I think the high-paying jobs of the future will require trained critical thinkers who have a broad exposure to formal methods, particularly basic statistics and data science programming skills.

My background: I am a philosopher of science with research experience in the foundations of computation, quantum information, machine learning, AI, systems theory, cybernetics, and data science.

I tutored and corrected tests for intro philosophy courses in my undergraduate degree, and I occasionally had to teach or tutor bachelor-level students during my graduate studies (Logic, Philosophy of Science, and Neurophilosophy).

I have a Masters degree in Logic and Philosophy of Science from the Munich Center for Mathematical Philosophy, which included training in a variety of formal methods including agent-based simulation, proof theory, basic statistics and Bayesian networks.

My Ph.D. research at Munich's Graduate School of Systemic Neuroscience also included training in data science and machine learning. I have an undergraduate degree in Philosophy, with a thesis on uncertainty in quantum theory. My Master's degree in Logic and Philosophy of Science included several philosophy of physics courses, including on quantum computation. During my Ph.D. in Systemic Neuroscience, I criticized quantum-like models of cognition (they use Hilbert space math to model cognition).

I am primarily competent foundations of quantum theory, and the basics of Hilbert space formulation (although I think wave mechanics are more fundamental, but I am less familiar with the math there).

I have worked with Python on high-performance computers at Cray, experimenting with and training a variety of machine learning examples.

I think much of what is important for learning Python is to find an important project or concept to learn, like a basic neural network or statistics example, and pick up the necessary Python in order to implement the project. I don't like learning a programming language from a textbook, it is more important to exp