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I was thinking about datascience lately. The problem is that I don’t know much about datascience. I learned about data bases in school and worked with them some in industry but that was mostly about how they work internally. But I never did much of anything with real work data applications.
It happened with our Physics Project in 2020. It extends to things like doing datascience or other forms of computational work (or programming). And its invention soon led to algebra, and calculus, and ultimately all the various mathematical sciences. ChatGPT and Wolfram|Alpha.
While the science aspect (chemistry, biology, and physics) and mathematics (calculus and algebra) is a breeze to figure out, the engineering and technology aspects are less straightforward. Coding can be effective when coupled with physical computing or on a computer.
There’s a discernible physical component to learning. in algebraic topology from a top-25 research university. Right now I'm using it to work through a datascience program on Codecademy. Here are some things I’ve re-learned about being a student in a math classroom, of sorts: 1. And I have a Ph.D.
Because non-invasive methods, in particular, rely upon detecting quite subtle stimuli, any interference – such as the noise created by movement or physically interacting with an object – can affect their functioning. “Most BCIs only work when the user is stationary, limiting their use in many real-world applications.”
So, for example, here’s a graphical representation of a simple arithmetic evaluation, with TraceOriginal → True : And here’s the corresponding “pruned” version, with TraceOriginal → Automatic : (And, yes, the structures of these graphs are closely related to things like the causal graphs we construct in our Physics Project.) In Version 3.0
There are so many open questions in cognitive science. I began my undergraduate studies thinking I wanted to do physics, which I really enjoyed, but I realised all the basic questions had been solved, and the remaining questions seemed too complicated to make realistic progress. Follow your nose on what you find most interesting.
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