/computer-science

:mortar_board: Path to a free self-taught graduation in Computer Science

open source society university

🎓 Path to a free self-taught graduation in Computer Science!

Contents

About

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.

Becoming an OSS student

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.

Curriculum

Introduction

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

Program Design

Course Duration
Systematic Program Design- Part 1: The Core Method 5 weeks
Systematic Program Design- Part 2: Arbitrary Sized Data 5 weeks
Systematic Program Design- Part 3: Abstraction, Search and Graphs 5 weeks

Algorithms

Course Duration
Algorithms, Part I 6 weeks
Algorithms, Part II 6 weeks
Analysis of Algorithms 6 weeks

Programming Paradigms

Course Duration
Introduction to Functional Programming 7 weeks
Object Oriented Programming in Java 6 weeks
Principles of Reactive Programming 7 weeks
Programming Languages 8-16 hours/week
Functional Programming Principles in Scala 7 weeks

Software Testing

Course Duration
Software Testing 4 weeks
Software Debugging 8 weeks

Math

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

Software Architecture

Course Duration
Web Application Architectures 6-9 hours/week
Software Architecture & Design -
Microservice Architectures TODO -

Software Engineering

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 -

Operating Systems

Course Duration
Operating System Engineering -
Operating Systems and System Programming -

Computer Networks

Course Duration
Computer Networks 4–12 hours/week
Software Defined Networking 7-10 hours/week

Databases

Course Duration
Introduction to Databases -
Database Design 9 hours
Database Management Essentials 8 weeks

Cloud Computing

Course Duration
Introduction to Cloud Computing 4 weeks
Cloud Computing Specialization -

Cryptography

Course Duration
Cryptography I 6 weeks
Cryptography II 6 weeks
Applied Cryptography 8 weeks

Compilers

Course Duration
Compilers 11 weeks

UX Design

Course Duration
Interaction Design Specialization -
UX Design for Mobile Developers 6 weeks

Artificial Intelligence

Course Duration
Artificial Intelligence 12 weeks

Machine Learning

Course Duration
Practical Machine Learning 4 weeks
Machine Learning 11 weeks
Neural Networks for Machine Learning 8 weeks

Natural Language Processing

Course Duration
Natural Language Processing 10 weeks
Natural Language Processing 10 weeks

Big Data

Course Duration
Big Data Specialization -

Data Mining

Course Duration
Data Mining specialization -

How to use this guide

Order of the classes

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.

Should I take all courses?

Yes! The intention is to conclude all the courses listed here!

Duration of the project

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!

How can I track/show my progress?

To track your progress, you should update the profile that you created here, adding the courses that you began or ended.

To show your progress, 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.

ps: You should share only files that you are allowed to! Do NOT disrespect the code of conduct that you signed in the beginning of some courses.

Be creative in order to show your progress! 😄

Cooperative work

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.

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 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.

Be creative!

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.

Stay tuned

Watch this repository for futures improvements and general information.

Prerequisite

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!

How to collaborate

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! =)

Community

Join us in our group!

You can also interact through GitHub issues.

We also have a chat room! Join the chat at https://gitter.im/open-source-society/computer-science-and-engineering

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.

Next Goals

References