References

• MIT Open Courseware
• Google - Guide for Technical Development
• Coursera
• How can I prepare myself to be a software engineer?

Tips

If you want to follow this path, here are some tips! Share your tips with us too!

Topics

• Computer Science
• Software Testing
• Math
• Operating Systems
• Networks
• Databases
• Cryptography
• Compilers
• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• Robotics
• Graphs
• Data Mining
• Parallel Programming
• Programming Languages
• Others

Computer Science

1. Introduction to Computer Science and Programming ✍ - ⎙
2. Computer Science 101 - Stanford / Other files - ⎙
3. Systematic Program Design - Part 1: The Core Method
4. Systematic Program Design - Part 2: Arbitrary Sized Data
5. Systematic Program Design - Part 3: Arbitrary Sized Data
6. Fundamentals of Computing
   1. An Introduction to Interactive Programming in Python (Part 1) - ⎙
   2. An Introduction to Interactive Programming in Python (Part 2) - ⎙
   3. Principles of Computing (Part 1) - ⎙
   4. Principles of Computing (Part 2) - ⎙
   5. Algorithmic Thinking (Part 1) - ⎙
   6. Algorithmic Thinking (Part 2) - ⎙
7. Structure and Interpretation of Computer Programs - ⎙
8. Elements of Software Construction - ⎙
9. Introduction to Algorithms - ⎙
10. Design and Analysis of Algorithms - ⎙
11. Paradigms of Computer Programming – Fundamentals - ⎙
12. Paradigms of Computer Programming – Abstraction and Concurrency - ⎙
13. Computer System Engineering - ⎙
14. Computer Language Engineering - ⎙
15. Great Ideas in Theoretical Computer Science - ⎙
16. Performance Engineering of Software Systems - ⎙
17. Engineering Innovation and Design - ⎙
18. Principles of Computer System Design: An Introduction - ⎙
19. How to Process, Analyze and Visualize Data - ⎙
20. Advanced Data Structures - ⎙
21. Advanced Algorithms - ⎙
22. Distributed Algorithms - ⎙

Software Testing

1. Software Testing - ⎙
2. Software Debugging - ⎙

Math

1. Mathematics for Computer Science - ⎙
2. Introduction to Logic - ⎙
3. Linear Algebra - ⎙
4. Coding the Matrix: Linear Algebra through Computer Science Applications - ⎙
5. Calculus One - ⎙
6. Calculus Two - ⎙
7. Linear and Discrete Optimization - ⎙
8. Probabilistic Graphical Models - ⎙
9. Game Theory - ⎙
10. Statistics One - ⎙
11. AP Statistics - ⎙

Operating Systems

1. Operating System Engineering - ⎙
2. Operating Systems and System Programming - ⎙

Networks

1. Networks - ⎙
2. Network and Computer Security - ⎙
3. Network Optimization - ⎙

Databases

1. Database Systems - ⎙
2. Database, Internet, and Systems Integration Technologies - ⎙

Cryptography

1. Cryptography I - ⎙
2. Applied Cryptography - ⎙

Compilers

1. Compilers - ⎙

Artificial Intelligence

1. Artificial Intelligence - ⎙

Machine Learning

1. Practical Machine Learning - ⎙
2. Machine Learning - ⎙
3. Neural Networks for Machine Learning - ⎙

Natural Language Processing

1. Natural Language Processing - ⎙
2. Natural Language Processing - ⎙

Robotics

Graphs

Data Mining

1. Data Mining - ⎙

Parallel Programming

1. Parallel Computing - ⎙
2. Heterogeneous Parallel Programming - ⎙

Programming Languages

1. Practical Programming in C - ⎙
2. Introduction to C Memory Management and C++ Object-Oriented Programming - ⎙
3. Effective Programming in C and C++ - ⎙

Others

1. Introduction to Functional Programming
2. Engineering Software as a Service
3. Engineering Software as a Service, Part 2
4. Automata, Computability, and Complexity - ⎙
5. Computational Biology: Genomes, Networks, Evolution - ⎙
6. Creating Video Games - ⎙
7. Computer Graphics - ⎙
8. User Interface Design and Implementation - ⎙
9. Making Sense of Data - ⎙
10. Data Science - ⎙