🎓 Path to a free self-taught graduation in Computer Science!
- About
- Becoming an OSS student
- Topics
- How to use this guide
- Prerequisite
- How to collaborate
- Community
- Next Goals
- References
This is a solid path for you that want to graduate in a Computer Science course in your own time, for free, with courses from the best universities of the World.
Futurely, more categories and/or courses will be added to this list or in a more advanced/specialized list.
Initially, we will also give preference for MOOC (Massive Open Online Course) type of courses because those courses were created with our style of learning in mind.
Your registration for this graduation course will be effectuated after you create your profile in our students folder.
"How can I do this?"
Just fork this repository and create a markdown file named with your GitHub username. It’s that simple.
Use the model below to create your own file:
# Student Profile
- **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]()
ps: In the Completed Courses section, you should link your repository that contain the files that you created in the respective course.
"Why should I do this?"
Making a public commitment, we have much more chances to succeed in our graduation, know better our fellows and share the things that we have done.
For those reasons we are using that 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 | 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 this means? That you should do one course at a time.
The courses already are in the order that you should consume 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!
Maybe to finish all the classes we will spend more time than with a regular CS course, but I can guarantee to you that your reward will be proportional to your motivation/dedication!
You should create a repository on GitHub to put all files that you created for each course.
You can create one repository for each course, or just one repository that will contain all files for all courses. The first option is our preferred approach.
We love cooperative work! But is quite difficult manage a large base of students with specific projects. Use our channels to communicate with other fellows and to combine and create new projects.
My friend here is the awesome part of the 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 be able to use them with whatever tool (programming language) that you touch.
Watch this repository for futures improvements and general information.
The only thing that you need to know is how to use Git and GitHub. Here are some resources to learn about them:
ps: You don't need to do all that courses. Just pick one of them, learn the minimal because the other things you will learn on the go!
You can open an issue and give your suggestion to how we could 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.
ps: A forum is an ideal way to interact with other students because in that way we do not lose important discussions, as occur usually in communication via chat apps.
- Adding our university page at Linkedin, so in that way we will be able to add OSS University in our Linkedin profile.