Pick some unrelated lectures, they said.
Pick some unrelated lectures, they said.
It will widen your horizon, they said. And here I was, foolishly thinking I could get away with half-assing statistics during my degree.
As somebody with a degree in bioinformatics, I have never seen something more true in my whole life.
Some more lies from my time in academia:
- “Planning and executing medical trials” = 6 months of statistics
- “Machine Learning” = 6 months of statistics
- “Pattern Recognition” = 6 months of statistics
- “Health Data Science” = 6 months of statistics
- Biostatistics = lol jk just 6 months of statistics
I thought the punch line was that biostatistics is actual biology, and biology is statistics :)
A someone not in the field (CS/Machine learning) what did you expect these to be?
Whatever course you do in STEM, you don't want to half-ass the first semester of calculus, linear algebra and statistics.
In fact, you probably want to go out of your way to actually learn linear algebra (because I've never seen anybody really learn it on the course, you need to apply it) and statistics (because you want to go deeper).
Linear algebra, absolutely. But I kind of hoped to get through my whole degree (mostly EE) without properly knowing statistics. Hah. First I take an elective Intro AI class, and then BioInf. I guess I hate myself.
Oh you can get through most degrees without properly learning linear algebra or statistics.
But those 3 are the knowledge that will pop here and there on everything you do, and leave you confused, incapable of understand things, and incapable of extending things if you don't know them. Usually, you won't even have to calculate anything, you just have to know them.
Whatever course you do in STEM, you don’t want to half-ass the first semester of calculus, linear algebra and statistics.
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People here saying this shit is useless are fucking wack, I use this shit frequently in my job. Basic affine transforms for grid data is an interview question we ask junior engineers.
My wife did more statistics in her psychology degree than I did in engineering.
Because engineering is precise, measurable, and easily reproducible. You should be testing your things in a way that all you need is a simple two-sample Z-test.
Experiments on the humans, on the hand, unfortunately, have been outlawed. So all you get is a bunch of shitty noisy data, and yet you're supposed to somehow make sense of it. Most people with a degree in stats would tell you not to even try, and yet those fucks at phycology departments always do while having had about one undergrad-level class as part of their masters.
TL;DR good psychology programs nowdays train decent statisticians as they should.
This is not really true. What is the hypothesis test which separates the energy which represents the information symbol 1001 from information symbol 1101? Log likelihood ratio involved in converting energy to information is both incredibly complicated probability theory, and quietly defines a huge part of our modern world.
Engineering formulas be like
"So there was this guy in 1896 and he did a bunch of trials and he figured out that a+b*x/c² is close enough to the real results, with values for a in range 1-2 and b in range 3-4. We still don't understand why, or how he got there, but it worked ever since."
Scientists want to understand things. Engineers don't care, as long as it works.
"More recent research has produced a more precise emperical relation, -4.2x^3.761+√(sin(2x²/π))±erf(e²ˣ+37), which produces results which are 0.2% more accurate on average."
It always got me that the maths I was doing in electrical engineering outclassed what my friend was doing for his astrophysics degree. He was probably at the better university too (Debatable for the subjects in question, but both really good).
Did I need that level of maths? No, but it was compulsory for the first 3 years so not much option.
That's because you didn't get to stochastic signals and systems.
But... but... these are my maths shoes!
Damn, looks like an elaborate course in probabilistic graphical models, sign me up!
I have a degree in regular informatics and I don't really know what that even means. 🤷