If you can make certain assumptions about the system
(mainly that sources of noise follow gaussian distributions and are independent), then the Kalman filter gives the best possible estimate of the system state. And it can be computed cheaply, like on the Apollo guidance computer.
You basically need to know some kind of a model for the system to run KF. Whereas ML is all about working out the model automatically.
As for similarities, KF is a really efficient implementation of Bayesian inference. I think that any ML model that isn't fundamentally using Bayesian inference, is fundamentally flawed.
> Good DS understands the basics of web technology
I'm not a data scientist but a portion of my job is creating pipelines, data analytics and such. I also only have a bare minimum knowledge of web technology. Why is knowledge of web technology part of being a good Data Scientist? Or is this point oriented specifically for data scientists working in web based companies?
Genuinely curious. I could imagine myself working as a DS in the future and that's why I found this article interesting.
I don't think there is a single correct answer here, but I'll offer a few insights from personal experience.
Firstly, valuable data tends to live in places accessible via web technology. Maybe you need to fetch a bunch of XML files from an FTP site? Having a clear understanding of all the nuances you're about to encounter will set you up for success.
Secondly, valuable data tends to be generated by web technology itself. Understanding that lifecycle can inform analytical strategy.
Finally, some data scientists add value by informing decision makers. One of the most powerful things you can do for them is give them a mobile friendly secure web experience that puts the data they need directly at their finger tips. While yes, Tableau et al. are an option here, you'll be ahead of your peers by knowing how to DIY it when it counts.
I'm also not a physicist, but for the fun of the discussion...
Radiative heat loss scales with the fourth power of temperature. I don't know what temperature the ISS radiators are but suppose they are around 300K. Then I think the radiative surface to keep something cool at 10K would need to be 30^4, or 810000 times larger per unit heat loss. So realistically I think you would need some kind of wacky very low temperature refrigeration to raise the temperature at the radiator, and then maybe radiate the heat into the lunar surface.
This happened to me on an emirates A380, but on landing. The timing was comical because immediately after touching down (and being rained on) the entertainment screen prompts you to rate your flight.
