Ok. So the thing in the back of my mind is to find the best way to master these skills most efficiently considering the limitations I have (full-time job, family obligations, etc.)

One possibility is to take some time off and do a full-time Bootcamp or a part-time one.

Another path is to enroll in a master’s degree.

All this time, I have been making a list of all the cool things I want to learn eventually. It is getting pretty long.

I just watched a video by Daniel Bourke, where he describes his path. Instead of spending tens of thousands of dollars on a rigid master’s degree that may not suit his interests or even thousands in a Bootcamp, he chose a different path.

He created a list of courses and books that together form the syllabus of the Daniel Bourke Masters Degree.

I completely agree with him. There are just so many good courses and resources online, some free and some with a small cost, that it makes a lot more sense to assemble your degree, especially if you are good at staying motivated and learning.

So, I have decided that this is going to be my path. I now present to you:

Rogelio Montemayor’s Master’s Degree in Data Science and Machine Learning:

  • Main Courses:
  • Foundations
    • CS50 on edX
    • DataCamp shell and git courses
  • Data Science / Machine Learning / AI
    • DataCamp Data Scientist With Python Track
    • DataCamp Machine Learning Scientist With Python Track
    • DataCamp Data Engineer with Python Track
    • DataCamp SQL Fundamentals Skill Track
    • DataCamp Python Programming Skill Track
    • HarvardX Data Science Professional Certificate on edX
    • Kaggle courses
    • Kaggle competitions (aim for expert status)
    • Elite Data Science Machine Learning Accelerator
    • Google Machine Learning Crash Course
    • Fast.ai Machine Learning Course
    • UM Applied Data Science with Python on Coursera
    • Andrew Ng’s Machine Learning on Coursera
    • Udacity Deep Learning Nanodegree Foundations
    • CS50’s Introduction to Artificial Intelligence with Python on edX
    • Udacity Artificial Intelligence Nanodegree
  • Big Data
    • Google Certified Associate Cloud Engineer
    • Google Certified Professional Data Engineer
    • Google Certified Professional Cloud Developer
    • Google Certified Professional Machine Learning Engineer
  • Math
    • Khan Academy Matrices
    • Khan Academy Linear Algebra
    • Khan Academy Calculus and Multivariable Calculus
    • 3blue1brown series
    • Math for Machine Learning on Coursera
  • Books
    • Take control of the Mac Command Line with Terminal
    • Information Theory, Inference and Learning Algorithms by David MacKay
    • An introduction to Statistical Learning by James G., Witten D., et al.
    • Naked Statistics by Charles Wheelan
    • Learn Python the Hard Way by Zed Shaw
    • Data Science from Scratch by Joel Grus
    • Hands-on Machine Learning with Scikit-Learn and TensorFlow by Aurélien Géron
    • The Hundred-Page Machine Learning Book by Andriy Burkov
    • Artificial Intelligence: A Modern Approach by Peter Norvig and Steven Russell
    • Deep Learning with Python by François Chollet
    • Neural Networks and Deep Learning by Michael Nielsen
    • Build a Career in Data Science by Emily Robinson and Jacqueline Nolis

*Bold means completed

Undoubtedly, this blueprint will change as I finish courses and keep learning. It is great to have a roadmap for this journey!