Here’s an interesting look at Python from the point of view of a hard core developer – anyone that remembers IRQs has been around a while.

From the video description:

The Python language’s memory model can be deduced from first principles: simply take modern C++ conventions and drive their safety and generality to infinity. But this limiting case generates its own compromises and opens its own categories of possible runtime errors. We will explore the position Python has staked out in the language design space of correctness versus performance, the choices Python programmers make when they need to move closer to C++, and the ways that the C++ community keeps adopting conventions that look suspiciously like Python.

Also, the audio starts around the 9 second mark.

Jim Simons was a mathematician and cryptographer who realized: the complex math he used to break codes could help explain patterns in the world of finance. Billions later, he’s working to support the next generation of math teachers and scholars.

In this video, TED’s Chris Anderson sits down with Simons to talk about his extraordinary life in numbers.

Every since getting into Data Science, I have been fascinated with the idea of exploring data in higher dimensions. Actually, this fascination dates back to a lecture in college on data structures, where the professor talked about visualizing five dimensional arrays. What does this space look like? Are we capable of even imagining such spaces?

Whether you realize it or not, lambda calculus has already impacted your world as a data scientist or a developer. If you’ve played around in functional programming languages like Haskell or F#, then you are familiar some of the same ideas. In fact, AWS’s serverless product is named Lambda after this branch of mathematics.

Watch this video to learn about lambda calculus.

Normally, I say the best way to get started with machine learning is to do machine learning. So, strictly speaking, there’s no “required math” to get started and follow along with online tutorials. However, if you want to take your AI game to the next level, then it’s time to get deep into the math.

Data Science Central outlines specifics around the subjects to tackle and where to find them online.

  1. Linear Algebra — Professor Strang’s textbook and MIT Open Courseware course are recommended for good reason. Khan Academy also has some great resources, and there is a helpful set of review notes from Stanford.
  2. Multivariate Calculus — Again, MIT Open Courseware has good courses, and so does Khan Academy.
  3. Probability — Stanford’s CS 229, a course I’ve mentioned later, has an awesome probability review worth checking out.

A lot has changed since I earned my degree in Computer Science, but the fundamentals like math and set theory have remained relatively constant. Ironically, I was student who loathed math for most of my academic career or at least I thought I did. I enjoyed Discrete Mathematics and Set Theory.

In truth, Computer Science majors have to learn a different kind of math compared to most other majors (except of course math majors). These branches of math are critical for those looking to go into research in fields like computer science, AI, or even pure mathematical research.