How To "Ultralearn" Data Science -- Part 1

Concepts in data science would be training and education which are prerequisites for data science. These include a solid foundation in mathematics (statistics, probability, linear algebra, and calculus), programming, machine learning, and AI, and business analysis. Facts in data science will then be the textbook stuff involved in data science, such as the facts in mathematics and machine learning that you have to deeply understand to the point where you can teach it to other people. Facts involved should not be memorized as formal education has brainwashed us to do but should be comprehended at the atomic level, where you can translate jargon into an easier language for the masses to understand. Procedures involved are the fundamentals in data science -- business understanding, data acquisition, and preparing (mining and cleaning), deployment, modeling, and visualization.