My Path to Computer Science Education and Curriculum.

For a clear representation, check out the OSSU GitHub repo.

iOS: [2017]

• Udacity: iOS Developer Nanodegree Plus Program ✔
• Udemy: “The Complete iOS 11 & Swift Developer Course” by Rob Percival ✔
• Udemy: “Complete iOS 11 developer Bootcamp” by Igneus Technologies ✔
• Udemy: “iOS 11 & Swift 4: From Beginner to Paid Professional” by Mark Price ✔
• Udemy: “iOS 11 & Swift 4 – The Complete iOS App Development Bootcamp” by Angela Yu ✔
• Udemy: “Complete iOS 11 Machine Learning Masterclass” by Yohann Taieb
• Udemy: “Mastering ARKit for iOS” by Mohammad Azam
• Stanford: “iPad and iPhone Application Development CS193P” by Paul Hegarty

Intro to CS: [2017]

• edX: “Introduction to Computer Science – CS50” by HarvardX ✔
• edX: “Introduction to Computer Science and Programming Using Python” by MIT ✔
• edX: “Introduction to Computational Thinking and Data Science” by MIT

Software Development MicroMasters Program: [2018] 

• edX: “How to Code – Simple Data” by The University of British Columbia
• edX: “How to Code – Complex Data” by The University of British Columbia
• edX: “Software Construction: Data Abstraction” by The University of British Columbia
• edX: “Software Construction: Object-Oriented Design” by The University of British Columbia
• edX: “Software Engineering: Introduction” by The University of British Columbia
• edX: “Software Development Capstone Project” by The University of British Columbia

Courses to Complete for Artificial Intelligence Career: [2018-2019]

• Coursera: “Machine Learning – Andrew Ng” by Stanford University

• Coursera: “Deep Learning Specialization” by deeplearning.ai Andrew Ng

Algorithms course for Google Career: [2019]

      STANFORD 4-PART ALGORITHMS COURSE

• Coursera: “Algorithms Part 1: Divide and Conquer, Sorting and Searching, and Randomized Algorithms” by Stanford University
• Coursera: “Algorithms Part 2: Graph Search, Shortest Paths, and Data Structures” by Stanford University
• Coursera: “Algorithms Part 3: Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming” by Stanford University
• Coursera: “Algorithms Part 4: Shortest Paths Revisited, NP-Complete Problems and What To Do About Them” by Stanford University

      PRINCETON 2-PART ALGORITHMS COURSE

• Coursera: “Algorithms Part 1” by Princeton University
• Coursera: “Algorithms Part 2” by Princeton University

Courses to Complete for CS Education: [2020]

• edX: “Programming Languages, Part A” by The University of Washington
• edX: “Programming Languages, Part B” by The University of Washington
• edX: “Programming Languages, Part C” by The University of Washington

• edX: “Linear Algebra – Foundations to Frontiers” by The University of Texas at Austin
• Coursera: “Calculus One” by The Ohio State University
• Coursera: “Calculus Two: Sequences and Series” by The Ohio State University
• MIT: “Mathematics for Computer Science” by MIT

• Coursera: “Build a Modern Computer from First Principles: From Nand to Tetris” by Hebrew University of Jerusalem
• Coursera: “Build a Modern Computer from First Principles: Nand to Tetris Part II by Hebrew University of Jerusalem
• Stanford: “Introduction to Computer Networking” by Stanford University

• Stanford:  “Databases” by Stanford University 
• edX: “Computer Graphics” by UCSanDiego
• Coursera: “Cryptography I” by Stanford University

• Stanford: “Compilers” by Stanford University
• Udacity: “Software Debugging” by Udacity
• edX: “LAFF – On Programming for Correctness” by The University of Texas at Austin
• Udacity: “Intro to Parallel Programming” by Udacity NVIDIA
• Udacity: “Software Architecture & Design” by Udacity Georgia Tech