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Thursday, 5 September 2019

computer science教程

a free self-taught education in Computer Science.

Open Source Society University

Path to a free self-taught 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, 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 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: 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. Decide how much or how little to spend based on your own time and budget; just remember that you can't purchase success!
Process. Students can work through the curriculum alone or in groups, in order or out of order.
  • For grouping up, please use the cohorts repository to find or create a cohort suited to you.
  • We recommend doing all courses in Core CS, only skipping a course when you are certain that you've already learned the material previously.
  • For simplicity, we recommend working through courses (especially Core CS) in order from top to bottom, as they have already been topologically sorted by their prerequisites.
  • Courses in Advanced CS are electives. Choose one subject (e.g. Advanced programming) you want to become an expert in, and take all the courses under that heading. You can also create your own custom subject, but we recommend getting validation from the community on the subject you choose.
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 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 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

This course will introduce you to the world of computer science.
Topics covered: computation imperative programming basic data structures and algorithms and more
Courses Duration Effort Prerequisites
Introduction to Computer Science and Programming using Python (alt) 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 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 7 weeks 8-10 hours/week none
How to Code - Complex Data 6 weeks 8-10 hours/week How to Code: Simple Data
Software Construction - Data Abstraction 6 weeks 8-10 hours/week How to Code - Complex Data
Software Construction - Object-Oriented Design 6 weeks 8-10 hours/week Software Construction - Data Abstraction
Programming Languages, Part A 4 weeks 8-16 hours/week recommended: Java, C
Programming Languages, Part B 3 weeks 8-16 hours/week Programming Languages, Part A
Programming Languages, Part C 3 weeks 8-16 hours/week Programming Languages, Part B

Readings

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
Essence of Linear Algebra - - pre-calculus
Linear Algebra - Foundations to Frontiers (alt) 15 weeks 8 hours/week Essence of Linear Algebra
Calculus 1A: Differentiation 13 weeks 6-10 hours/week pre-calculus
Calculus 1B: Integration 13 weeks 5-10 hours/week Calculus 1A
Calculus 1C: Coordinate Systems & Infinite Series 13 weeks 5-10 hours/week Calculus 1B
Mathematics for Computer Science1 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: procedural programming manual memory management boolean algebra gate logic memory computer architecture assembly machine language virtual machines high-level languages compilers operating systems network protocols and more
Courses Duration Effort Additional Text / Assignments Prerequisites
Introduction to Computer Science - CS50 (alt) 12 weeks 10-20 hours/week After the sections on C, skip to the next course. Why? introductory programming
Build a Modern Computer from First Principles: From Nand to Tetris (alt) 6 weeks 7-13 hours/week - C-like programming language
Build a Modern Computer from First Principles: Nand to Tetris Part II 6 weeks 12-18 hours/week - one of these programming languages, From Nand to Tetris Part I
Introduction to Computer Networking 8 weeks 4–12 hours/week Assignment 1
Assignment 2
Assignment 3
Assignment 4		 algebra, probability, basic CS
ops-class.org - Hack the Kernel 15 weeks 6 hours/week Replace course textbook with Operating Systems: Three Easy Pieces algorithms

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: Design and Analysis, Part I 8 weeks 4-8 hours/week any programming language, Mathematics for Computer Science
Algorithms: Design and Analysis, Part II 8 weeks 4-8 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 8-12 hours/week some programming, basic CS
Machine Learning 11 weeks 4-6 hours/week linear algebra
Computer Graphics 6 weeks 12 hours/week C++ or Java, linear algebra
Cryptography I 6 weeks 5-7 hours/week linear algebra, probability
Software Engineering: Introduction 6 weeks 8-10 hours/week Software Construction - Object-Oriented Design
Software Development Capstone Project 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. 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 Duration Effort Prerequisites
Compilers 9 weeks 6-8 hours/week none
Software Debugging 8 weeks 6 hours/week Python, object-oriented programming
Software Testing 4 weeks 6 hours/week Python, programming experience
LAFF - On 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 11 7 weeks 8-10 hours/week calculus, basic mechanics
Electricity and Magnetism, Part 2 7 weeks 8-10 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 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 Duration Effort Prerequisites
Introduction to Logic 10 weeks 4-8 hours/week set theory
Automata Theory 7 weeks 10 hours/week discrete mathematics, logic, algorithms
Computational Geometry 16 weeks 8 hours/week algorithms, C++
Introduction to Formal Concept Analysis 6 weeks 4-6 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 2-5 hours/week linear algebra, calculus, programming, probability
Data Mining (Specialization) 30 weeks 2-5 hours/week machine learning
Big Data (Specialization) 30 weeks 3-5 hours/week none
Internet of Things (Specialization) 30 weeks 1-5 hours/week strong programming
Cloud Computing (Specialization) 30 weeks 2-6 hours/week C++ programming
Full Stack Web Development (Specialization) 27 weeks 2-6 hours/week programming, databases
Data Science (Specialization) 43 weeks 1-6 hours/week none
Functional Programming in Scala (Specialization) 29 weeks 4-5 hours/week One year programming experience

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, but the project will also 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 OSSU-CS badge in the README of your repository! Open Source Society University - Computer Science
  • Markdown: [![Open Source Society University - Computer Science](https://img.shields.io/badge/OSSU-computer--science-blue.svg)](https://github.com/ossu/computer-science)
  • HTML: Open Source Society University - Computer Science

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 world-class 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 graduate-level.
Choose one or more of the following specializations:
These aren't the only specializations you can choose. Check the following websites for more options:

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 battle-tested Erlang Virtual Machine!
    • Explore borrowing and lifetimes through Rust, a systems language which achieves memory- and thread-safety without a garbage collector!
    • Explore dependent type systems through Idris, a new Haskell-inspired language with unprecedented support for type-driven development.
keep learning

Code of conduct

OSSU's code of conduct.

Community


from https://github.com/ossu/computer-science
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