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.

In this talk given at the Royal Institute, Eugenia Cheng explores how anyone can think like a mathematician to understand what people are really telling us – and how we can argue back.

Taking a careful scalpel to fake news, politics, privilege, sexism and dozens of other real-world situations, she will teach us how to find clarity without losing nuance.