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🎓 Path to a free education in Computer Science, Kottans projects.
| Coursese | Duration | Effort |
|---|---|---|
| Computational Thinking for Problem Solving | 4 weeks | 6-8 hours/week |
| Introduction to Computer Science - CS50 (alt) | 12 weeks | 10-20 hours/week |
| Introduction to Computer Science and Programming using Python | 9 weeks | 15 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
| 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!
- OBS: probably the best resource to learn Haskell: Haskell Programming from First Principles
paid
- OBS: 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 One1 (alt) | 16 weeks | 8-10 hours/week | pre-calculus |
| Calculus Two: Sequences and Series | 7 weeks | 9-10 hours/week | Calculus One |
| Mathematics for Computer Science | 13 weeks | 5 hours/week | single variable calculus (Calculus Two) |
1 Note: When you are enrolled, please see this list of errors and these recommendations for how to progress through the course.
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 (alt) | 6 weeks | 7-13 hours/week | none |
| Build a Modern Computer from First Principles: Nand to Tetris Part II | 6 weeks | 12-18 hours/week | From Nand to Tetris Part I |
| Introduction to Computer Networking | 8 weeks | 4–12 hours/week | algebra, probability, basic CS |
| ops-class.org - Hack the Kernel | 15 weeks | 6 hours/week | algorithms |
- Recommended: While Hack the Kernel recommends Modern Operating Systems as a textbook, we suggest using Operating Systems: Three Easy Pieces.
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 | programming experience |
| LAFF: Programming for Correctness | 7 weeks | 6 hours/week | linear algebra |
| Introduction to Parallel Programming | 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 |
|---|---|---|---|
| Calculus: Parametric Equations and Polar Coordinates | - | - | single-variable calculus (Calculus Two) |
| Multivariable Calculus | 13 weeks | 12 hours/week | Parametric Equations and Polar Coordinates |
| 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 |
|---|---|---|---|
| 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 | 8 weeks | 10 hours/week | discrete mathematics, logic, algorithms |
| 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 |
| 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/weeks | 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, the project will 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 and Facebook 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 was 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.
- Google - Guide for Technical Development
- Coursera
- edX
- Udacity
- Stanford University
- Carnegie Mellon University: Computer Science Major Requirements
- MIT Open Courseware
- Teach Yourself Computer Science
- Obtaining a Thorough CS Background Online
Obtaining a Thorough CS Background Online