![Open Source Society University (OSSU)](http://i.imgur.com/GjEbgIJ.png)
Path to a self-taught education in Computer Science!
# Contents - [About](#about) - [Motivation & Preparation](#motivation--preparation) - [Curriculum](#curriculum) - [How to use this guide](#how-to-use-this-guide) - [Prerequisite](#prerequisite) - [How to collaborate](#how-to-collaborate) - [Community](#community) - [Team](#team) - [References](#references) # About 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. # Motivation & Preparation Here are two interesting links that can make all the difference in your journey. 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**. - [MIT Challenge](https://www.scotthyoung.com/blog/myprojects/mit-challenge-2/) 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. - [Learning How to Learn](https://www.coursera.org/learn/learning-how-to-learn) **Are you ready to get started?** # Curriculum - [Core CS](#core-cs) - [Core programming](#core-programming) - [Core math](#core-math) - [Core systems](#core-systems) - [Core theory](#core-theory) - [Core applications](#core-applications) - [Advanced programming](#advanced-programming) - [Electives](#electives) - [Specializations](#specializations) --- ## Core CS ### Core programming Courses | Duration | Effort :-- | :--: | :--: [Introduction to Computer Science - CS50](https://www.edx.org/course/introduction-computer-science-harvardx-cs50x#!)| 12 weeks | 10-20 hours/week [How to Code: Systematic Program Design (XSeries)](https://www.edx.org/xseries/how-code-systematic-program-design) | 15 weeks | 5 hours/week [Object Oriented Programming in Java](https://www.coursera.org/learn/object-oriented-java) | 6 weeks | 4-6 hours/week [Programming Languages, Part A](https://www.coursera.org/learn/programming-languages) | 4 weeks | 8-16 hours/week [Programming Languages, Part B](https://www.coursera.org/learn/programming-languages-part-b) | 3 weeks | 8-16 hours/week [Programming Languages, Part C](https://www.coursera.org/learn/programming-languages-part-c) | 3 weeks | 8-16 hours/week ### Core math Courses | Duration | Effort :-- | :--: | :--: [Calculus One](https://www.coursera.org/learn/calculus1)| 16 weeks | 8-10 hours/week [Calculus Two: Sequences and Series](https://www.coursera.org/learn/advanced-calculus)| 7 weeks | 9-10 hours/week [Introduction to Probability - The Science of Uncertainty](https://www.edx.org/course/introduction-probability-science-mitx-6-041x-2) | 18 weeks | 12 hours/week [Discrete Mathematics](https://www.coursera.org/learn/discrete-mathematics) | 11 weeks | 3-5 hours/week [Linear Algebra - Foundations to Frontiers](https://www.edx.org/course/linear-algebra-foundations-frontiers-utaustinx-ut-5-04x#!)| 15 weeks | 8 hours/week ### Core systems Courses | Duration | Effort :-- | :--: | :--: [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 [Build a Modern Computer from First Principles: Nand to Tetris Part II ](https://www.coursera.org/learn/nand2tetris2) | 6 weeks | 12-18 hours/week [Databases](https://lagunita.stanford.edu/courses/DB/2014/SelfPaced/about)| 12 weeks | 8-12 hours/week [Introduction to Computer Networking](https://lagunita.stanford.edu/courses/Engineering/Networking-SP/SelfPaced/about)| - | 4–12 hours/week ### Core theory Courses | Duration | Effort :-- | :--: | :--: [Divide and Conquer, Sorting and Searching, and Randomized Algorithms](https://www.coursera.org/learn/algorithms-divide-conquer) | 4 weeks | 4-8 hours/week [Graph Search, Shortest Paths, and Data Structures](https://www.coursera.org/learn/algorithms-graphs-data-structures) | 4 weeks | 4-8 hours/week [Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming](https://www.coursera.org/learn/algorithms-greedy) | 4 weeks | 4-8 hours/week [Shortest Paths Revisited, NP-Complete Problems and What To Do About Them](https://www.coursera.org/learn/algorithms-npcomplete) | 4 weeks | 4-8 hours/week [Automata Theory](https://lagunita.stanford.edu/courses/course-v1:ComputerScience+Automata+Fall2016/about) | 8 weeks | 10 hours/week ### Core applications Courses | Duration | Effort :-- | :--: | :--: [Machine Learning](https://www.coursera.org/learn/machine-learning)| 11 weeks | 4-6 hours/week [Computer Graphics](https://www.edx.org/course/computer-graphics-uc-san-diegox-cse167x)| 6 weeks | 12 hours/week [Cryptography I](https://www.coursera.org/course/crypto)| 6 weeks | 5-7 hours/week ### Advanced programming Courses | Duration | Effort :-- | :--: | :--: [Software Testing](https://www.udacity.com/course/software-testing--cs258)| 4 weeks | 6 hours/week [Software Debugging](https://www.udacity.com/course/software-debugging--cs259)| 8 weeks | 6 hours/week [Introduction to Parallel Programming](https://www.udacity.com/course/intro-to-parallel-programming--cs344) | 12 weeks | - [Software Architecture & Design](https://www.udacity.com/course/software-architecture-design--ud821)| 8 weeks | 6 hours/week ## Electives Courses | Duration | Effort :-- | :--: | :--: [Cryptography II](https://www.coursera.org/course/crypto2)| 6 weeks | 6-8 hours/week [Compilers](https://lagunita.stanford.edu/courses/Engineering/Compilers/Fall2014/about)| 9 weeks | 6-8 hours/week [Introduction to Natural Language Processing](https://www.coursera.org/learn/natural-language-processing)| 12 weeks | - ## Specializations After finishing the courses above, start your specializations on the topics that you have more interest. The following platforms currently offer specializations: ### edX: [xSeries](https://www.edx.org/xseries) ### Coursera: [Specializations](https://www.coursera.org/specializations) ### Udacity: [Nanodegree](https://www.udacity.com/nanodegree) ### FutureLearn: [Collections](https://www.futurelearn.com/courses/collections) ![keep learning](http://i.imgur.com/REQK0VU.jpg) # How to use this guide ## Order of the classes This guide was developed to be flexible. 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, and different courses are available at different times and have wildly different time requirements. Therefore, many students will take the courses in a non-linear order, based on availability and how much time they have to devote to each class. Any course that is part of 'Core CS' section should be available either regularly, in self-paced format, or in archived form. Some of the electives are only available once in a while. ## How to track and show your progress 1. Create an account in [Trello](https://trello.com/). 1. Copy [this](https://trello.com/b/9DPXYv5f) board to your personal account. See how to copy a board [here](http://blog.trello.com/you-can-copy-boards-now-finally/). Now that you have a copy of our official board, you just need to pass the cards to the `Doing` column or `Done` column as you progress in your study. We also have **labels** to help you have more control through the process. The meaning of each of these labels is: - `Main Curriculum`: cards with that label represent courses that are listed in our curriculum. - `Extra Courses`: cards with that label represent courses that was added by the student. - `Doing`: cards with that label represent courses the student is current doing. - `Done`: cards with that label represent courses finished by the student. Those cards should also have the link for at least one project/article built with the knowledge acquired in such course. - `Section`: cards with that label represent the section that we have in our curriculum. Those cards with the `Section` label are only to help the organization of the Done column. You should put the *Course's cards* below its respective *Section's card*. - `Extra Sections`: cards with that label represent sections that was added by the student. 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**. ## Should I take all courses? 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. ## Duration of the project 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. It will probably take longer if you go slower, but regardless, your **reward** will be proportional to your **effort**. You must focus on your **habit**, and **forget** about goals. Try to invest 1 ~ 2 hours **every day** studying this curriculum. If you do this, **inevitably** you'll finish this curriculum. > See more about "Commit to a process, not a goal" [here](http://jamesclear.com/goals-systems). ## Project-based **OSS University** is **project-focused**. 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. In order to show everyone that you successfully finished a course, you should create a **real project**. > "What does it mean?" 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 are purely theoretical (e.g. calculus). But anytime you gain practical skills (e.g., a new programming language), you should use it right away to **validate** and **consolidate** your knowledge. The 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! [![Open Source Society University - Computer Science](https://img.shields.io/badge/OSSU-computer--science-blue.svg)](https://github.com/open-source-society/computer-science) - Markdown: `[![Open Source Society University - Computer Science](https://img.shields.io/badge/OSSU-computer--science-blue.svg)](https://github.com/open-source-society/computer-science)` - HTML: `