Descart: What an excellent time to be long dead and therefore not need to even think about this logical abomination of a sentence!
jol@discuss.tchncs.de
on 22 Jun 14:49
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Science researchers and students often spend a lot of their time doing statistical analysis, including using programming for that.
propter_hog@hexbear.net
on 22 Jun 15:31
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Computational biology and ecology are a huge part of those fields. I work at a research lab (in computer science) and one of our sister labs is dedicated to environmental stuff and has mostly biologists and ecologists employed at it; a large part of the research they do involves supercomputing somehow, so we tend to partner with them a lot. As an example, modeling population growth or decline due to a change in the populationās environment is one such use of computing and statistics in biology and ecology.
Peanutbjelly@sopuli.xyz
on 22 Jun 15:52
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Bayesian analysis of complex intelligent systems via fristonās free energy principle and active inference? Or machine learning?
Personally love the stuff circling Michael Levin at tufts university. I could also imagine thereās a lot of unique model building in different biological/ecological niches.
Probably Python and R for statistical analysis, which is common nowadays in most empirical sciences.
Venus_Ziegenfalle@feddit.org
on 22 Jun 14:07
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Vivisection too
thevoidzero@lemmy.world
on 22 Jun 15:36
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Honest opinion programming is easy and fun when you learn it and it saves you time and allows you to test your ideas. Creating something gives you dopamine.
Problem is before people even try any programming for themselves, they are introduced to it through school or work where they have to do it for homeworks or analysis while also learning new things. And they hate it.
atomicbocks@sh.itjust.works
on 22 Jun 18:01
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I didnāt really start hating it until not doing it for 13 hours a day meant losing my job because, and I quote, āwe can have meetings all day because you have all night to finish your workā.
blackbirdbiryani@lemmy.world
on 22 Jun 22:04
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Maybe a lukewarm take now, but you can no longer expect to succeed well in biology if you donāt have at least an intermediate understanding of programming and statistics.
Without the former, you are going to be wasting a lot of time doing manual work (I kid you not but I see my co-workers waste literal hours gazing at matrices in Excel like theyāre gonna land on a significant gene by accident).
Without the latter, you are going to be wasting thousands of dollars in reagents and working time running experiments that never had the hope of succeeding (what do you mean I need more than one replicate?).
Yes you can stick to lab work but donāt expect to get paid more than the average janitor, because youāre competing against literal thousands of graduates who can use a pipette but not R. Maybe if you were a specialist in an expensive niche equipment like flow cytometry or mass spectrometry, but surprise surprise, these kind of equipment require an even more advance understanding of statistics to understand/process the results.
If youāre a biologist who thinks you hate math, I promise you programming is more approachable than high school math, thereās so many tutorials available these days for free that are leagues better than any material from your professor.
Try to get as many opportunities that involve command line work on clusters, analyses with R, and maybe python as well, and youād be a candidate that would stick above the rest. Programming and statistics is rapidly becoming a common competency, and if you donāt have those skills you wonāt be able to compete with people who do.
This is why I stopped pursuing my degree, Iām never going to work in a lab stop trying to force irrelevant knowledge on me, granted it was psych not biology/ecology
threaded - newest
What does programming have to do with animals?
Humans are animals, and humans invented programming. Therefore, programming is applied biology.
Descart: What an excellent time to be long dead and therefore not need to even think about this logical abomination of a sentence!
Science researchers and students often spend a lot of their time doing statistical analysis, including using programming for that.
Computational biology and ecology are a huge part of those fields. I work at a research lab (in computer science) and one of our sister labs is dedicated to environmental stuff and has mostly biologists and ecologists employed at it; a large part of the research they do involves supercomputing somehow, so we tend to partner with them a lot. As an example, modeling population growth or decline due to a change in the populationās environment is one such use of computing and statistics in biology and ecology.
Bayesian analysis of complex intelligent systems via fristonās free energy principle and active inference? Or machine learning?
Personally love the stuff circling Michael Levin at tufts university. I could also imagine thereās a lot of unique model building in different biological/ecological niches.
lm(turtle_gender ~ temp, data = data_frame)The turtles are not safe from Python
Probably Python and R for statistical analysis, which is common nowadays in most empirical sciences.
Vivisection too
Honest opinion programming is easy and fun when you learn it and it saves you time and allows you to test your ideas. Creating something gives you dopamine.
Problem is before people even try any programming for themselves, they are introduced to it through school or work where they have to do it for homeworks or analysis while also learning new things. And they hate it.
I didnāt really start hating it until not doing it for 13 hours a day meant losing my job because, and I quote, āwe can have meetings all day because you have all night to finish your workā.
Program an animal sim using real stats. š
Like SimAnt, the cockroaches in Half-Life, or WolfQuest. Make a realistic dragon simulation!
Ever play SimPark? Iād fill up the park with blackberries to attract bears and scare tourists.
<img alt="" src="https://sh.itjust.works/pictrs/image/8caeed68-11c1-419a-a1c6-1ef182ad7dc5.jpeg">
Maybe a lukewarm take now, but you can no longer expect to succeed well in biology if you donāt have at least an intermediate understanding of programming and statistics.
Without the former, you are going to be wasting a lot of time doing manual work (I kid you not but I see my co-workers waste literal hours gazing at matrices in Excel like theyāre gonna land on a significant gene by accident).
Without the latter, you are going to be wasting thousands of dollars in reagents and working time running experiments that never had the hope of succeeding (what do you mean I need more than one replicate?).
Yes you can stick to lab work but donāt expect to get paid more than the average janitor, because youāre competing against literal thousands of graduates who can use a pipette but not R. Maybe if you were a specialist in an expensive niche equipment like flow cytometry or mass spectrometry, but surprise surprise, these kind of equipment require an even more advance understanding of statistics to understand/process the results.
If youāre a biologist who thinks you hate math, I promise you programming is more approachable than high school math, thereās so many tutorials available these days for free that are leagues better than any material from your professor.
Try to get as many opportunities that involve command line work on clusters, analyses with R, and maybe python as well, and youād be a candidate that would stick above the rest. Programming and statistics is rapidly becoming a common competency, and if you donāt have those skills you wonāt be able to compete with people who do.
Is programming skills that important in days of LLM where companies are replacing juniors with AI?
absolutely
It wouldāve been smarter of those companies to replace the bosses with AI.
This is why I stopped pursuing my degree, Iām never going to work in a lab stop trying to force irrelevant knowledge on me, granted it was psych not biology/ecology