discrete mathematics; basic statistics; O-notation; graph theory; probability theory; 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 (pre-calculus) [Calculus One](https://www.coursera.org/learn/calculus1)| 16 weeks | 8-10 hours/week | high school math (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 #### Readings - **Required** since Nand2Tetris does not go deep enough into operating systems: [Operating Systems: Three Easy Pieces](http://pages.cs.wisc.edu/~remzi/OSTEP/) - Supplemental: [Computer Networking: A Top-Down Approach](https://www.amazon.com/gp/product/0133594149?pldnSite=1) ### Core theory Algorithms and data structures is the most important subject you will learn in Core CS, and there are two major course sequences for learning it. 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**: Agile methodology; REST; software specifications; refactoring; 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 [Software Engineering: Introduction](https://www.edx.org/course/software-engineering-introduction-ubcx-softeng1x) | 6 weeks | 8-10 hours/week | Software Construction - Object-Oriented Design [Software Development Capstone Project](https://www.edx.org/course/software-development-capstone-project-ubcx-softengprjx)**\*** | 6-7 weeks | 8-10 hours/week | Software Engineering: Introduction \* **Required** if you intend not to go past Core CS; **strongly recommended** even for those who will continue on to Advanced CS. #### Readings - Supplemental: [Transaction Processing: Concepts and Techniques](https://www.amazon.com/Transaction-Processing-Concepts-Techniques-Management/dp/1558601902) - Supplemental: [Data and Reality: A Timeless Perspective on Perceiving and Managing Information in Our Imprecise World](https://www.amazon.com/Data-Reality-Perspective-Perceiving-Information/dp/1935504215) - Supplemental: [Architecture of a Database System](http://db.cs.berkeley.edu/papers/fntdb07-architecture.pdf) ## Advanced CS After completing **every single course** in Core CS, students should choose a subset of courses from Advanced CS based on interest. Not every course from a subcategory needs to be taken. But students should take *every* course that is relevant to the field they intend to go into. 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**: 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 :-- | :--: | :--: | :--: [Compilers](https://lagunita.stanford.edu/courses/Engineering/Compilers/Fall2014/about)| 9 weeks | 6-8 hours/week | none [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 #### Readings - Recommended: [Design Patterns](https://www.amazon.com/Design-Patterns-Elements-Reusable-Object-Oriented/dp/0201633612/ref=sr_1_1?s=books&ie=UTF8&qid=1488071249&sr=1-1&keywords=Design+Patterns) - Recommended: [Refactoring](https://www.refactoring.com/) - Recommended: [The Architecture of Open Source Applications](http://aosabook.org/en/index.html) ### 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) #### Readings - Supplemental: [Modern Operating Systems](https://www.amazon.com/Modern-Operating-Systems-Andrew-Tanenbaum/dp/013359162X) ### 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 [Introduction to Logic](https://www.coursera.org/learn/logic-introduction) | 10 weeks | 4-8 hours/week | set theory [Automata Theory](https://lagunita.stanford.edu/courses/course-v1:ComputerScience+Automata+Fall2016/about) | 8 weeks | 10 hours/week | discrete mathematics, logic, algorithms [Computational Geometry](https://www.edx.org/course/computational-geometry-tsinghuax-70240183x) | 16 weeks | 8 hours/week | algorithms, C++ [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 ## 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. After you've gotten through all of Core CS and the parts of Advanced CS relevant to you, 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. Another option is using the Capstone project from taking one of the Specializations in [Advanced applications](#advanced-applications); whether or not this makes sense depends on the course, the project, and whether or not the course's Honor Code permits you to display your work publicly. In some cases, it may not be permitted; do **not** violate your course's Honor Code! 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: `