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This list is a copy of ossu/computerscience with ranks
Open Source Society University
Path to a free selftaught education in Computer Science!
Contents
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, wellrounded 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 (nonCS) 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 selfpaced format, otherwise running at least once a month or so)
 Fulfill the academic requirements 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 or extras/readings.
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: graduatelevel 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 1822 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 nonlinear 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. 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.
Getting help. Please check our Frequently Asked Questions, and if you cannot find the answer, file an issue or talk to our friendly community!
Curriculum
Curriculum version: 8.0.0
(see CHANGELOG)
Prerequisites
 Core CS assumes the student has already taken high school math and physics, including algebra, geometry, and precalculus. 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 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 assumes the student has taken a basic physics course (e.g. AP Physics in high school).
Introduction to Computer Science
These courses will introduce you to the world of computer science. Both are required, but feel free to skip straight to the second course when CS50 (the first course) moves away from C. (Why?)
Topics covered:
imperative programming
procedural programming
C
manual memory management
basic data structures and algorithms
Python
SQL
basic HTML, CSS, JavaScript
and more
Courses  Duration  Effort  Prerequisites 

Introduction to Computer Science  CS50 (alt)  12 weeks  1020 hours/week  none 
Introduction to Computer Science and Programming using Python  9 weeks  15 hours/week  high school algebra 
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
objectoriented design
Java
static typing
dynamic typing
MLfamily languages (via Standard ML)
Lispfamily languages (via Racket)
Ruby
and more
Courses  Duration  Effort  Prerequisites 

How to Code  Simple Data  7 weeks  810 hours/week  none 
How to Code  Complex Data  6 weeks  810 hours/week  How to Code: Simple Data 
Software Construction  Data Abstraction  6 weeks  810 hours/week  How to Code  Complex Data 
Software Construction  ObjectOriented Design  6 weeks  810 hours/week  Software Construction  Data Abstraction 
Programming Languages, Part A  4 weeks  816 hours/week  recommended: Java, C 
Programming Languages, Part B  3 weeks  816 hours/week  Programming Languages, Part A 
Programming Languages, Part C  3 weeks  816 hours/week  Programming Languages, Part B 
Readings
 Required to learn about monads, laziness, purity: Learn You a Haskell for a Great Good!
 Note: probably the best resource to learn Haskell: Haskell Programming from First Principles
paid
 Note: probably the best resource to learn Haskell: Haskell Programming from First Principles
 Required, to learn about logic programming, backtracking, unification: Learn Prolog Now!
Core math
Topics covered:
linear transformations
matrices
vectors
mathematical proofs
number theory
differential calculus
integral calculus
sequences and series
discrete mathematics
basic statistics
Onotation
graph theory
vector calculus
discrete probability
and more
Courses  Duration  Effort  Prerequisites 

Essence of Linear Algebra      precalculus 
Linear Algebra  Foundations to Frontiers (alt)  15 weeks  8 hours/week  Essence of Linear Algebra 
Calculus 1A: Differentiation  13 weeks  610 hours/week  precalculus 
Calculus 1B: Integration  13 weeks  510 hours/week  Calculus 1A 
Calculus 1C: Coordinate Systems & Infinite Series  13 weeks  510 hours/week  Calculus 1B 
Mathematics for Computer Science^{1}  13 weeks  5 hours/week  Calculus 1C 
^{1}: Students struggling with MIT Math for CS can consider taking the Discrete Mathematics Specialization first. It is more interactive but less comprehensive, and it costs money to unlock full interactivity.
Core systems
Topics covered:
boolean algebra
gate logic
memory
computer architecture
assembly
machine language
virtual machines
highlevel languages
compilers
operating systems
network protocols
and more
Courses  Duration  Effort  Prerequisites 

Build a Modern Computer from First Principles: From Nand to Tetris (alt)  6 weeks  713 hours/week  none 
Build a Modern Computer from First Principles: Nand to Tetris Part II  6 weeks  1218 hours/week  one of these programming languages, From Nand to Tetris Part I 
Introduction to Computer Networking  8 weeks  4–12 hours/week  algebra, probability, basic CS 
opsclass.org  Hack the Kernel  15 weeks  6 hours/week  algorithms 
Readings
 Recommended: While Hack the Kernel recommends Modern Operating Systems as a textbook, we suggest using Operating Systems: Three Easy Pieces.
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
NPcompleteness
and more
Courses  Duration  Effort  Prerequisites 

Algorithms: Design and Analysis, Part I  8 weeks  48 hours/week  any programming language, Mathematics for Computer Science 
Algorithms: Design and Analysis, Part II  8 weeks  48 hours/week  Part I 
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  12 weeks  812 hours/week  some programming, basic CS 
Machine Learning  11 weeks  46 hours/week  linear algebra 
Computer Graphics  6 weeks  12 hours/week  C++ or Java, linear algebra 
Cryptography I  6 weeks  57 hours/week  linear algebra, probability 
Software Engineering: Introduction  6 weeks  810 hours/week  Software Construction  ObjectOriented Design 
Software Development Capstone Project  67 weeks  810 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. A Specialization’s Capstone, if taken, may act as the 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
goaloriented programming
GPU programming
CUDA
parallel computing
objectoriented analysis and design
UML
largescale software architecture and design
and more
Courses  Duration  Effort  Prerequisites 

Compilers  9 weeks  68 hours/week  none 
Software Debugging  8 weeks  6 hours/week  Python, objectoriented programming 
Software Testing  4 weeks  6 hours/week  Python, programming experience 
LAFF: Programming for Correctness  7 weeks  6 hours/week  linear algebra 
Introduction to Parallel Programming (alt)  12 weeks    C, algorithms 
Software Architecture & Design  8 weeks  6 hours/week  software engineering in Java 
Advanced math
Topics covered:
parametric equations
polar coordinate systems
multivariable integrals
multivariable differentials
probability theory
and more
Courses  Duration  Effort  Prerequisites 

Multivariable Calculus  13 weeks  12 hours/week  MIT Calculus 1C 
Introduction to Probability  The Science of Uncertainty  18 weeks  12 hours/week  Multivariable 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 

Reliable Distributed Systems, Part 1  5 weeks  5 hours/week  Scala, intermediate CS 
Reliable Distributed Systems, Part 2  5 weeks  5 hours/week  Part 1 
Electricity and Magnetism, Part 1^{1}  7 weeks  810 hours/week  calculus, basic mechanics 
Electricity and Magnetism, Part 2  7 weeks  810 hours/week  Electricity and Magnetism, Part 1 
Computation Structures 1: Digital Circuits  10 weeks  6 hours/week  electricity, magnetism 
Computation Structures 2: Computer Architecture  10 weeks  6 hours/week  Computation Structures 1 
Computation Structures 3: Computer Organization  10 weeks  6 hours/week  Computation Structures 2 
^{1} Note: These courses assume knowledge of basic physics. (Why?) If you are struggling, you can find a physics MOOC or utilize the materials from Khan Academy: Khan Academy  Physics
Advanced theory
Topics covered:
formal languages
Turing machines
computability
eventdriven concurrency
automata
distributed shared memory
consensus algorithms
state machine replication
computational geometry theory
propositional logic
relational logic
Herbrand logic
concept lattices
game trees
and more
Courses  Duration  Effort  Prerequisites 

Introduction to Logic  10 weeks  48 hours/week  set theory 
Automata Theory  8 weeks  10 hours/week  discrete mathematics, logic, algorithms 
Computational Geometry  16 weeks  8 hours/week  algorithms, C++ 
Introduction to Formal Concept Analysis  6 weeks  46 hours/week  logic, probability 
Game Theory  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)  26 weeks  25 hours/week  linear algebra, calculus, programming, probability 
Data Mining (Specialization)  30 weeks  25 hours/week  machine learning 
Big Data (Specialization)  30 weeks  35 hours/week  none 
Internet of Things (Specialization)  30 weeks  15 hours/week  strong programming 
Cloud Computing (Specialization)  30 weeks  26 hours/week  C++ programming 
Full Stack Web Development (Specialization)  27 weeks  26 hours/week  programming, databases 
Data Science (Specialization)  43 weeks  16 hours/week  none 
Functional Programming in Scala (Specialization)  29 weeks  45 hours/weeks  One year programming experience 
Final project
OSS University is projectfocused. 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 or First Timers Only.
Another option is using the Capstone project from taking one of the Specializations in 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 OSSUCS badge in the README of your repository! ★32702
 Markdown:
[![Open Source Society University  Computer Science](https://img.shields.io/badge/OSSUcomputerscienceblue.svg) ★32702](https://github.com/ossu/computerscience)
 HTML:
<a href="https://github.com/ossu/computerscience"><img alt="Open Source Society University  Computer Science" src="https://img.shields.io/badge/OSSUcomputerscienceblue.svg"></a>
Evaluation
Upon completing your final project, submit your project’s information to PROJECTS via a pull request and use our 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 worldclass computer science education.
Cooperative work
You can create this project alone or with other students! We love cooperative work! 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 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 in the Advanced CS section, but the next step to completing your studies is to develop skills and knowledge in a specific domain. Many of these courses are graduatelevel.
Choose one or more of the following specializations:
 Mastering Software Development in R Specialization by Johns Hopkins University
 Artificial Intelligence Engineer Nanodegree by IBM, Amazon, and Didi
 Machine Learning Engineer Nanodegree by kaggle
 Cybersecurity MicroMasters by the Rochester Institute of Technology
 Android Developer Nanodegree by Google
These aren’t the only specializations you can choose. Check the following websites for more options:
 edX: xSeries
 Coursera: Specializations
 Udacity: Nanodegree
Where to go next?
 Look for a job as a developer!
 Check out the readings for classic books you can read that will sharpen your skills and expand your knowledge.
 Join a local developer meetup (e.g. via meetup.com).
 Pay attention to emerging technologies in the world of software development:
 Explore the actor model through Elixir, a new functional programming language for the web based on the battletested Erlang Virtual Machine!
 Explore borrowing and lifetimes through Rust, a systems language which achieves memory and threadsafety without a garbage collector!
 Explore dependent type systems through Idris, a new Haskellinspired language with unprecedented support for typedriven development.
Code of conduct
Community
 Subscribe to our newsletter.
 Use our forum ★11 if you need some help.
 You can also interact through GitHub issues.
 We also have a chat room!
 Add Open Source Society University to your Linkedin 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 forum for important discussions.
How to show your progress
 Create an account in Trello.
 Copy this board to your personal account. See how to copy a board here.
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 Resources
: 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 theSection
label are only to help the organization of the Done column. You should put the Course’s cards below its respective Section’s card.
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 Founders: Eric Douglas
 Curriculum Maintainers: Eric Douglas and hanjiexi
 Contributors: contributors
References
 Google  Guide for Technical Development
 Coursera
 edX
 Udacity
 Stanford University
 Carnegie Mellon University: Computer Science Major Requirements
 MIT Open Courseware
 Teach Yourself Computer Science

Obtaining a Thorough CS Background Online
This list is a copy of ossu/computerscience with ranks