Open Source Society University (OSSU)



The OSSU curriculum is a complete education in computer science using online materials. It's not merely for career training or professional development. It's for those who want a proper, well-rounded grounding in concepts fundamental to all computing disciplines, and for those who have the discipline, will, and (most importantly!) good habits to obtain this education largely on their own, but with support from a worldwide community of fellow learners.

It is designed according to the degree requirements of undergraduate computer science majors, minus general education (non-CS) requirements, as it is assumed most of the people following this curriculum are already educated outside the field of CS. The courses themselves are among the very best in the world, often coming from Harvard, Princeton, MIT, etc., but specifically chosen to meet the following criteria.

Courses must:

When no course meets the above criteria, the coursework is supplemented with a book. When there are courses or books that don't fit into the curriculum but are otherwise of high quality, they belong in extras/courses or extras/readings.

Organization. The curriculum is designed as follows:

Duration. It is possible to finish Core CS within about 2 years if you plan carefully and devote roughly 18-22 hours/week to your studies. Courses in Core CS should be taken linearly if possible, but since a perfectly linear progression is rarely possible, each class's prerequisites are specified so that you can design a logical but non-linear progression based on the class schedules and your own life plans.

Cost. All or nearly all course material prior to Pro CS is available for free. However, some courses may charge money for assignments/tests/projects to be graded. Note that Coursera offers financial aid. Decide how much or how little to spend based on your own time and budget; just remember that you can't purchase success!

Process. Students can work through the curriculum alone or in groups, in order or out of order.

Content policy. If you plan on showing off some of your coursework publicly, you must share only files that you are allowed to. Do NOT disrespect the code of conduct that you signed in the beginning of each course!

How to contribute. Please see CONTRIBUTING.

Getting help. Please check our Frequently Asked Questions, and if you cannot find the answer, file an issue or talk to our friendly community!


Curriculum version: 8.0.0 (see CHANGELOG)


Intro CS

Introduction to Programming

If you've never written a for-loop, or don't know what a string is in programming, start here. Choose one of the two course series below. Either one will give you an introduction to programming that assumes no prior knowledge.

Trying to decide between them?

Python for Everyone will introduce you to a popular language and will quickly move to practical programming tasks - using web APIs and databases. This will give you a taste of what many professional developers do.

Fundamentals of Computing will also start by introducing you to Python. It then moves on to give an introduction to academic Computer Science topics, like sorting and recursion. This will give you a taste of what the following courses will be like. (Students who complete Fundamentals of Computing can skip Intro to Computer Science and begin Core CS.)

Topics covered: simple programs simple data structures

Courses | Effort | Prerequisites :-- | :--: | :--: Python for Everyone | 58 hours | none Fundamentals of Computing | 138 hours | high school mathematics

Introduction to Computer Science

This course will introduce you to the world of computer science. Students who have been introduced to programming, either from the courses above or through study elsewhere, should take this course for a flavor of the material to come. If you finish the course wanting more, Computer Science is likely for you!

Topics covered: computation imperative programming basic data structures and algorithms and more

Courses | Duration | Effort | Prerequisites :-- | :--: | :--: | :--: Introduction to Computer Science and Programming using Python (alt) | 9 weeks | 15 hours/week | high school algebra

Core CS

All coursework under Core CS is required, unless otherwise indicated.

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 :-- | :--: | :--: | :--: How to Code - Simple Data | 7 weeks | 8-10 hours/week | none How to Code - Complex Data | 6 weeks | 8-10 hours/week | How to Code: Simple Data Software Construction - Data Abstraction | 6 weeks | 8-10 hours/week | How to Code - Complex Data Software Construction - Object-Oriented Design | 6 weeks | 8-10 hours/week | Software Construction - Data Abstraction Programming Languages, Part A | 4 weeks | 8-16 hours/week | recommended: Java, C Programming Languages, Part B | 3 weeks | 8-16 hours/week | Programming Languages, Part A Programming Languages, Part C | 3 weeks | 8-16 hours/week | Programming Languages, Part B


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 vector calculus discrete probability and more

Courses | Duration | Effort | Prerequisites :-- | :--: | :--: | :--: Essence of Linear Algebra | - | - | pre-calculus Linear Algebra - Foundations to Frontiers (alt) | 15 weeks | 8 hours/week | Essence of Linear Algebra Calculus 1A: Differentiation | 13 weeks | 6-10 hours/week | pre-calculus Calculus 1B: Integration | 13 weeks | 5-10 hours/week | Calculus 1A Calculus 1C: Coordinate Systems & Infinite Series | 13 weeks | 5-10 hours/week | Calculus 1B Mathematics for Computer Science1 | 13 weeks | 5 hours/week | Calculus 1C

1: Students struggling with MIT Math for CS can consider taking the Discrete Mathematics Specialization first. It is more interactive but less comprehensive, and it costs money to unlock full interactivity.

Core systems

Topics covered: procedural programming manual memory management boolean algebra gate logic memory computer architecture assembly machine language virtual machines high-level languages compilers operating systems network protocols and more

Courses | Duration | Effort | Additional Text / Assignments| Prerequisites :-- | :--: | :--: | :--: | :--: Introduction to Computer Science - CS50 (alt) | 12 weeks | 10-20 hours/week | After the sections on C, skip to the next course. Why? | introductory programming Build a Modern Computer from First Principles: From Nand to Tetris (alt) | 6 weeks | 7-13 hours/week | - | C-like programming language Build a Modern Computer from First Principles: Nand to Tetris Part II | 6 weeks | 12-18 hours/week | - | one of these programming languages, From Nand to Tetris Part I Introduction to Computer Networking| 8 weeks | 4–12 hours/week | Assignment 1Assignment 2Assignment 3Assignment 4 | algebra, probability, basic CS - Hack the Kernel | 15 weeks | 6 hours/week | Replace course textbook with Operating Systems: Three Easy Pieces | algorithms

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 and more

Courses | Duration | Effort | Prerequisites :-- | :--: | :--: | :--: Algorithms: Design and Analysis, Part I | 8 weeks | 4-8 hours/week | any programming language, Mathematics for Computer Science Algorithms: Design and Analysis, Part II | 8 weeks | 4-8 hours/week | 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 and more

Courses | Duration | Effort | Prerequisites :-- | :--: | :--: | :--: Databases| 12 weeks | 8-12 hours/week | some programming, basic CS Machine Learning| 11 weeks | 4-6 hours/week | linear algebra Computer Graphics| 6 weeks | 12 hours/week | C++ or Java, linear algebra Software Engineering: Introduction | 6 weeks | 8-10 hours/week | Software Construction - Object-Oriented Design Software Development Capstone Project | 6-7 weeks | 8-10 hours/week | Software Engineering: Introduction

Advanced CS

After completing every required 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. A Specialization's Capstone, if taken, may act as the 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| 9 weeks | 6-8 hours/week | none Software Debugging| 8 weeks | 6 hours/week | Python, object-oriented programming Software Testing | 4 weeks | 6 hours/week | Python, programming experience LAFF - On Programming for Correctness | 7 weeks | 6 hours/week | linear algebra Introduction to Parallel Programming (alt) | 12 weeks | - | C, algorithms Software Architecture & Design| 8 weeks | 6 hours/week | software engineering in Java

Advanced math

Topics covered: parametric equations polar coordinate systems multivariable integrals multivariable differentials probability theory and more

Courses | Duration | Effort | Prerequisites :-- | :--: | :--: | :--: Multivariable Calculus | 13 weeks | 12 hours/week | MIT Calculus 1C Introduction to Probability - The Science of Uncertainty | 18 weeks | 12 hours/week | Multivariable Calculus

Advanced systems

Topics covered: digital signaling combinational logic CMOS technologies sequential logic finite state machines processor instruction sets caches pipelining virtualization parallel processing virtual memory synchronization primitives system call interface and more

Courses | Duration | Effort | Prerequisites :-- | :--: | :--: | :--: Reliable Distributed Systems, Part 1 (alt) | 5 weeks | 5 hours/week | Scala, intermediate CS Reliable Distributed Systems, Part 2 (alt) | 5 weeks | 5 hours/week | Part 1 Electricity and Magnetism, Part 11 | 7 weeks | 8-10 hours/week | calculus, basic mechanics Electricity and Magnetism, Part 2 | 7 weeks | 8-10 hours/week | Electricity and Magnetism, Part 1 Computation Structures 1: Digital Circuits | 10 weeks | 6 hours/week | electricity, magnetism Computation Structures 2: Computer Architecture | 10 weeks | 6 hours/week | Computation Structures 1 Computation Structures 3: Computer Organization | 10 weeks | 6 hours/week | Computation Structures 2

1 Note: These courses assume knowledge of basic physics. (Why?) If you are struggling, you can find a physics MOOC or utilize the materials from Khan Academy: Khan Academy - Physics

Advanced theory

Topics covered: formal languages Turing machines computability event-driven concurrency automata distributed shared memory consensus algorithms state machine replication computational geometry theory propositional logic relational logic Herbrand logic concept lattices game trees and more

Courses | Duration | Effort | Prerequisites :-- | :--: | :--: | :--: Introduction to Logic | 10 weeks | 4-8 hours/week | set theory Automata Theory | 7 weeks | 10 hours/week | discrete mathematics, logic, algorithms Computational Geometry | 16 weeks | 8 hours/week | algorithms, C++ Introduction to Formal Concept Analysis | 6 weeks | 4-6 hours/week | logic, probability Game Theory | 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 :-- | :--: | :--: | :--: Modern Robotics (Specialization) | 26 weeks | 2-5 hours/week | freshman-level physics, linear algebra, calculus, linear ordinary differential equations Data Mining (Specialization) | 30 weeks | 2-5 hours/week | machine learning Big Data (Specialization) | 30 weeks | 3-5 hours/week | none Internet of Things (Specialization) | 30 weeks | 1-5 hours/week | strong programming Cloud Computing (Specialization) | 30 weeks | 2-6 hours/week | C++ programming Full Stack Web Development (Specialization) | 27 weeks | 2-6 hours/week | programming, databases Data Science (Specialization) | 43 weeks | 1-6 hours/week | none Functional Programming in Scala (Specialization) | 29 weeks | 4-5 hours/week | One year programming experience

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, but the project will also validate and consolidate your knowledge. You can create something entirely new, or you can find an existing project that needs help via websites like CodeTriage or First Timers Only.

Another option is using the Capstone project from taking one of the Specializations in 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!

Put the OSSU-CS badge in the README of your repository! Open Source Society University - Computer Science


Upon completing your final project, submit your project's information to PROJECTS via a pull request and use our community channels to announce it to your fellow students.

Your peers and mentors from OSSU will then informally evaluate your project. You will not be "graded" in the traditional sense — everyone has their own measurements for what they consider a success. The purpose of the evaluation is to act as your first announcement to the world that you are a computer scientist and to get experience listening to feedback — both positive and negative — and taking it in stride.

The final project evaluation has a second purpose: to evaluate whether OSSU, through its community and curriculum, is successful in its mission to guide independent learners in obtaining a world-class computer science education.

Cooperative work

You can create this project alone or with other students! We love cooperative work! Use our channels to communicate with other fellows to combine and create new projects!

Which programming languages should I use?

My friend, here is the best part of liberty! You can use any language that you want to complete the final project.

The important thing is to internalize the core concepts and to be able to use them with whatever tool (programming language) that you wish.

Pro CS

After completing the requirements of the curriculum above, you will have completed the equivalent of a full bachelor's degree in Computer Science, or quite close to one. You can stop in the Advanced CS section, but 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:

These aren't the only specializations you can choose. Check the following websites for more options:

Where to go next?

keep learning

Code of conduct

OSSU's code of conduct.


How to show your progress

  1. Create an account in Trello.
  2. Copy this board to your personal account. See how to copy a board here.

Now that you have a copy of our official board, you just need to pass the cards to the Doing column or Done column as you progress in your study.

We also have labels to help you have more control through the process. The meaning of each of these labels is:

The intention of this board is to provide our students a way to track their progress, and also the ability to show their progress through a public page for friends, family, employers, etc. You can change the status of your board to be public or private.