Open Source Society University
Path to a self-taught education in Computer Science!
# Contents
- [Summary](#summary)
- [Curriculum](#curriculum)
- [Prerequisites](#prerequisites)
- [Intro CS](#intro-cs)
- [Core CS](#core-cs)
- [Advanced CS](#advanced-cs)
- [Final project](#final-project)
- [Pro CS](#pro-cs)
- [Community](#community)
- [How to show your progress](#how-to-show-your-progress)
- [Team](#team)
- [References](#references)
# Summary
The OSSU curriculum is a **complete education in computer science** using online materials.
It's not merely for career training or professional development.
It's for those who want a proper, *well-rounded* grounding in concepts fundamental to all computing disciplines,
and for those who have the discipline, will, and (most importantly!) good habits to obtain this education largely on their own,
but with support from a worldwide community of fellow learners.
It is designed according to the degree requirements of undergraduate computer science majors, minus general education (non-CS) requirements,
as it is assumed most of the people following this curriculum are already educated outside the field of CS.
The courses themselves are among the very best in the world, often coming from Harvard, Princeton, MIT, etc.,
but specifically chosen to meet the following criteria.
**Courses must**:
- Be open for enrollment
- Run regularly (ideally in self-paced format, otherwise running at least once a month or so)
- Fulfill the [academic requirements](REQUIREMENTS.md) of OSSU
- Fit neatly into the progression of the curriculum with respect to topics and difficulty level
- Be of generally high quality in teaching materials and pedagogical principles
When no course meets the above criteria, the coursework is supplemented with a book.
When there are courses or books that don't fit into the curriculum but are otherwise of high quality,
they belong in [extras/courses](extras/courses.md) or [extras/readings](extras/readings.md).
**Organization**. The curriculum is designed as follows:
- *Intro CS*: for students to try out CS and see if it's right for them
- *Core CS*: corresponds roughly to the first three years of a computer science curriculum, taking classes that all majors would be required to take
- *Advanced CS*: corresponds roughly to the final year of a computer science curriculum, taking electives according to the student's interests
- *Final Project*: a project for students to validate, consolidate, and display their knowledge, to be evaluated by their peers worldwide
- *Pro CS*: graduate-level specializations students can elect to take after completing the above curriculum if they want to maximize their chances of getting a good job
**Duration**. It is possible to finish Core CS within about 2 years if you plan carefully and devote roughly 18-22 hours/week to your studies.
Courses in Core CS should be taken linearly if possible, but since a perfectly linear progression is rarely possible,
each class's prerequisites is specified so that you can design a logical but non-linear progression
based on the class schedules and your own life plans.
**Cost**. All or nearly all course material prior to Pro CS is available for free,
however some courses may charge money for assignments/tests/projects to be graded.
Note that Coursera offers [financial aid](https://learner.coursera.help/hc/en-us/articles/209819033-Apply-for-Financial-Aid).
Decide how much or how little to spend based on your own time and budget;
just remember that you can't purchase success!
**Content policy**. If you plan on showing off some of your coursework publicly, you must share only files that you are allowed to.
*Do NOT disrespect the code of conduct* that you signed in the beginning of each course!
**How to contribute**. Please see [CONTRIBUTING](CONTRIBUTING.md).
# Curriculum
**Curriculum version**: `7.0` (see [CHANGELOG](CHANGELOG.md))
- [Prerequisites](#prerequisites)
- [Intro CS](#intro-cs)
- [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 CS](#advanced-cs)
- [Advanced programming](#advanced-programming)
- [Advanced math](#advanced-math)
- [Advanced systems](#advanced-systems)
- [Advanced theory](#advanced-theory)
- [Advanced applications](#advanced-applications)
- [Final project](#final-project)
- [Pro CS](#pro-cs)
---
## Prerequisites
- [Core CS](#core-cs) assumes the student has already taken high school math and physics, including algebra, geometry, and pre-calculus.
Some high school graduates will have already taken AP Calculus, but this is usually only about 3/4 of a college calculus class, so the calculus courses in the curriculum are still recommended.
- [Advanced CS](#advanced-cs) assumes the student has already taken the entirety of Core CS
and is knowledgeable enough now to decide which electives to take.
- Note that [Advanced systems](#advanced-systems) assumes the student has taken a basic physics course (e.g. AP Physics in high school).
## Intro CS
Use the first course, CS50, to determine if Computer Science is right for you.
Only proceed in the curriculum if it really excites you.
Feel free to use the second and third **optional** courses to gain the fundamental skills you need to excel at teaching yourself Computer Science;
they are very lightweight courses so it's suggested take them concurrently with another course.
**Topics covered**:
imperative programming;
procedural programming;
C;
manual memory management;
basic data structures and algorithms;
basic Python;
SQL;
basic HTML, CSS, JavaScript;
and more.
Courses | Duration | Effort | Prerequisites
:-- | :--: | :--: | :--:
[Introduction to Computer Science - CS50](https://www.edx.org/course/introduction-computer-science-harvardx-cs50x#!) ([alt](https://cs50.harvard.edu/)) | 12 weeks | 10-20 hours/week | none
[Learning How to Learn](https://www.coursera.org/learn/learning-how-to-learn) | 4 weeks | 2 hours/week | none
[Effective Thinking Through Mathematics](https://www.edx.org/course/effective-thinking-through-mathematics-utaustinx-ut-9-01x-0) | 4 weeks | 2 hours/week | none
## Core CS
All coursework under Core CS is **required**, unless otherwise indicated.
### Core programming
**Topics covered**:
functional programming;
design for testing;
program requirements;
common design patterns;
unit testing;
object-oriented design;
Java;
static typing;
dynamic typing;
ML-family languages (via Standard ML);
Lisp-family languages (via Racket);
Ruby;
and more.
Courses | Duration | Effort | Prerequisites
:-- | :--: | :--: | :--:
[How to Code - Simple Data](https://www.edx.org/course/how-code-simple-data-ubcx-htc1x) | 7 weeks | 8-10 hours/week | none
[How to Code - Complex Data](https://www.edx.org/course/how-code-complex-data-ubcx-htc2x) | 6 weeks | 8-10 hours/week | How to Code: Simple Data
[Software Construction - Data Abstraction](https://www.edx.org/course/software-construction-data-abstraction-ubcx-softconst1x) | 6 weeks | 8-10 hours/week | How to Code - Complex Data
[Software Construction - Object-Oriented Design](https://www.edx.org/course/software-construction-object-oriented-ubcx-softconst2x) | 6 weeks | 8-10 hours/week | Software Construction - Data Abstraction
Programming Languages ([A](https://www.coursera.org/learn/programming-languages), [B](https://www.coursera.org/learn/programming-languages-part-b), [C](https://www.coursera.org/learn/programming-languages-part-c)) | 10 weeks | 8-16 hours/week | recommended: C and Java
#### Readings
- **Required** to learn about monads, laziness, purity: [Learn You a Haskell for a Great Good!](http://learnyouahaskell.com/)
- **Required**, to learn about logic programming, backtracking, unification, any resource on Prolog covering these topics, such as:
- [Prolog Programming for Artificial Intelligence](https://www.amazon.com/Programming-Artificial-Intelligence-International-Computer/dp/0321417461)
- [Learn Prolog Now](http://www.learnprolognow.org/)
- [Art of Prolog](https://mitpress.mit.edu/books/art-prolog)
### Core math
**Topics covered**:
linear transformations;
matrices;
vectors;
mathematical proofs;
number theory;
differential calculus;
integral calculus;
sequences and series;
discrete mathematics;
basic statistics;
O-notation;
graph theory;
vector calculus;
discrete probability;
and more.
Courses | Duration | Effort | Prerequisites
:-- | :--: | :--: | :--:
[Linear Algebra - Foundations to Frontiers](https://www.edx.org/course/linear-algebra-foundations-frontiers-utaustinx-ut-5-04x#!) ([alt](http://ulaff.net/)) | 15 weeks | 8 hours/week | pre-calculus
[Calculus One](https://www.coursera.org/learn/calculus1) ([alt](https://mooculus.osu.edu/)) | 16 weeks | 8-10 hours/week | pre-calculus
[Calculus Two: Sequences and Series](https://www.coursera.org/learn/advanced-calculus)| 7 weeks | 9-10 hours/week | Calculus One
[Mathematics for Computer Science](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-spring-2015/index.htm) | 13 weeks | 5 hours/week | single variable calculus (Calculus Two)
### Core systems
**Topics covered**:
boolean algebra;
gate logic;
memory;
machine language;
computer architecture;
assembly;
machine language;
virtual machines;
high-level languages;
compilers;
operating systems;
network protocols;
and more.
Courses | Duration | Effort | Prerequisites
:-- | :--: | :--: | :--:
[Build a Modern Computer from First Principles: From Nand to Tetris](https://www.coursera.org/learn/build-a-computer) ([alt](http://www.nand2tetris.org/)) | 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 | From Nand to Tetris Part I
[Introduction to Computer Networking](https://lagunita.stanford.edu/courses/Engineering/Networking-SP/SelfPaced/about)| - | 4–12 hours/week | algebra, probability, basic CS
#### Readings
- **Required** since Nand2Tetris does not go deep enough into operating systems: [Operating Systems: Three Easy Pieces](http://pages.cs.wisc.edu/~remzi/OSTEP/)
### Core theory
**Topics covered**:
divide and conquer;
sorting and searching;
randomized algorithms;
graph search;
shortest paths;
data structures;
greedy algorithms;
minimum spanning trees;
dynamic programming;
NP-completeness;
and more.
Courses | Duration | Effort | Prerequisites
:-- | :--: | :--: | :--:
[Algorithms (1/4)](https://www.coursera.org/learn/algorithms-divide-conquer) | 4 weeks | 4-8 hours/week | one programming language, proofs, probability
[Algorithms (2/4)](https://www.coursera.org/learn/algorithms-graphs-data-structures) | 4 weeks | 4-8 hours/week | Algorithms (1/4)
[Algorithms (3/4)](https://www.coursera.org/learn/algorithms-greedy) | 4 weeks | 4-8 hours/week | Algorithms (2/4)
[Algorithms (4/4)](https://www.coursera.org/learn/algorithms-npcomplete) | 4 weeks | 4-8 hours/week | Algorithms (3/4)
### Core applications
**Topics covered**:
Agile methodology;
REST;
software specifications;
refactoring;
relational databases;
transaction processing;
data modeling;
neural networks;
supervised learning;
unsupervised learning;
OpenGL;
raytracing;
block ciphers;
authentication;
public key encryption;
and more.
Courses | Duration | Effort | Prerequisites
:-- | :--: | :--: | :--:
[Databases](https://lagunita.stanford.edu/courses/DB/2014/SelfPaced/about)| 12 weeks | 8-12 hours/week | some programming, basic CS
[Machine Learning](https://www.coursera.org/learn/machine-learning)| 11 weeks | 4-6 hours/week | linear algebra
[Computer Graphics](https://www.edx.org/course/computer-graphics-uc-san-diegox-cse167x)| 6 weeks | 12 hours/week | C++ or Java, linear algebra
[Cryptography I](https://www.coursera.org/course/crypto)| 6 weeks | 5-7 hours/week | linear algebra, probability
[Software Engineering: Introduction](https://www.edx.org/course/software-engineering-introduction-ubcx-softeng1x) | 6 weeks | 8-10 hours/week | Software Construction - Object-Oriented Design
[Software Development Capstone Project](https://www.edx.org/course/software-development-capstone-project-ubcx-softengprjx) | 6-7 weeks | 8-10 hours/week | Software Engineering: Introduction
## Advanced CS
After completing **every required course** in Core CS, students should choose a subset of courses from Advanced CS based on interest.
Not every course from a subcategory needs to be taken.
But students should take *every* course that is relevant to the field they intend to go into.
The Advanced CS study should then end with one of the Specializations under [Advanced applications](#advanced-applications).
A Specialization's Capstone, if taken, may act as the [Final project](#final-project), if permitted by the Honor Code of the course.
If not, or if a student chooses not to take the Capstone, then a separate Final project will need to be done to complete this curriculum.
### Advanced programming
**Topics covered**:
debugging theory and practice;
goal-oriented programming;
GPU programming;
CUDA;
parallel computing;
object-oriented analysis and design;
UML;
large-scale software architecture and design;
and more.
Courses | Duration | Effort | Prerequisites
:-- | :--: | :--: | :--:
[Compilers](https://lagunita.stanford.edu/courses/Engineering/Compilers/Fall2014/about)| 9 weeks | 6-8 hours/week | none
[Software Debugging](https://www.udacity.com/course/software-debugging--cs259)| 8 weeks | 6 hours/week | Python, object-oriented programming
[LAFF: Programming for Correctness](https://www.edx.org/course/laff-programming-correctness-utaustinx-ut-p4c-14-01x) | 7 weeks | 6 hours/week | linear algebra
[Introduction to Parallel Programming](https://www.udacity.com/course/intro-to-parallel-programming--cs344) | 12 weeks | - | C, algorithms
[Software Architecture & Design](https://www.udacity.com/course/software-architecture-design--ud821)| 8 weeks | 6 hours/week | software engineering in Java
### Advanced math
**Topics covered**:
real analysis;
multivariable integrals;
multivariable differentials;
probability theory;
and more.
Courses | Duration | Effort | Prerequisites
:-- | :--: | :--: | :--:
[Introduction to Mathematical Thinking](https://www.coursera.org/learn/mathematical-thinking) | 10 weeks | 10 hours/week | pre-calculus
[Multivariable Calculus](https://ocw.mit.edu/courses/mathematics/18-02sc-multivariable-calculus-fall-2010/index.htm) | 13 weeks | 12 hours/week | single-variable calculus (Calculus Two)
[Introduction to Probability - The Science of Uncertainty](https://www.edx.org/course/introduction-probability-science-mitx-6-041x-2) | 18 weeks | 12 hours/week | single- and multi-variable calculus
### Advanced systems
**Topics covered**:
digital signaling;
combinational logic;
CMOS technologies;
sequential logic;
finite state machines;
processor instruction sets;
caches;
pipelining;
virtualization;
parallel processing;
virtual memory;
synchronization primitives;
system call interface;
and more.
Courses | Duration | Effort | Prerequisites
:-- | :--: | :--: | :--:
[Electricity and Magnetism, Part 1](https://www.edx.org/course/electricity-magnetism-part-1-ricex-phys102-1x-0)1 | 7 weeks | 8-10 hours/week | calculus, basic mechanics
[Electricity and Magnetism, Part 2](https://www.edx.org/course/electricity-magnetism-part-2-ricex-phys102-2x-0) | 7 weeks | 8-10 hours/week | Electricity and Magnetism, Part 1
[Computation Structures 1: Digital Circuits](https://www.edx.org/course/computation-structures-part-1-digital-mitx-6-004-1x-0)2 | 10 weeks | 6 hours/week | electricity, magnetism
[Computation Structures 2: Computer Architecture](https://www.edx.org/course/computation-structures-2-computer-mitx-6-004-2x) | 10 weeks | 6 hours/week | Computation Structures 1
[Computation Structures 3: Computer Organization](https://www.edx.org/course/computation-structures-3-computer-mitx-6-004-3x-0) | 10 weeks | 6 hours/week | Computation Structures 2
[ops-class.org - Hack the Kernel](https://www.ops-class.org/) | 15 weeks | 6 hours/week | algorithms
**1 Note**:
These courses assume knowledge of basic physics;
if you are struggling, you can find a physics MOOC or utilize the materials from Khan Academy:
[Khan Academy - Physics](https://www.khanacademy.org/science/physics)
**2 Note**:
A shorter, less intense (but still very advanced) alternative to Computation Structures is here:
[Computer Architecture](https://www.coursera.org/learn/comparch)
### Advanced theory
**Topics covered**:
formal languages;
Turing machines;
computability;
computational geometry theory;
propositional logic;
relational logic;
Herbrand logic;
concept lattices;
game trees;
and more.
Courses | Duration | Effort | Prerequisites
:-- | :--: | :--: | :--:
[Introduction to Logic](https://www.coursera.org/learn/logic-introduction) | 10 weeks | 4-8 hours/week | set theory
[Automata Theory](https://lagunita.stanford.edu/courses/course-v1:ComputerScience+Automata+Fall2016/about) | 8 weeks | 10 hours/week | discrete mathematics, logic, algorithms
[Computational Geometry](https://www.edx.org/course/computational-geometry-tsinghuax-70240183x) | 16 weeks | 8 hours/week | algorithms, C++
[Introduction to Formal Concept Analysis](https://www.coursera.org/learn/formal-concept-analysis) | 6 weeks | 4-6 hours/week | logic, probability
[Game Theory](https://www.coursera.org/learn/game-theory-1) | 8 weeks | x hours/week | mathematical thinking, probability, calculus
### Advanced applications
These Coursera Specializations all end with a Capstone project.
Depending on the course, you may be able to utilize the Capstone as your Final Project for this Computer Science curriculum.
Note that doing a Specialization with the Capstone at the end always costs money.
So if you don't wish to spend money or use the Capstone as your Final, it may be possible to take the courses in the Specialization for free by manually searching for them, but not all allow this.
Courses | Duration | Effort | Prerequisites
:-- | :--: | :--: | :--:
[Robotics (Specialization)](https://www.coursera.org/specializations/robotics) | 26 weeks | 2-5 hours/week | linear algebra, calculus, programming, probability
[Data Mining (Specialization)](https://www.coursera.org/specializations/data-mining) | 30 weeks | 2-5 hours/week | machine learning
[Big Data (Specialization)](https://www.coursera.org/specializations/big-data) | 30 weeks | 3-5 hours/week | none
[Internet of Things (Specialization)](https://www.coursera.org/specializations/internet-of-things) | 30 weeks | 1-5 hours/week | strong programming
[Cloud Computing (Specialization)](https://www.coursera.org/specializations/cloud-computing) | 30 weeks | 2-6 hours/week | C++ programming
[Full Stack Web Development (Specialization)](https://www.coursera.org/specializations/full-stack) | 27 weeeks | 2-6 hours/week | programming, databases
[Data Science (Specialization)](https://www.coursera.org/specializations/jhu-data-science) | 43 weeks | 1-6 hours/week | none
## Final project
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.
After you've gotten through all of Core CS and the parts of Advanced CS relevant to you, you should think about a problem that you can solve using the knowledge you've acquired.
Not only does real project work look great on a resume, the project will *validate* and *consolidate* your knowledge.
You can create something entirely new, or you can find an existing project that needs help via websites like
[CodeTriage](https://www.codetriage.com/)
or
[First Timers Only](http://www.firsttimersonly.com/).
Another option is using the Capstone project from taking one of the Specializations in [Advanced applications](#advanced-applications);
whether or not this makes sense depends on the course, the project, and whether or not the course's Honor Code permits you to display your work publicly.
In some cases, it may not be permitted;
do **not** violate your course's Honor Code!
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: ``
### Evaluation
Upon completing your final project, submit your project's information to [PROJECTS](PROJECTS.md)
via a pull request and use our [community](#community) channels to announce it to your fellow students.
Your peers and mentors from OSSU will then informally evaluate your project.
You will not be "graded" in the traditional sense — everyone has their own measurements for what they consider a success.
The purpose of the evaluation is to act as your first announcement to the world that you are a computer scientist,
and to get experience listening to feedback — both positive and negative — and taking it in stride.
The final project evaluation has a second purpose: to evaluate whether OSSU,
through its community and curriculum, is successful in its mission to guide independent learners in obtaining a world-class computer science education.
### Cooperative work
You can create this project alone or with other students!
**We love cooperative work**!
Use our [channels](#community) 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 final project.
The important thing is to **internalize** the core concepts and to be able to use them with whatever tool (programming language) that you wish.
## Pro CS
After completing the requirements of the curriculum above, you will have completed the equivalent of a full bachelor's degree in Computer Science, or quite close to one.
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.
Many of these courses are graduate-level.
Choose one or more of the following **specializations**:
- [Mastering Software Development in R Specialization](https://www.coursera.org/specializations/r) by Johns Hopkins University
- [Artificial Intelligence Engineer Nanodegree](https://www.udacity.com/ai) by IBM, Amazon, and Didi
- [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
- [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**:
- edX: [xSeries](https://www.edx.org/xseries)
- Coursera: [Specializations](https://www.coursera.org/specializations)
- Udacity: [Nanodegree](https://www.udacity.com/nanodegree)
### Where to go next?
- Look for a job as a developer!
- Check out the [readings](extras/readings.md) for classic books you can read that will sharpen your skills and expand your knowledge.
- Join a local developer meetup (e.g. via [Meetups.com](https://www.meetups.com/)).
- Pay attention to emerging technologies in the world of software development, such as:
- [Elixir](http://elixir-lang.org/), a new functional programming language for the web based on the battle-tested BEAM VM!
- [Rust](https://www.rust-lang.org/), a systems language which uses borrowing and lifetimes to achieve memory safety without a garbage collector!
# Community
- Subscribe to [/r/opensourcesociety](https://www.reddit.com/r/opensourcesociety/)!
- Join us in our [group](https://groups.google.com/forum/#!forum/open-source-society-university)!
- You can also interact through [GitHub issues](https://github.com/open-source-society/computer-science/issues).
- 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)
- Add **Open Source Society University** to your [Facebook](https://www.facebook.com/ossuniversity) profile!
> **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**.
## How to 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*.
## Team
* **Curriculum Founder**: [Eric Douglas](https://github.com/ericdouglas)
* **Curriculum Maintainer**: [Eric Douglas](https://github.com/ericdouglas)
* **Contributors**: [contributors](https://github.com/open-source-society/computer-science/graphs/contributors)
# References
- [Google - Guide for Technical Development](https://www.google.com/about/careers/students/guide-to-technical-development.html)
- [Coursera](https://www.coursera.org/)
- [edX](https://www.edx.org)
- [Udacity](https://www.udacity.com/)
- [Stanford University](https://lagunita.stanford.edu/)
- [Carnegie Mellon University: Computer Science Major Requirements](https://www.csd.cs.cmu.edu/academics/undergraduate/requirements)
- [MIT Open Courseware](http://ocw.mit.edu/courses/#electrical-engineering-and-computer-science)
- [Teach Yourself Computer Science](https://teachyourselfcs.com/)
- [Obtaining a Thorough CS Background Online](http://spin.atomicobject.com/2015/05/15/obtaining-thorough-cs-background-online/)