This is a **solid path** for those of you who want to complete a **Computer Science** course 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.
The first one is 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**.
The second link is a MOOC that will teach you learning techniques used by experts in art, music, literature, math, science, sports, and many other disciplines.
These are **fundamental abilities** to succeed in our journey.
[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). The learning curve will be steep, however, so for those who
find it too difficult, looking over the material in this course is recommended:
[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).
[Calculus Two: Sequences and Series](https://www.coursera.org/learn/advanced-calculus)| 7 weeks | 9-10 hours/week | Calculus One
[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
[Discrete Mathematics](https://www.coursera.org/learn/discrete-mathematics) | 11 weeks | 3-5 hours/week | high school math
[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
[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 are missing key elements from computer architecture such as pipelining and memory hierarchy.
A supplemental textbook is recommended for those interested in the subject:
[Computer Organization and Design](https://smile.amazon.com/Computer-Organization-Design-Fifth-Architecture/dp/0124077269).
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.
For those interested in this subject, this free supplemental textbook is strongly recommended:
[Operating Systems: Three Easy Pieces](http://pages.cs.wisc.edu/~remzi/OSTEP/).
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:
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.
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.
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 finish a course, you should think about a problem that you can solve using the acquired knowledge in the course.
It doesn't have to be a big project, but rather it should show the world that you are capable of creating something useful with the concepts that you learned.
It won't make sense to do a project for *every* course, as some have no immediate practical application.
But anytime you gain practical skills (e.g., a new programming language), you should use it right away to **validate** and **consolidate** your knowledge.
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>`
- [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).
You **need** to have in mind that what you are able to create with the concepts that you learned will be your certificate **and this is what really matters**!
In order to show that you **really** learned those things, you need to be **creative**!
Here are some tips about how you can do that:
- **Articles**: create blog posts to synthesize/summarize what you learned.
- **GitHub repository**: keep your course's files organized in a GH repository, so in that way other students can use it to study with your annotations.
The important thing for each course is to **internalize** the core concepts and to be able to use them with whatever tool (programming language) that you wish.
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**.
