Terence Tao explains 6 essential mathematical concepts [video]

You can also think of conditional convergence as convergence under the condition of a specific order. It then turns out that unconditional convergence (that is, convergence where it doesn't matter what order you choose) is equivalent to absolute convergence (that is, the sum over the absolute values converges).

Here is another list of ideas in math made by a lesser math person. (top 100 most useful)

counting zero integer decimal positional notation 100, 1000, … the four arithmetic operations + – * / fractions decimal notation 0.1, 0.01, … basic propositional logic (Modus ponens, contrapositive, If-then, and, or, nand, …) negative numbers equivalence classes equality & substitution basic algebra – idea of variables, equations, … the idea of probability commutative and associative properties distributive property powers (squared, cubed,…), – compound interest (miracle of) scientific notation 1.3e6 = 1,300,000 polynomials first order predicate logic infinity irrational numbers De Morgan’s laws statistical independence the notion of a function square root (cube root, …) inequalities (list of inequalities) power laws (i.e. abac=ab+c ) Cartesian coordinate plane basic set theory random variable probability distribution histogram the mean, expected value & strong law of large numbers the graph of a function standard deviation Pythagorean theorem vectors and vector spaces limits real numbers as limits of fractions, the least upper bound continuity Rn, Euclidean Space, and Hilbert spaces (inner or dot product) derivative correlation central limit theorem, Gaussian Distribution, Properties of Guassains. integrals chain rule modular arithmetic sine cosine tangent π, circumference, area, and volume formulas for circles, rectangles, parallelograms, triangles, spheres, cones,… linear regression Taylor’s theorem the number e and the exponential function Rolle’s theorem, Karush–Kuhn–Tucker conditions, derivative is zero at the maximum the notion of linearity Big O notation injective (one-to-one) / surjective (onto) functions imaginary numbers symmetry Euler’s Formula eiπ+1=0 Fourier transform, convolution in time domain is the product in the frequency domain (& vice versa), the FFT fundamental theorem of calculus logarithms matrices conic sections Boolean algebra Cauchy–Schwarz inequality binomial theorem – Pascal’s triangle the determinant ordinary differential equation (ODE) mode (maximum likelihood estimator) cosine law prime numbers linear independence Jacobian fundamental theorem of arithmetic duality – (polyhedron faces & points, geometry lines and points, Dual Linear Program, dual space, …) intermediate value theorem eigenvalues median entropy KL distance binomial distribution Bayes’ theorem 210≈1000 compactness, Heine – Borel theorem metric space, Triangle Inequality Projections, Best Approximation 1/(1−X)=1+X+X2+… partial differential equations quadratic formula Reisz representation theorem Fubini’s theorem the ideas of groups, semigroups, monoids, rings, … Singular Value Decomposition numeric integration – trapezoidal rule, Simpson’s rule, … mutual information Plancherel’s theorem matrix condition number integration by parts Euler’s method for numerical integration of ODEs (and improved Euler & Runge–Kutta) pigeon hole principle

mathematical used less often: Baire category theorem, Banach Spaces, Brouwer Fixed Point Theorem, Carathéodory’s Theorem, Category Theory, Cauchy integral formula, calculus of variations, closed graph theorem, Chinese remainder theorem, Clifford algebra (quaternions), Context Free Grammars, countable vs uncountable infinity, Cramer’s Rule, cohomology, Euclidean algorithm, fundamental group, Gauss’ Law, Grassmannian algebra , Graph Theory, Hahn-Banach Theorem, homology, Hairy Ball Theorem, Hölder’s inequality, inclusion-exclusion, Jordan Decomposition, Kalman Filters, Markov Chains (Hidden Markov Models), modules, non-associative algebras, Picard’s Great Theorem, Platonic/Euclidean solids, Principle of Induction, Probabilistic Graphical Models (Bayesian Networks, Markov Random Fields), Pontryagin duality, Quaternions, Spectral Theorem, Sylow p subgroup, repeating decimals equal a fraction, ring ideals, sine law, tensors, tessellation, transcendental numbers, Uniform Boundedness Theorem, Weierstrass approximation theorem. From http://artent.net/2012/11/27/100-most-useful-theorems-and-id...

Here's something to ponder. Assuming the Riemann Hypothesis is true, would that monkey type out a proof of the RH before it got to Hamlet? I guess it depends on if the proof can be expressed in fewer words than the word count of Hamlet.

I think there is lots of nuance between theory and application. Some people are really good teachers, and good teachers can give us mental models are abstract enough to get the gist of things.

Roger Federer would say he never knew what kind of grip he used on his shots(which is one of the first things one learns as a beginner), and I think Roger might not be an elite coach, because so much of his greatness may have come from a very intuitive understanding of tennis. (Would I still take him as my coach, heck yeah).

I think some people have really intuitive understanding of their subjects and can express that understanding in amazing applications, but they lack the communication skills, patience, or language to properly pass on the knowledge to others.

Being good at a thing and explaining it well are different skills. Part of the problem is that you have to empathize with your audience when explaining. It's not sufficient to break a problem down into its essence; its "essence" varies depending on who you're talking to.

An example from something I've had to iterate on: When explaining an event loop multiprocessing runtime sort of thing, I eventually found I had to hand-wave "and your CPU hates that" to establish an appropriate premise to the problem and solution (referring to item-by-item dynamic dispatch with a large number of task types as the specific demon which needed to be slain while discussing that subset of the design). People in the know didn't need more understanding. People not in the know were happy to brush their lack of microarchitectural understanding under the rug. With that premise, both crowds were able to understand what followed.

That wasn't my first attempt. I have a bad habit of trying to explain those missing details as well, especially when it's clear the listener doesn't know them yet -- trying to get them into a position where they could've built the thing themselves -- but that only lands well with like 1-5% of people I've met.

Critically, agreeing with you, that's a communication failure, not an engineering failure. I understood the problem just as well in both cases; I just didn't understand the full extent of the people problem.