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 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!
Getting help (Details about our FAQ and chatroom)
Curriculum version: 8.0.0
(see CHANGELOG)
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 Introduction to CS Tools.)
Topics covered:
simple programs
simple data structures
Courses | Effort | Prerequisites :-- | :--: | :--: Python for Everyone (alt) | 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
Understanding theory is important, but you will also be expected to create programs. There are a number of tools that are widely used to make that process easier. Learn them now to ease your future work writing programs.
Topics covered:
terminals and shell scripting
vim
command line environments
version control
and more
Courses | Duration | Effort | Prerequisites :-- | :--: | :--: | :--: The Missing Semester of Your CS Education | 2 weeks | 12 hours/week | -
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
The How to Code courses are based on the textbook How to Design Programs. The First Edition is available for free online and includes problem sets and solutions. Students are encouraged to do these assignments.
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 Programming Languages, Part A | 5 weeks | 4-8 hours/week | recommended: Java, C Programming Languages, Part B | 3 weeks | 4-8 hours/week | Programming Languages, Part A Programming Languages, Part C | 3 weeks | 4-8 hours/week | Programming Languages, Part B
Students must choose one of the following topics: calculus, linear algebra, logic, or probability.
Courses | Duration | Effort | Prerequisites :-- | :--: | :--: | :--: 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 | 6 weeks | 5-10 hours/week | Calculus 1B
Courses | Duration | Effort | Prerequisites :-- | :--: | :--: | :--: Essence of Linear Algebra | - | - | pre-calculus Linear Algebra | 14 weeks | 12 hours/week | Essence of Linear Algebra
Courses | Duration | Effort | Prerequisites :-- | :--: | :--: | :--: Introduction to Logic | 10 weeks | 4-8 hours/week | set theory
Courses | Duration | Effort | Prerequisites :-- | :--: | :--: | :--: Introduction to Probability - The Science of Uncertainty | 18 weeks | 12 hours/week | Multivariable Calculus
In addition to their math elective, students must complete the following course on discrete mathematics.
Topics covered:
discrete mathematics
mathematical proofs
basic statistics
O-notation
discrete probability
and more
Courses | Duration | Effort | Notes | Prerequisites :-- | :--: | :--: | :--: | :--: Mathematics for Computer Science | 13 weeks | 5 hours/week | An alternate version with solutions to the problem sets is here. Students struggling can consider the Discrete Mathematics Specialization first. It is more interactive but less comprehensive, and costs money to unlock full interactivity. | Calculus 1C
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 Operating Systems: Three Easy Pieces | 10-12 weeks | 6 hours/week | Homework Lectures Supplement | 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 :-- | :--: | :--: | :--: Divide and Conquer, Sorting and Searching, and Randomized Algorithms | 4 weeks | 4-8 hours/week | any programming language, Mathematics for Computer Science Graph Search, Shortest Paths, and Data Structures | 4 weeks | 4-8 hours/week | Divide and Conquer, Sorting and Searching, and Randomized Algorithms Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming | 4 weeks | 4-8 hours/week | Graph Search, Shortest Paths, and Data Structures Shortest Paths Revisited, NP-Complete Problems and What To Do About Them | 4 weeks | 4-8 hours/week | Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming
Topics covered
Confidentiality, Integrity, Availability
Secure Design
Defensive Programming
Threats and Attacks
Network Security
Cryptography
and more
Note: These courses are provisionally recommended. There is an open Request For Comment on security course selection. Contributors are encouraged to compare the various courses in the RFC and offer feedback.
Courses | Duration | Effort | Prerequisites :-- | :--: | :--: | :--: Information Security: Context and Introduction | 5 weeks | 3 hours/week | - Principles of Secure Coding| 4 weeks | 4 hours/week | - Identifying Security Vulnerabilities | 4 weeks | 4 hours/week | -
Choose one of the following: Courses | Duration | Effort | Prerequisites :-- | :--: | :--: | :--: Identifying Security Vulnerabilities in C/C++Programming | 4 weeks | 5 hours/week | - Exploiting and Securing Vulnerabilities in Java Applications | 4 weeks | 5 hours/week | -
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 :-- | :--: | :--: | :--: Relational Database Systems| 6 weeks | 3 hours/week | - 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 | Core Programming, and a sizable project 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 :-- | :--: | :--: | :--: Introduction to Parallel Programming (alt) (HW)| 12 weeks | - | C, algorithms Compilers (alt)| 9 weeks | 6-8 hours/week | none Introduction to Haskell| 14 weeks | - | - Learn Prolog Now!| 12 weeks | - | - 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 Software Architecture & Design| 8 weeks | 6 hours/week | software engineering in Java
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 :-- | :--: | :--: | :--: 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 :-- | :--: | :--: | :--: Theory of Computation (Lectures) | 8 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 | 3 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 :-- | :--: | :--: | :--: 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 Game Design and Development (Specialization) | 6 months | 5 hours/week | programming, interactive design
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](https://img.shields.io/badge/OSSU-computer--science-blue.svg)](https://github.com/ossu/computer-science)
<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. Congratulations!
What is next for you? The possibilities are boundless and overlapping:
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 the Section
label 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.