rocio01 / osu-cs

open source university computer science degree syllabus

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NB This curriculum is my modified version of the OSSU and Open Source Computer Degree syllabus with extra MIT OCW EECS courses.

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Standing on the shoulders of giants

Your path to a Open Source Computer Science Degree

This is a curated list of free courses from reputable universities like MIT, Stanford, and Princeton that satisfy the same requirements as an undergraduate Computer Science degree, minus general education.

Summary

The OSU curriculum is a complete education in computer science using online materials initially based on the OSSU, MIT OCW and OSCS syllabi. As per the OSSU's mandate, "The courses themselves are among the very best in the world, often coming from Harvard, Princeton, MIT, etc.". The aim being to take the best elements of these open source courses in order to provide the framework for a comprehensive Computer Science Degree syllabus.

Please note, this syllabus is initially forked from the OSSU and Forrest Knight OSCS syllabuses. I've taken what I consider to be the best elements of both and have added other courses from the MIT OCW courselist in alignment with my own interests e.g I replaced the Duke/ San Diego Java courses with MIT courses in Java, C and C++, based on my own interests. (Please note git commit messages for things I have changed and updated - at the moment I'm tweaking courses as I go through the syllabus i.e. I add new courses or swap courses if I feel there is a better or more pertinent version of a course eg swapping out University of San Diego's course on computer graphics for MIT's course or adding extra game development courses from MIT (including OCW notes as well as the lecture video's usually linked to both MIT OCW site and Youtube)).

After having sampled a good chunk of the courses from the afore-mentioned institutions, I ascertained that the content and structure of the MIT courses were the best fit for my educational needs. This was time consuming in the short-run but overall makes sure my time and effort is directed properly.

As such, these following courses/ modules are pertinent to how I want to shape my own syllabus. For example, I am particularly interested in AI, neuroscience, computational biology and mathematics in relation to the study of computer science. Where applicable, the MIT courses are done via edx etc, but please note that not all courses are certificate eligible (as they are sourced from the MIT OCW directory directly).

I've also looked at the 6-3 MIT catalog and the general MIT OCW course list and also this list in order to put together a curriculum that reflects my interests and educational needs (hence a lot of the graduate courses towards the end of the syllabus).

One way to appraoch this OSU Computer Science Degree syllabus, which I highly recommend, is to do what I have done and use an existing open source syllabus (or that of the OSSU or OSCS) that satisfies the demands of a full degree, and create your own Open Source Degree Syllabus and tailor it to your own needs! Refer to Course Info for general details on what is involved in the syllabus.

Introduction to Computer Science

Why study Computer Science?

Title	Overview
What Do computer Scientists Do	(from lecture 26 that goes over what Computer Scientists do and what careers CS students can go into.)
Intro lecture to Computer Science	MIT

Computer Science Basics

Courses	School	Duration	Effort	Frequency	Notes
Computer Science 101	Stanford	-	-	self-paced	introductory course
Intro to Computer Science	Harvard	10 weeks	10-20 hours/week	self-paced	none
Introduction to Computer Science and Programming using Python (Part 1) (alt)	MIT	9 weeks	15 hours/week	high school algebra
Computational thinking and problem solving	Penn	4 weeks	3-6 hours per week	self-paced	none

CS50 Extension Courses

Courses	School	Duration	Effort	Notes
Beyond CS50	Harvardx	1 week	11 lectures	CS50
CS50 Web Programming with Python and Javascript	Harvardx	12 weeks	6-9 hours per week	CS50
CS50 Game Development	Harvardx	12 weeks	6-9 hours per week	CS50
CS50 Mobile Development with React-Native	Harvardx	12 week/11 lectures	6-9 hours per week	CS50

Core Programming

Courses	School	Duration	Effort	Frequency	Notes
Introduction to Computational thinking and data science (Part 2) (alt)	MIT	9 weeks	15 hours/week	high school algebra
Introduction to Programming in Java	MIT	4 weeks	2 sessions per week	1 hour per session	Video Lectures, MIT lecture notes
Introduction to C and C++	MIT	4 weeks	2 sessions per week	1 hour per session	none
Effective programming in C and C++	MIT	10 lectures	3 sessions per week	2 hours per session
Practical programming in C	MIT weeks	3 weeks	5 sessions per week	90 mins per session	none

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
• Required, to learn about logic programming, backtracking, unification: Learn Prolog Now!

GIR/ AUS courses

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 These courses are part of my General Institute Requirements (GIRs) and Advanced Undergraduate Subjects (AUS)

Courses	School	Duration	Effort	Frequency	Notes
Reverse Engineering the Mind: Brain and Cognitive sciences (panel discussion)	MIT	80 mins	1 session	Panel discussion	none
Introduction to Computational Neuroscience	MIT	26 lectures	8 problem sets	self-paced	Basic biology, chemistry, and physics. intro/ review YT playlist
Foundations of Computational and Systems Biology	MIT	22 sessions	80 mins per session	self-paced	(AUS) see MIT OCW for additional paperwork/ coursework
Computation Structures	MIT	26 sessions	45-60 mins per session	self-paced	(GIR) YT playlistCircuits and Electronics
Automata, Computability and Complexity	MIT	23 sessions	90 mins per session	self-paced	none
Theory of Computation	MIT	6 assignments	-	self-paced	graduate level
Mathematics for Computer Science	MIT 1	13 weeks	5 hours/week	Self-paced	Calculus 1C
Calculus 1A: Differentiation	MIT	12 weeks	6-10 hours/week	self-paced	pre-calculus
Calculus 1B: Integration	MIT	15 weeks	6-10 hours/week	self-paced	Calculus 1A: Differentiation
Calculus 1C: Coordinate Systems & Infinite Series	MIT	8 weeks	6-10 hours/week	self-paced	Calculus 1B: Integration

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

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.

Theory

Courses	School	Duration	Effort	Frequency	Notes
Computer Science: Algorithms, Theory, and Machines	Princeton	10 weeks	2-5 hours/week	once a month	Calculus 1A (all), basic programming
Multicore Programming Primer	MIT	31 sessions	self-paced	-	MIT OCW lecture notes, YT video lectures
Algorithms, Part I	Princeton	6 weeks	6-12 hours/week	once a month	Computer Science: Algorithms, Theory, and Machines
Algorithms, Part II	Princeton	6 weeks	6-12 hours/week	once a month	Algorithms, Part I

Applications

Courses	School	Duration	Effort	Notes
Databases	Stanford	12 weeks	8-12 hours/week	some programming, basic CS
Machine Learning	Stanford	11 weeks	4-6 hours/week	linear algebra
Creating Video Games	MIT	45 sessions	self-paced	MIT OCW, YT video lectures
Computer Graphics	MIT	self-paced	self-paced	video lectures/ MIT OCW lecture notes / C++ or Java, linear algebra
Cryptography I	Stanford	6 weeks	5-7 hours/week	linear algebra, probability

Auxiliary CS courses

Courses	School	Duration	Effort	Frequency	Notes
Linux Command Line Basics	Udacity	1 week	5 hours/week	self-paced	none
The Unix Workbench	JHU	4 weeks	4 hours/week	once a month	none
Automated Software Testing(Alt)	TUDelft	5 weeks	3-5 hours per week	self-paced	none
UML Class Diagrams for Software Engineering	Ku Leuven	3 weeks	4-5 hours per week	self-paced	none
Kotlin for Java developers	Jetbrains	19 hours	7 hours per week	self-paced	Java basics
Building Scalable Java Microservices with Spring Boot and Spring Cloud	Google Cloud	15 hours	6-8 hours per week	self-paced	Basic Java
Parallel, Concurrent, and Distributed Programming in Java Specialization	Rice	2 months	10 hours per week	self-paced	Basic Java

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 goal-oriented programming GPU programming CUDA parallel computing object-oriented analysis and design UML large-scale software architecture and design and more

Courses	School	Duration	Effort	Notes
Compilers	Stanford	9 weeks	6-8 hours/week	none
Software Debugging	Udacity	8 weeks	6 hours/week	Python, object-oriented programming
Software Testing	Udacity	4 weeks	6 hours/week	Python, programming experience
LAFF - On Programming for Correctness	UT of Austin	7 weeks	6 hours/week	linear algebra
Introduction to Parallel Programming (alt)	Udacity	12 weeks	-	C, algorithms
Software Architecture & Design	Udacity	8 weeks	6 hours/week	software engineering in Java
Building scalable Java Microservices	Google Cloud	3 weeks	6-8 hours per week	Prior Java experience

Math pre-requisites

Courses	School	Duration	Effort	Frequency	Notes
Introduction to Mathemetical Thinking	Stanford	9 Weeks	23 hours	Self-paced
Precalculus	ASU	15 weeks	9-10 hours per week	-	high school math
Essence of Calculus	YT	-	-	-	high school math
Essence of Linear Algebra	YT	-	-	-	pre-calculus
Linear Algebra - Foundations to Frontiers (alt)	UT of Austin	15 weeks	8 hours/week	-	Essence of Linear Algebra
Mathematical Thinking in Computer Science	UC San Diego	6 weeks	2-5 hours/week	once a month	none

Advanced math

Topics covered: parametric equations polar coordinate systems multivariable integrals multivariable differentials probability theory and more

Courses	School	Duration	Effort	Notes
Multivariable Calculus	MIT	13 weeks	12 hours/week	MIT Calculus 1C
Introduction to Probability - The Science of Uncertainty	MIT	18 weeks	12 hours/week	Multivariable Calculus

Advanced theory

Topics covered: formal languages Turing machines computability event-driven 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	School	Duration	Effort	Notes
Introduction to Logic	Stanford	10 weeks	4-8 hours/week	set theory
Automata Theory	Stanford	7 weeks	10 hours/week	discrete mathematics, logic, algorithms
Computational Geometry	TsingHau University	16 weeks	8 hours/week	algorithms, C++
Introduction to Formal Concept Analysis	NRU o HE	6 weeks	4-6 hours/week	logic, probability
Game Theory	Stanford	8 weeks	x hours/week	mathematical thinking, probability, calculus

Specializations

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.

Refine this list once specialisation/ final year project is chosen NB: This list is a refelction of my own specialisation interests (please refer to EDX/ Coursera/ MIT OCW for more specialisations)

AI/ ML route

Courses	Duration	Effort	Notes
Machine Learning (Specialisation)	8 months	7 hours per week	Data clustering algorithms, machine learning, classification algorithms
Deep Learning (Specialisation)	3 months	11 hours per week	tensorflow, convolutional neural network, artificial neural network, deep learning
Advanced Data Science with IBM (Specialisation)	2 months	14 hours per week	data science, IOT, deep learning, Apache Spark
IBM AI Engineering (Professional Certificate)	2 months	12 hours per week	data science, deep learning, artificial intelligence, machine learning
IBM Applied Engineering (Professional Certificate)	2 months	13 hours per week	data science, deep learning, artificial intelligence, machine learning, Watson AI
Advanced Machine Learning (Specialisation)	Flexible	set your own deadlines

Game dev/ Unity route

Courses	Duration	Effort	Notes
C# Programming for Unity Game Development (Specialisation)	5 months	6 hours per week	video game development, C#, Unity, game programming
Unity 3D Artist (Specialisation)	4 months	4 hours per week	asset creation and management, light, reflection and post processing effects, scene interactions, character setup and animation
Unity XR: How to build AR and VR apps	4 months	5 hours per week	xr, ar, vr, mr, mobile apps, hand-held ar

Blockchain route

Courses	Duration	Effort	Notes
Blockchain - University of California, Irvine	2 months	9 hours per week	Beginner level
Blockchain - University of Buffalo	2 months	12 hours per week	smart contract , ethereum , blockchains , solidity
Blockchain revolution for the enterprise	2 months	12 hours per week