🎓 Path to a free self-taught graduation in Computer Science!
- About
- Becoming an OSS student
- Curriculum
- How to use this guide
- Prerequisite
- How to collaborate
- Community
- Next Goals
- References
This is a solid path for those of you who want to complete a Computer Science course on your own time, for free, with courses from the best universities in the World.
In the future, more categories and/or courses will be added to this list or a more advanced/specialized list.
Initially, we will also give preference to MOOC (Massive Open Online Course) type of courses because those courses were created with our style of learning in mind.
To officially register for this course you must create a profile in our students profile issue.
"How can I do this?"
Comment in this issue (please, do not open a new one) using the following template:
- **Name**: YOUR NAME
- **GitHub**: [@your_username]()
- **Twitter**: [@your_username]()
- **Linkedin**: [link]()
- **Website**: [yourblog.com]()
## Completed Courses
**Name of the Section**
Course|Files
:--|:--:
Course Name| [link]()
IMPORTANT: add your profile only once and after you finish each course you can return to that issue and update your comment.
ps: In the Completed Courses section, you should link the repository that contains the files that you created in the respective course.
"Why should I do this?"
By making a public commitment, we have a greater chance of successfully graduating, a way to get to know our peers better, and an opportunity to share the things that we have done.
That is why we are using this strategy.
- Introduction
- Program Design
- Programming Paradigms
- Software Testing
- Math
- Algorithms
- Software Architecture
- Software Engineering
- Operating Systems
- Computer Networks
- Databases
- Cloud Computing
- Cryptography
- Compilers
- UX Design
- Artificial Intelligence
- Machine Learning
- Natural Language Processing
- Big Data
- Data Mining
| Course | Duration |
|---|---|
| Introduction to Computer Science | 9 ~ 15 weeks |
| Introduction to Computer Science and Programming Using Python | 9 weeks |
| Introduction to Computational Thinking and Data Science | 10 weeks |
| Course | Duration |
|---|---|
| Introduction to Functional Programming | 7 weeks |
| Principles of Reactive Programming | 7 weeks |
| Programming Languages | 8-16 hours/week |
| Functional Programming Principles in Scala | 7 weeks |
| Course | Duration |
|---|---|
| Software Testing | 4 weeks |
| Software Debugging | 8 weeks |
| Course | Duration |
|---|---|
| Effective Thinking Through Mathematics | 9 weeks |
| Applications of Linear Algebra Part 1 | 5 weeks |
| Applications of Linear Algebra Part 2 | 4 weeks |
| Linear and Discrete Optimization | 3-6 hours/week |
| Probabilistic Graphical Models | 11 weeks |
| Game Theory | 9 weeks |
| Course | Duration |
|---|---|
| Algorithms, Part I | 6 weeks |
| Algorithms, Part II | 6 weeks |
| Analysis of Algorithms | 6 weeks |
| Course | Duration |
|---|---|
| Web Application Architectures | 6-9 hours/week |
| Software Architecture & Design | - |
| Microservice Architectures TODO | - |
| Course | Duration |
|---|---|
| Engineering Software as a Service (SaaS), Part 1 | 9 weeks |
| Engineering Software as a Service (Saas), Part 2 | 8 weeks |
| Software Product Management Specialization | - |
| Course | Duration |
|---|---|
| Operating System Engineering | - |
| Operating Systems and System Programming | - |
| Course | Duration |
|---|---|
| Computer Networks | 4–12 hours/week |
| Software Defined Networking | 7-10 hours/week |
| Course | Duration |
|---|---|
| Introduction to Databases | - |
| Database Design | 9 hours |
| Database Management Essentials | 8 weeks |
| Course | Duration |
|---|---|
| Introduction to Cloud Computing | 4 weeks |
| Cloud Computing Specialization | - |
| Course | Duration |
|---|---|
| Cryptography I | 6 weeks |
| Cryptography II | 6 weeks |
| Applied Cryptography | 8 weeks |
| Course | Duration |
|---|---|
| Compilers | 11 weeks |
| Course | Duration |
|---|---|
| Interaction Design Specialization | - |
| UX Design for Mobile Developers | 6 weeks |
| Course | Duration |
|---|---|
| Artificial Intelligence | 12 weeks |
| Course | Duration |
|---|---|
| Practical Machine Learning | 4 weeks |
| Machine Learning | 11 weeks |
| Neural Networks for Machine Learning | 8 weeks |
| Course | Duration |
|---|---|
| Natural Language Processing | 10 weeks |
| Natural Language Processing | 10 weeks |
| Course | Duration |
|---|---|
| Big Data Specialization | - |
| Course | Duration |
|---|---|
| Data Mining specialization | - |
This guide was developed to be consumed in a linear approach. What does this mean? That you should complete one course at a time.
The courses are already in the order that you should complete them. Just start in the Introduction section and after finishing the first course, start the next one.
If the course isn't open, do it anyway with the resources from the previous class.
Yes! The intention is to conclude all the courses listed here!
It may take longer to complete all of the classes compared to a regular CS course, but I can guarantee you that your reward will be proportional to your motivation/dedication!
You should create a repository on GitHub to put all of the files that you created for each course.
You can create one repository per course, or just one repository that will contain all of the files for each course. The first option is our preferred approach.
We love cooperative work! But is quite difficult to manage a large base of students with specific projects. 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 courses.
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 is a crucial part of your journey through all those courses.
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.
- Real projects: you can try to develop at least one real project for each course that you enroll. It doesn't need to be a big project, just a small one to validate and consolidate your knowledge. Some project suggestions here and here.
Watch this repository for futures improvements and general information.
The only things that you need to know are how to use Git and GitHub. Here are some resources to learn about them:
ps: You don't need to do all of the courses. Just pick one and learn the basics because you will learn more on the go!
You can open 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 and fix any mistakes that you have found.
Let's do it together! =)
Join us in our group!
You can also interact through GitHub issues.
We also have a chat room!
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.
- Adding our university page at Linkedin, so that way we will be able to add OSS University in our Linkedin profile.