self-taught-CS-curriculum

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self-taught-CS-curriculum

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A self-taught CS curriculum

self-taught-CS-curriculum 최신버젼 다운로드

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## A self-taught B.SC in Computer Science, Economics, and Data Science curriculum
### Content
#### Objectives
#### 1. CS-Maths
#### 2. CS-Introduction
#### 3. CS-Foundations
#### 4. CS-Cores
#### 5. CS-Advanced
#### 6. CS-Tools
#### 7. Economics

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### Objectives: 
This is a self-taught curriculum was built by [Alan Nguyen](https://github.com/alan-nguyen) based on MIT and Stanford curriculums. After completing this curriculum, start applying for jobs, doing contract work, start your own data science consulting group, or just keep on learning. Remember to believe in your ability to learn. You can learn computer science and data science, the important thing is be focus, practice deep work and you will master the concepts, programming skills and data science techniques. Keep learning. **Life is a journey or learning.**

## 1. CS-Maths
- [ ] [Single Variable Calculus](https://ocw.mit.edu/courses/mathematics/18-01sc-single-variable-calculus-fall-2010/) - OCW - _Ongoing_
- [ ] [Linear Algebra](https://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/) - OCW - _Ongoing_
- [ ] [Mathemetics for Computer Science](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-spring-2015/) - OCW - _Ongoing_
- [ ] [Multi Variable Calculus](https://ocw.mit.edu/courses/mathematics/18-02sc-multivariable-calculus-fall-2010/) - OCW
- [ ] [Introduction to Probability](https://ocw.mit.edu/resources/res-6-012-introduction-to-probability-spring-2018/index.htm) - OCW - _Ongoing_

## 2. CS-Introduction
- [x] [Computer Science 101](https://lagunita.stanford.edu/courses/Engineering/CS101/Summer2014/course/) - Lagunita (Free Certificate)
- [x] [Introduction to Computer Science Programming in Python](https://courses.edx.org/courses/course-v1:MITx+6.00.1x+2T2019/course/) - OCW or Edx
- [ ] [Introduction to Computational Thinking](https://courses.edx.org/courses/course-v1:MITx+6.00.2x+1T2019/course/) - OCW or Edx
- [ ] [6.01 Introduction to EECS vis Robotics](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-01sc-introduction-to-electrical-engineering-and-computer-science-i-spring-2011/) - OCW

## 3. CS-Foundations
- [ ] [6.006 Introduction to Algorithms](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/) - OCW - _Ongoing_
- [ ] [6.004 Computation Structures](https://courses.edx.org/courses/course-v1:MITx+6.004.1x_3+3T2016/course/) - OCW or Edx
- [ ] [6.005 Software Construction](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-005-software-construction-spring-2016/) - OCW

## 4. CS-Cores
- [ ] [6.009 Fundamentals of Programming](https://www.edx.org/course/software-construction-java-mitx-6-005-1x) - Edx
- [ ] 6.031 Elements of Software Construction
- [ ] [6.033 Computer Systems Engineering](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-033-computer-system-engineering-spring-2018/) - OCW
- [ ] [6.046 Design and Analysis of Algorithms I](https://lagunita.stanford.edu/courses/course-v1:Engineering+Algorithms1+SelfPaced/about) - Lagunita (Free Certificate)
- [ ] [6.046 Design and Analysis of Algorithms II](https://lagunita.stanford.edu/courses/course-v1:Engineering+Algorithms2+SelfPaced/about) - Lagunita (Free Certificate)

**Optional**
- [ ] [6.045 Automata, Computability, and Complexity](https://lagunita.stanford.edu/courses/course-v1:ComputerScience+Automata+SelfPaced/about) - Lagunita

## 5. CS-Advanced
- [x] [Deep learning specialization](https://www.coursera.org/specializations/deep-learning?) - Coursera (June-2019)
- [ ] [Computer Vision](http://cs231n.stanford.edu/) - Stanford
- [ ] [Natural Language Processing](http://web.stanford.edu/class/cs224n/) - Stanford 
- [ ] [Mining massive data](https://lagunita.stanford.edu/courses/course-v1:ComputerScience+MMDS+SelfPaced/about) - Lagunita (Free Certificate)
- [ ] 6.034 Artificial Intelligence 
- [ ] [6.036 Introduction to Machine learning](https://www.coursera.org/learn/machine-learning) - Coursera 
## 6. CS-Tools
- [x] [Python - DataCamp](https://www.datacamp.com/home)
- [x] [SQL - DataCamp](https://www.datacamp.com/home)
- [ ] [R - DataCamp](https://www.datacamp.com/home) - _Ongoing_
- [ ] [JavaScript - freecodecamp](https://www.freecodecamp.org/)
- [ ] [Java - Codecademy](https://www.codecademy.com/learn/learn-java)

## 7. Economics
- [ ] [14.01 Principles of Microeconomics](https://ocw.mit.edu/courses/economics/14-01sc-principles-of-microeconomics-fall-2011/) - OCW
- [x] [Principles of Economics](https://lagunita.stanford.edu/courses/course-v1:HumanitiesSciences+Econ1V+Summer2019/course/) - Lagunita ([Free Certificate](https://prod-cert-bucket.s3.amazonaws.com/downloads/a02021d3d9d04fdb8668c072464bd76d/Statement.pdf))
- [ ] [Econometric Data Science](https://ocw.mit.edu/courses/economics/14-32-econometrics-spring-2007/) - OCW
- [ ] Elective Subjects

**Economics Electives**

Select **three** of the following, including _at least one subject from each group_:

_Data Science_
- 14.20 Industrial Organization and Competitive Strategy
- 14.27 Economics and E-Commerce
- 14.36 Advanced Econometrics
- 14.41 Public Finance and Public Policy
- 14.64 Labor Economics and Public Policy
- 14.74 Foundations of Development Policy
- 14.75 Political Economy and Economic Development
- 15.780 Stochastic Models in Business Analytics

_Theory_
- 14.04 Intermediate Microeconomic Theory
- 14.12 Economic Applications of Game Theory
- 14.13 Psychology and Economics
- 14.15[J] Networks
- 14.16 Strategy and Information
- 14.19 Market Design
- 14.26 Economics of Incentives: Theory and Applications
- 14.54 International Trade

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