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.
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*.
[How to Code: Systematic Program Design (XSeries)](https://www.edx.org/xseries/how-code-systematic-program-design) | 15 weeks | 5 hours/week | none
[Object Oriented Programming in Java](https://www.coursera.org/learn/object-oriented-java) | 6 weeks | 4-6 hours/week | some programming
[Programming Languages, Part A](https://www.coursera.org/learn/programming-languages) | 4 weeks | 8-16 hours/week | recommended: Java, C
[Programming Languages, Part B](https://www.coursera.org/learn/programming-languages-part-b) | 3 weeks | 8-16 hours/week | Programming Languages, Part A
[Programming Languages, Part C](https://www.coursera.org/learn/programming-languages-part-c) | 3 weeks | 8-16 hours/week | Programming Languages, Part B
**Note**: The Object-Oriented Programming in Java class is intended for students who have already taken a basic Java course, but it can still be completed by those who have only studied basic programming before in a different, Java-like language (e.g., C).
[Introduction to Programming in Java](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-092-introduction-to-programming-in-java-january-iap-2010/index.htm).
[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
[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
[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
**Note 1**: The 'From Nand to Tetris' course, in part I, will have you create an entire computer architecture from scratch, but is missing key elements from computer architecture such as pipelining and memory hierarchy.
**Note 2**: Part II of the same course has you build the very lowest levels of an operating system on top of the computer architecture you built, however it does not go very deep into operating systems.
Both of the above textbooks should be considered a requirement for anyone who intends to become a *[systems programmer](https://en.wikipedia.org/wiki/System_programming)*.
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.
After finishing the curriculum above, you will have completed close to a full bachelor's degree in Computer Science.
You can stop here, but if you really want to make yourself valuable, the next step to completing your studies is to develop skills and knowledge in a specific domain.
Choose one or more of the following specializations:
- [Artificial Intelligence Engineer Nanodegree](https://www.udacity.com/ai) by IBM, Amazon, and Didi
- [Data Mining Specialization](https://www.coursera.org/specializations/data-mining) by the University of Illinois at Urbana-Champaign
- [Big Data Specialization](https://www.coursera.org/specializations/big-data) by the University of California at San Diego
- [Data Analyst Nanodegree](https://www.udacity.com/course/data-analyst-nanodegree--nd002) by Facebook and mongoDB
- [Applied Data Science with Python Specialization](https://www.coursera.org/specializations/data-science-python) by the University of Michigan
- [Data Science Specialization](https://www.coursera.org/specializations/jhu-data-science) by Johns Hopkins University
- [Mastering Software Development in R Specialization](https://www.coursera.org/specializations/r) by Johns Hopkins University
- [Machine Learning Engineer Nanodegree](https://www.udacity.com/course/machine-learning-engineer-nanodegree--nd009) by kaggle
- [Cybersecurity MicroMasters](https://www.edx.org/micromasters/ritx-cybersecurity) by the Rochester Institute of Technology
- [Cloud Computing Specialization](https://www.coursera.org/specializations/cloud-computing) by the University of Illinois at Urbana-Champaign
- [Internet of Things Specialization](https://www.coursera.org/specializations/internet-of-things) by the University of California at San Diego
- [Full Stack Web Development Specialization](https://www.coursera.org/specializations/full-stack) by the Hong Kong University of Science and Technology
- [Android Developer Nanodegree](https://www.udacity.com/course/android-developer-nanodegree-by-google--nd801) by Google
These aren't the only specializations you can choose. Check the following websites for more options:
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.
> "What does it mean?"
After you finish the curriculum, 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.
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!
[](https://github.com/open-source-society/computer-science)
- Markdown: `[](https://github.com/open-source-society/computer-science)`
- HTML: `<a href="https://github.com/open-source-society/computer-science"><img alt="Open Source Society University - Computer Science" src="https://img.shields.io/badge/OSSU-computer--science-blue.svg"></a>`
### Cooperative work
You can create this project alone or with other students!
**We love cooperative work**!
Use our [channels](#community) to communicate with other fellows to combine and create new projects!
### Project Suggestions
- [Projects](https://github.com/karan/Projects): A list of practical projects that anyone can solve in any programming language.
- [app-specs](https://github.com/ericdouglas/app-specs): A curated list of applications specifications and implementations to practice new technologies, improve your portfolio and sharpen your skills.
- [FreeCodeCamp](http://www.freecodecamp.com/): Course that teaches you fullstack JavaScript development through a bunch of projects.
- [JavaScript Projects](https://github.com/javascript-society/javascript-projects): List of projects related with the [JavaScript Path](https://github.com/javascript-society/javascript-path).
### 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.
Ideally, it can be consumed in a linear approach, i.e. you complete one course at a time, but in reality different people have different preferences with regard to how many courses they wish to take at once.
Plus, different courses are available at different times and have wildly different time requirements.
No promises are made about the cost of any of the courses.
The web is utterly filled with free educational material if you are willing to spend the time looking for it;
this curriculum has specifically been designed to prioritize *quality* over low cost.
Nevertheless, the reality is that the professors who have made these courses and the platforms who host them are extraordinarily generous.
The content of virtually every course on Coursera and edX is available at no charge, but if you want graded assignments and quizzes, you may have to pay, depending on the course;
yet, these sums of money are nothing compared to the cost of attending their prestigious instutitions.
- [How to Design Programs](http://www.ccs.neu.edu/home/matthias/HtDP2e/)
(Note: This is the **book** upon which *How to Code: Systematic Program Design* is based, but the course is not taught by the book's author; they are completely separate)
Just remember that purchasing a course might save you some time and give you some extra motivation, but you cannot buy your way to success in this field.
It's encouraged more as a way to thank the professor for their work.
The most diligent students will be the most successful, regardless of how much or how little they spend.
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**.
If you are serious about getting an online education comparable to a bachelor's degree in Computer Science, you should absolutely take **all** of the courses under the 'Core CS' section.
These courses are equivalent to about 3/4 of a full bachelor's degree in CS.
So if you want to really complete your studies, then you should select one of the specializations to finish out your program, such as one in Artificial Intelligence or Big Data.
If you are able to devote 18-20 hours per week to this curriculum, taking 1-3 clases at a time, you could hypothetically finish the Core CS section in under 2 years.
A specialization would then take you a few more months.
This curriculum assumes the student has already taken high school math, including algebra, geometry, and pre-calculus.
Some high school students will have taken calculus, but this is usually only about 3/4 of a college calculus class, so the calculus courses listed above are still recommended.
To show respect to all of our students, we will keep a [CHANGELOG](CHANGELOG.md) file that contains all the alterations that our curriculum may suffer.
Our students can trust in this curriculum because it has been **carefully planned** and covers the major **core topics** that a conventional Computer Science program covers.
You can [open an issue](https://help.github.com/articles/creating-an-issue/) and give us your suggestions as to how we can improve this guide, or what we can do to improve the learning experience.
You can also [fork this project](https://help.github.com/articles/fork-a-repo/) and send a [pull request](https://help.github.com/articles/using-pull-requests/) to fix any mistakes that you have found.
If you want to suggest a new resource, send a pull request adding such resource to the [extras](https://github.com/open-source-society/computer-science/tree/master/extras) section.
The **extras** section is a place where all of us will be able to submit interesting additional articles, books, courses and specializations, keeping our curriculum *as immutable and concise as possible*.
We also have a chat room! [](https://gitter.im/open-source-society/computer-science?utm_campaign=pr-badge&utm_content=badge&utm_medium=badge&utm_source=badge)
> **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 subreddit/group for important discussions**.
