Path to a free self-taught education in Computer Science!
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:
- Be open for enrollment
- Run regularly (ideally in self-paced format, otherwise running at least once a month or so)
- Fulfill the academic requirements of OSSU
- Fit neatly into the progression of the curriculum with respect to topics and difficulty level
- Be of generally high quality in teaching materials and pedagogical principles
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:
- Intro CS: for students to try out CS and see if it's right for them
- Core CS: corresponds roughly to the first three years of a computer science curriculum, taking classes that all majors would be required to take
- Advanced CS: corresponds roughly to the final year of a computer science curriculum, taking electives according to the student's interests
- Final Project: a project for students to validate, consolidate, and display their knowledge, to be evaluated by their peers worldwide
- Pro CS: graduate-level specializations students can elect to take after completing the above curriculum if they want to maximize their chances of getting a good job
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.
- For grouping up, please use the cohorts repository to find or create a cohort suited to you.
- We recommend doing all courses in Core CS, only skipping a course when you are certain that you've already learned the material previously.
- For simplicity, we recommend working through courses (especially Core CS) in order from top to bottom, as they have already been topologically sorted by their prerequisites.
- Courses in Advanced CS are electives. Choose one subject (e.g. Advanced programming) you want to become an expert in and take all the courses under that heading. You can also create your own custom subject, but we recommend getting validation from the community on the subject you choose.
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)
- Core CS assumes the student has already taken high school math and physics, including algebra, geometry, and pre-calculus. Some high school graduates will have already taken AP Calculus, but this is usually only about 3/4 of a college calculus class, so the calculus courses in the curriculum are still recommended.
- Advanced CS assumes the student has already taken the entirety of Core CS and is knowledgeable enough now to decide which electives to take.
- Note that Advanced systems assumes the student has taken a basic physics course (e.g. AP Physics in high school).
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 |
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 |
All coursework under Core CS is required, unless otherwise indicated.
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 |
- Required to learn about monads, laziness, purity: Learn You a Haskell for a Great Good!
- Note: probably the best resource to learn Haskell: Haskell Programming from First Principles
paid
- Note: probably the best resource to learn Haskell: Haskell Programming from First Principles
- Required, to learn about logic programming, backtracking, unification: Learn Prolog Now!
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.
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 1 Assignment 2 Assignment 3 Assignment 4 |
algebra, probability, basic CS |
| ops-class.org - Hack the Kernel | 15 weeks | 6 hours/week | Replace course textbook with Operating Systems: Three Easy Pieces | algorithms |
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 |
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 | 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 |
| Cryptography I | 6 weeks | 5-7 hours/week | linear algebra, probability |
| 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 |
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.
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 |
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 |
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 | 5 weeks | 5 hours/week | Scala, intermediate CS |
| Reliable Distributed Systems, Part 2 | 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
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 |
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) | 26 weeks | 2-5 hours/week | linear algebra, calculus, programming, probability |
| 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 |
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!
- Markdown:
[](https://github.com/ossu/computer-science) - HTML:
<a href="https://github.com/ossu/computer-science"><img alt="Open Source Society University - Computer Science" src="https://img.shields.io/badge/OSSU-computer--science-blue.svg"></a>
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.
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!
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.
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:
- Mastering Software Development in R Specialization by Johns Hopkins University
- Artificial Intelligence Engineer Nanodegree by IBM, Amazon, and Didi
- Machine Learning Engineer Nanodegree by Kaggle
- Cybersecurity MicroMasters by the Rochester Institute of Technology
- Android Developer Nanodegree by Google
These aren't the only specializations you can choose. Check the following websites for more options:
- edX: xSeries
- Coursera: Specializations
- Udacity: Nanodegree
- Look for a job as a developer!
- Check out the readings for classic books you can read that will sharpen your skills and expand your knowledge.
- Join a local developer meetup (e.g. via meetup.com).
- Pay attention to emerging technologies in the world of software development:
- Explore the actor model through Elixir, a new functional programming language for the web based on the battle-tested Erlang Virtual Machine!
- Explore borrowing and lifetimes through Rust, a systems language which achieves memory- and thread-safety without a garbage collector!
- Explore dependent type systems through Idris, a new Haskell-inspired language with unprecedented support for type-driven development.
- Subscribe to our newsletter.
- Use our forum if you need some help.
- You can also interact through GitHub issues.
- We also have a chat room!
- Add Open Source Society University to your Linkedin profile!
PS: A forum is an ideal way to interact with other students as we do not lose important discussions, which usually occur in communication via chat apps. Please use our forum for important discussions.
- Create an account in Trello.
- 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:
Main Curriculum: cards with that label represent courses that are listed in our curriculum.Extra Resources: cards with that label represent courses that were added by the student.Doing: cards with that label represent courses the student is current doing.Done: cards with that label represent courses finished by the student. Those cards should also have the link for at least one project/article built with the knowledge acquired in such course.Section: cards with that label represent the section that we have in our curriculum. Those cards with theSectionlabel are only to help the organization of the Done column. You should put the Course's cards below its respective Section's card.
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.
- Eric Douglas: founder of OSSU and co-maintainer of all its curricula
- hanjiexi: lead technical maintainer
- waciumawanjohi: lead academic maintainer
- Contributors