References

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

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. Structure and Interpretation of Computer Programs - ⎙
4. Elements of Software Construction - ⎙
5. Introduction to Algorithms - ⎙
6. Design and Analysis of Algorithms - ⎙
7. Computer System Engineering - ⎙
8. Computer Language Engineering - ⎙
9. Great Ideas in Theoretical Computer Science - ⎙
10. Performance Engineering of Software Systems - ⎙
11. Engineering Innovation and Design - ⎙
12. Principles of Computer System Design: An Introduction - ⎙
13. How to Process, Analyze and Visualize Data - ⎙
14. Advanced Data Structures - ⎙
15. Advanced Algorithms - ⎙
16. 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. Automata, Computability, and Complexity - ⎙
2. Computational Biology: Genomes, Networks, Evolution - ⎙
3. Creating Video Games - ⎙
4. Computer Graphics - ⎙
5. User Interface Design and Implementation - ⎙
6. Making Sense of Data - ⎙
7. Data Science - ⎙