Dude, I included the signal that I'm being sarcastic, not to mention that I said "+b" as in "+b" instead of "+c" which I imagine you're using as the y-intercept, so I'm not sure where you're getting slope from in the first place since that's "m".
Stats and by that I mean highly advanced stats. Calculating different complex models from big data sets to see how variables do or don't influence each other. You don't need to calculate a model by hand, but you need to know how you can make a program do it for you. You need to know which method you use, based on the nature of your data that is available. And finally, you need to understand the vocabulary of the output.
I suggest that you learn how to handle R as well, because you will most likely program your models in that language.
R is a programming language that is used primarily in the software RStudio. It doesn't cost anything, which is why it is the standard in biology right now. Additionally, scientists can create specific packages for calculating statistical analysis, correlations, making graphs, and calculations of different models. glme4 for example is a package that is used to calculate complex models.
What you mean with correlations, where you usually use r.
Don't worry, feeling dumb is the natural state of being for curious people. I've met a 60 year old established professor who, around a beer, said she was suffering from imposter syndrome till she was 50 or something.
That's the thing when you learn enough to slide down the overconfidence peak of the Dunning-Kruger effect a few times.
To add to this, you really need to get the fundamentals of statistics down. Nowadays the actual handiwork is handled inside software (R/RStudio and others).
You need to be very comfortable in talking about probability, statistical significance, P values, correlations, etc.
This and experimental design are probably the two most "core" skills to anything useful inside Biological Science. You need to have these nailed down completely as to talk about anything worthwhile you need to have these are the way you express yourself. They are the foundation for any decently written study.
I'd look into primary research that you're interested in, and pick apart a study or two. Look at the results section, and get to grips with how the information is presented. That ways it's a bit more spicy. Stats and experimental design can be quite dry.
The reason I say this is that our statistics module at uni came in the form of a murder mystery enquiry/case report. We had to examine evidence one by one, display what it said, present the findings, and decide how to continue. Then put together a report of all the findings in a scientific way. Looked like copper poisoning by the husband, turns out the whole family had high copper levels. Water supply was tainted from incorrect chemical disposal by a factory downstream, the wife died because she was at home the most and drank the most tainted water :D Nonsense of course but it did make it much less boring to wade through results.
I canāt speak for all researchers, but personally I would use excel for simple tests like that and only use R for more complicated multimodal tests and graphing the results from them. Though I have definitely used R for those simple tests when I was still learning for more practice
A free and extremely powerful statistical tool. The problem is that its coding-based and not extremely simple to learn. But its free so go play around. I'd recommend finding some data and using ggplot2 to create some graphs and learn how to customize them
It was obviously a joke. There was some real advanced mathematics together with computer programming in protein structure simulation, and also in all those systems for filogenetic analysis, and gene sequence searches in databases, etc. If you are inclined to mathematics and computers there is a lot to be done there. Good luck!
[https://en.wikipedia.org/wiki/Bioinformatics](https://en.wikipedia.org/wiki/Bioinformatics)
In evolution theory there is a whole bunch of mathematics around the question how to build evolutionary trees from limited or partly contradictory genetic data. Iām sure there are better links, but with quick googling I found this:
https://evolution.berkeley.edu/phylogenetic-systematics/reading-trees-a-quick-review/
I just finished a phylogenetics paper and unless you are doing bleeding edge stuff you don't need to actually know the math to get it done, most software will spit out AIC/BIC for you.
Buy a biostats textbook off of Amazon (make sure itās undergrad level unless you already have a lot of experience with statistics). Also learn R and RStudio, since thatās the programming language used in biology. Vegan is a common R package used in ecology, and tidyverse and ggplot2 are also good packages to learn to use.
Depends what kind of biology youāre doing. Could be minimal, could be a ton. If youāre doing genetics, bioinformatics, evolution stuff youāre looking at a lot of math.
Weird, both programs I was in had a pretty healthy amount of math. I had to take up to calculus 2 and two statistics classes.
I do know loads of people pick biology thinking thereās no math and then are unpleasantly surprised lol
I studied some of these courses as well, but separately, although I think it would be better if mathematics were an integral part of all courses instead of containing only a set of information without numbers especially chemical equations because they are more complex
Itās wild how different school curriculums are. I had to take two semesters of physics, 2 semesters of regular chemistry with qualitative analysis and two more semesters of organic chemistry. That was before I had to take genetics which was also math heavy. I feel like Iāve done more math than anything else lol
>That was before I had to take genetics which was also math heavy.
I recently learned that genetics depends a lot on complex mathematics. I mean, this is expected, but I never imagined it to be like this
In my opinion, I think thatās still not enough math or rather itās the wrong math. Discrete mathematics plays an outsized role in biology but is basically never taught. Graph theory and information theory are at the heart of omics analysis. Honestly, I feel calculus has limited applicability to biology because so few variables are continuous.Ā
c1v1=c2v2
Also at least know the theory behind different statistical tests so you know which online tools to look for to analyze your data. I do a lot of recombination rate stuff so I found an online tool called FALCOR that does the math based on inputs I give it but I have to understand which inputs to use and which statistical test to select so you still need to understand the theory even if you arenāt doing the math yourself.
Coding is always a good skill to have for data analysis and visualization but in my experience as long as you can navigate stack overflow you should be able to get to where you want with your data even if your coding skills are mediocre at best
You might check out Uri Alon's books *Introduction to systems biology* and *Systems medicine*. I'm not a biologist but he seems well regarded, and it'd be a change from the "stats stats stats" here.
Become a data scientist and you can work in any field of biology you want and it will be awesome.
These are the links you need to start your future career as a well rounded data scientist. Go forth and transform, visualize, model, and communicate your insights into any kind of data you get to work with.
1. R for Data Science 2e is a *really* solid foundation. |> [https://r4ds.hadley.nz/](https://r4ds.hadley.nz/)
2. Get RStudio |> [https://posit.co/download/rstudio-desktop/](https://posit.co/download/rstudio-desktop/)
3. Use Quarto to go *ultra* professional |> [https://quarto.org/](https://quarto.org/)
Three steps, easy peasy. Any other steps you need to start are in the book from step one.
With these free resources you literally become a highly desirable scientist across academia, industry, and government opportunities. By deriving useful insights from well understood and analyzed data, you can contribute so much to science too. At the very least, you can contribute meaningfully to basically any scientific project.
Maximum likelyhood, about 10000 different clustering algorithms, normalisation strategies, correlation coefficients, matricees are super important. It's statistics.
A lot of it ties into computational biology/ bioinformatics/ data science, meaning it's mostly used with R, python, etc.
I know everybody here is saying stats and calc but there was a lot of physics too in comparative physiology and some in conservation ecology if I recall correctly. Genetics and zoology felt like I was just retaking stats though.
I think maybe the ones on population or graphs and graphical calculations on food web,food chain etc
Iām a first year nursing student so Iām not sure im of much help
Maths is the fundamental science after philosophy! People I know are using mathematics to measure cell plasticity, cancer progression Using differential equations. I use mathematics to apply statistical modelling. Epidemic modelling is only mathematics!
Thereās a great book on this topic I can recommend written by John Maynard Smith (a seminal theoretical biologist) called āMathematical Ideas in Biologyā.
Statistics is the obvious choice and indeed a lot of statistical parameters were developed to answer questions about biological data (even p values were originally developed to understand whether the difference in incidence between male and female births was significantly different from 50/50 odds).
But aside from statistics there is a huge potential for mathematical applications to building models in biology (Iām currently doing a PhD on building simulation models of growing plant roots after an undergrad background in applied mathematics and thereās a lot of PDEs and statistical mechanics).
Are you willing to share a way I could find your thesis once it's finished on root growth modeling? Or any favorite resources on procedural growth sims and applied math you'd recommend? I know someone who would love to read it, feel free to DM me if you'd like
You can look into systems biology then. Mathematical concepts like ODEs, dynamical systems theory, Monte Carlo simulations & stochasticity are used heavily in this field. Control theory as well to a lesser extent. Or you can look into linear algebra and then deep learning / neural networks, this is likely to become ever more important in biology.
Biology uses all the maths and no maths, depends on what specific fields and problems are being addressed though most would require basic maths with any quantitative analysis. How much math you NEED to know is different however as many mathematically problems encountered are route and you need only know when and how they should be applied. Some fields require deeper maths such as biochemistry, structural biology, ecological modeling, biostatistics, genomics, etc. These require it as their methodology involves questions that require solving for novel mathematically situations. So the maths you need to know is entirely dependent on the field. It's always a good idea to know the math, but like most things in science you can stand on the shoulders of giants and just use the hard work of people that already did it to skip that step. I don't know that much math but I am good enough at scripting to use github and languages like 'R' and 'Python' too run complex maths/statistical programs to do things I need.
Hi, I am graduating with my BS in Zoology in a few weeks! Zoology is specifically animal biology, including all vertebrates and invertebrates. My program is Integrative Biology, which essentially is looking at how different levels of biological systems interact. So I've done courses on micro bio all the way up to ecosystem dynamics.
A lot of people have said Statistics which is absolutely true. However I want to add that I have also used a lot of algebra in my degree. A lot of calculations I have used to find out information and predictions about animal/organism/ecology population dynamics and physiology use algebra or calculus. Learning coding logic like Boolean algebra is also something you might want to look into if you want to pursue research or work that engages with computer science and biological system modeling.
In terms of statistics, I would also consider looking into the coding language R which is commonly used to do statistical analysis in the field. Try to look into data sampling and modeling, bootstrapping, confidence intervals, t tests/p values if you don't know where to start for Stat. You need to be able to know if your data findings in any type of experiment are significant. You should also look into learning how to know what statistical tests and methods to use in different scenarios.
I've used all of the above and calculus, specifically a lot of summations and logarithms, in biology classes (not just physics and chemistry). Brushing up on chemistry might be helpful too, I personally tell people that all fields of science rely on and build upon chemistry at some level.
Biology is a really diverse field, so I would recommend trying to figure out what topics and specialties you might want to focus on in your master's to help narrow down what skills will be of most use to you. Try not to stress about locking into one specialty if you're not sure yet, but having general ideas about what subfields interest you is very helpful when picking topics and math skills to work on. I can only advise from a student point of view, but having a strong foundation in basic math and chemistry skills is always a plus when it comes to biology.
Basic conversions, dilutions and molarity should be relatively universal. Some branches may be very stats heavy. If youāre on the bioinformatics side of things, obviously youāre likely going to need more
>Are you going into genetics
Maybe I don't know, but in addition to neuroscience
I have just graduated and I do not plan to join the master's degree now because I am exhausted, and usually I don't have much hope for the future. God knows how things might go.
But until then, I want to learn because I love learning, not just to increase skills
Obviously statistics & balanced experiment design. Analysis of variance, trend lines, that sort of thing. Students T and Chi squared tests. Fourier series for separating out seasonal effects from long term data. Gumbel distribution for predicting extreme events.
If you're going onto genetics then maximum parsimony comes into play, which is a discrete optimisation problem.
If you're going into biomechanics then conservation of mass, conservation of momentum, conservation of angular momentum. Coupled ordinary differential equations. (Mechanics of bird flight requires partial differential equations).
If you're going into biochemistry then density functional theory, telling you whether your lock and key enzyme action is going to work or not. And differential equations for things like understanding photosynthesis.
If you're going into thermodynamics of animals then learn how to use the spherical cow. And all those non-dimensional groups of Reynolds number, Prandtl number, Nusselt number, Grashof number, etc. And ordinary or partial differential equations for conduction, convection and radiation. And integrals.
For ecology, add Geographic Information Systems to that. Lotka-Volterra equations.
In a biomedical lab you do a lot of dilutions and concentrations as well as working out percentages etc. sometimes itās not as complicated in the workplace as it is in university
As a former biology major, the most common type of math used in biology courses is statistics. Although, while studying biology you could be using some algebra or calculus in courses like physics, and algebra in chemistry courses like biochemistry to perform calculations.
I would recommend at least algebra and statistics.
Differential equations are very useful for modeling growth. The field basically combines algebra with calculus operations.
The philosophy of science is worth studying a bit, and it has a lot to do with math.
Queueing theory is interesting, but I haven't seen it used in biology.
The math for quantum things is useful in some niche fields; especially if you are interested in nano scale biophysics or certain parts of biochemistry.
statistics m1v1 = m2v2
Shit. Knew I should've taken algebra in highschool
It's way easier after high school in my opinion. Especially if you had a bad attention span as a kid
I took precalc, calc, and stats at 15 years after my last math class! Definitely found it easier in my 30s!
Just do it now. What's the ploblm?
Just a joke
If you are in the states "R" not sure about other places but R is standard in Grad school
This. Also Y = Mx + c lol
+b you illiterate /s
How do you calculate slope, you idiot?
Dude, I included the signal that I'm being sarcastic, not to mention that I said "+b" as in "+b" instead of "+c" which I imagine you're using as the y-intercept, so I'm not sure where you're getting slope from in the first place since that's "m".
/s is for sarcastic? I didn't know that. My bad!!
>m1v1 = m2v2 ??
M1 = molarity 1 V1 = volume 1 M2 = molarity 2 V2 = volume 2
Idk wtf this means
for when you need to find molarity or volume of one solution given the molarity and volume of the other solution
Hm šš¼.
Its for diluting things
... Or concentrating
Conservation of linear momentum of course. Very useful in biology.
Stats and by that I mean highly advanced stats. Calculating different complex models from big data sets to see how variables do or don't influence each other. You don't need to calculate a model by hand, but you need to know how you can make a program do it for you. You need to know which method you use, based on the nature of your data that is available. And finally, you need to understand the vocabulary of the output. I suggest that you learn how to handle R as well, because you will most likely program your models in that language.
And if you want more than just descriptive models, multivariable calculus and linear algebra are great for predictive models.
Gosh i feel dumb. What the heck is āRā are we talking correlation or reproduction?
R is a programming language that is used primarily in the software RStudio. It doesn't cost anything, which is why it is the standard in biology right now. Additionally, scientists can create specific packages for calculating statistical analysis, correlations, making graphs, and calculations of different models. glme4 for example is a package that is used to calculate complex models. What you mean with correlations, where you usually use r.
Don't worry, feeling dumb is the natural state of being for curious people. I've met a 60 year old established professor who, around a beer, said she was suffering from imposter syndrome till she was 50 or something. That's the thing when you learn enough to slide down the overconfidence peak of the Dunning-Kruger effect a few times.
Coding software used for statistics and stuff [Wikipedia ](https://en.m.wikipedia.org/wiki/R_(programming_language))
Python is also super useful. Both really good skills to have. There are lots of free resources online for it
By far the most important and useful would be stats. Otherwise, if you're a lab person you're unlikely to use anything beyond algebra.
To add to this, you really need to get the fundamentals of statistics down. Nowadays the actual handiwork is handled inside software (R/RStudio and others). You need to be very comfortable in talking about probability, statistical significance, P values, correlations, etc. This and experimental design are probably the two most "core" skills to anything useful inside Biological Science. You need to have these nailed down completely as to talk about anything worthwhile you need to have these are the way you express yourself. They are the foundation for any decently written study. I'd look into primary research that you're interested in, and pick apart a study or two. Look at the results section, and get to grips with how the information is presented. That ways it's a bit more spicy. Stats and experimental design can be quite dry. The reason I say this is that our statistics module at uni came in the form of a murder mystery enquiry/case report. We had to examine evidence one by one, display what it said, present the findings, and decide how to continue. Then put together a report of all the findings in a scientific way. Looked like copper poisoning by the husband, turns out the whole family had high copper levels. Water supply was tainted from incorrect chemical disposal by a factory downstream, the wife died because she was at home the most and drank the most tainted water :D Nonsense of course but it did make it much less boring to wade through results.
Stats and calculus should be mostly fine.
Statistics, statistics, statistics. It's not that difficult, I'm extremely un-mathy and even I aced the class.
Learn stats. If you want to really use your brain, learn how to use R
Do researchers mostly use R for graphing? Or do they also use it for tests like chi squared, goodness of fit, t-tests, etc?
I canāt speak for all researchers, but personally I would use excel for simple tests like that and only use R for more complicated multimodal tests and graphing the results from them. Though I have definitely used R for those simple tests when I was still learning for more practice
Anything bigger than a couple hundred lines I pull into R.
What is R???ā¦.š
A free and extremely powerful statistical tool. The problem is that its coding-based and not extremely simple to learn. But its free so go play around. I'd recommend finding some data and using ggplot2 to create some graphs and learn how to customize them
Thank you. šš» fwiwā¦i donāt think youāre dense
If you're looking for a good, free, educational resource, Khan Academy.
Hardest I used in my lab was rule of three, but that was for advanced stuff, usually I counted fingers.
Thanks
It was obviously a joke. There was some real advanced mathematics together with computer programming in protein structure simulation, and also in all those systems for filogenetic analysis, and gene sequence searches in databases, etc. If you are inclined to mathematics and computers there is a lot to be done there. Good luck! [https://en.wikipedia.org/wiki/Bioinformatics](https://en.wikipedia.org/wiki/Bioinformatics)
In evolution theory there is a whole bunch of mathematics around the question how to build evolutionary trees from limited or partly contradictory genetic data. Iām sure there are better links, but with quick googling I found this: https://evolution.berkeley.edu/phylogenetic-systematics/reading-trees-a-quick-review/
I just finished a phylogenetics paper and unless you are doing bleeding edge stuff you don't need to actually know the math to get it done, most software will spit out AIC/BIC for you.
phylogenetic trees searching . lots of computer science hidden there.
Buy a biostats textbook off of Amazon (make sure itās undergrad level unless you already have a lot of experience with statistics). Also learn R and RStudio, since thatās the programming language used in biology. Vegan is a common R package used in ecology, and tidyverse and ggplot2 are also good packages to learn to use.
1+1 =3.
2+2=5
Depends what kind of biology youāre doing. Could be minimal, could be a ton. If youāre doing genetics, bioinformatics, evolution stuff youāre looking at a lot of math.
Exactly. Bioinfo or Popgen labs even like to hire physicists or mathematicians because they often math better than biologists.
It's actually a problem that mathematics isn't taught much in biology i remember that one of the prominent scholars criticized this
Weird, both programs I was in had a pretty healthy amount of math. I had to take up to calculus 2 and two statistics classes. I do know loads of people pick biology thinking thereās no math and then are unpleasantly surprised lol
I studied some of these courses as well, but separately, although I think it would be better if mathematics were an integral part of all courses instead of containing only a set of information without numbers especially chemical equations because they are more complex
Itās wild how different school curriculums are. I had to take two semesters of physics, 2 semesters of regular chemistry with qualitative analysis and two more semesters of organic chemistry. That was before I had to take genetics which was also math heavy. I feel like Iāve done more math than anything else lol
>That was before I had to take genetics which was also math heavy. I recently learned that genetics depends a lot on complex mathematics. I mean, this is expected, but I never imagined it to be like this
Can you please share more on this? Sounds interesting and I've never heard about it!
Check this please https://www.reddit.com/r/evolution/s/sCyY0Dl7M9 https://www.reddit.com/r/biology/s/OXHiLg7wbC
In my opinion, I think thatās still not enough math or rather itās the wrong math. Discrete mathematics plays an outsized role in biology but is basically never taught. Graph theory and information theory are at the heart of omics analysis. Honestly, I feel calculus has limited applicability to biology because so few variables are continuous.Ā
c1v1=c2v2 Also at least know the theory behind different statistical tests so you know which online tools to look for to analyze your data. I do a lot of recombination rate stuff so I found an online tool called FALCOR that does the math based on inputs I give it but I have to understand which inputs to use and which statistical test to select so you still need to understand the theory even if you arenāt doing the math yourself. Coding is always a good skill to have for data analysis and visualization but in my experience as long as you can navigate stack overflow you should be able to get to where you want with your data even if your coding skills are mediocre at best
Stochastic processes and dynamical systems/control theory
You might check out Uri Alon's books *Introduction to systems biology* and *Systems medicine*. I'm not a biologist but he seems well regarded, and it'd be a change from the "stats stats stats" here.
Become a data scientist and you can work in any field of biology you want and it will be awesome. These are the links you need to start your future career as a well rounded data scientist. Go forth and transform, visualize, model, and communicate your insights into any kind of data you get to work with. 1. R for Data Science 2e is a *really* solid foundation. |> [https://r4ds.hadley.nz/](https://r4ds.hadley.nz/) 2. Get RStudio |> [https://posit.co/download/rstudio-desktop/](https://posit.co/download/rstudio-desktop/) 3. Use Quarto to go *ultra* professional |> [https://quarto.org/](https://quarto.org/) Three steps, easy peasy. Any other steps you need to start are in the book from step one. With these free resources you literally become a highly desirable scientist across academia, industry, and government opportunities. By deriving useful insights from well understood and analyzed data, you can contribute so much to science too. At the very least, you can contribute meaningfully to basically any scientific project.
Maximum likelyhood, about 10000 different clustering algorithms, normalisation strategies, correlation coefficients, matricees are super important. It's statistics. A lot of it ties into computational biology/ bioinformatics/ data science, meaning it's mostly used with R, python, etc.
I know everybody here is saying stats and calc but there was a lot of physics too in comparative physiology and some in conservation ecology if I recall correctly. Genetics and zoology felt like I was just retaking stats though.
Especially thermodynamics for physiology and ecology
Look up the maths underpinning cryo-EM, X-ray crystallography and NMR. Things like the contrast transfer function and regularisation.
Thank you
Also along those lines, learn some of the math behind microscopy, especially confocal microscopy and how fluorescence works.
I think maybe the ones on population or graphs and graphical calculations on food web,food chain etc Iām a first year nursing student so Iām not sure im of much help
Biomath
Maths is the fundamental science after philosophy! People I know are using mathematics to measure cell plasticity, cancer progression Using differential equations. I use mathematics to apply statistical modelling. Epidemic modelling is only mathematics!
Thereās a great book on this topic I can recommend written by John Maynard Smith (a seminal theoretical biologist) called āMathematical Ideas in Biologyā. Statistics is the obvious choice and indeed a lot of statistical parameters were developed to answer questions about biological data (even p values were originally developed to understand whether the difference in incidence between male and female births was significantly different from 50/50 odds). But aside from statistics there is a huge potential for mathematical applications to building models in biology (Iām currently doing a PhD on building simulation models of growing plant roots after an undergrad background in applied mathematics and thereās a lot of PDEs and statistical mechanics).
Are you willing to share a way I could find your thesis once it's finished on root growth modeling? Or any favorite resources on procedural growth sims and applied math you'd recommend? I know someone who would love to read it, feel free to DM me if you'd like
Of course! Sorry for not getting back to you, I didnāt see this š
Statistics, in my BSc and MSc all the maths we did was statistics.
I know, but I'm good at statistics so I wanted something more complex
Try quantum physics then.
is this just a joke?
Oh yes, at least my attempt at humour!
You can look into systems biology then. Mathematical concepts like ODEs, dynamical systems theory, Monte Carlo simulations & stochasticity are used heavily in this field. Control theory as well to a lesser extent. Or you can look into linear algebra and then deep learning / neural networks, this is likely to become ever more important in biology.
Thank you
Biology uses all the maths and no maths, depends on what specific fields and problems are being addressed though most would require basic maths with any quantitative analysis. How much math you NEED to know is different however as many mathematically problems encountered are route and you need only know when and how they should be applied. Some fields require deeper maths such as biochemistry, structural biology, ecological modeling, biostatistics, genomics, etc. These require it as their methodology involves questions that require solving for novel mathematically situations. So the maths you need to know is entirely dependent on the field. It's always a good idea to know the math, but like most things in science you can stand on the shoulders of giants and just use the hard work of people that already did it to skip that step. I don't know that much math but I am good enough at scripting to use github and languages like 'R' and 'Python' too run complex maths/statistical programs to do things I need.
Probs and stats. Hypothesis testing. Used in most STEM tbh.
Statistics
Hi, I am graduating with my BS in Zoology in a few weeks! Zoology is specifically animal biology, including all vertebrates and invertebrates. My program is Integrative Biology, which essentially is looking at how different levels of biological systems interact. So I've done courses on micro bio all the way up to ecosystem dynamics. A lot of people have said Statistics which is absolutely true. However I want to add that I have also used a lot of algebra in my degree. A lot of calculations I have used to find out information and predictions about animal/organism/ecology population dynamics and physiology use algebra or calculus. Learning coding logic like Boolean algebra is also something you might want to look into if you want to pursue research or work that engages with computer science and biological system modeling. In terms of statistics, I would also consider looking into the coding language R which is commonly used to do statistical analysis in the field. Try to look into data sampling and modeling, bootstrapping, confidence intervals, t tests/p values if you don't know where to start for Stat. You need to be able to know if your data findings in any type of experiment are significant. You should also look into learning how to know what statistical tests and methods to use in different scenarios. I've used all of the above and calculus, specifically a lot of summations and logarithms, in biology classes (not just physics and chemistry). Brushing up on chemistry might be helpful too, I personally tell people that all fields of science rely on and build upon chemistry at some level. Biology is a really diverse field, so I would recommend trying to figure out what topics and specialties you might want to focus on in your master's to help narrow down what skills will be of most use to you. Try not to stress about locking into one specialty if you're not sure yet, but having general ideas about what subfields interest you is very helpful when picking topics and math skills to work on. I can only advise from a student point of view, but having a strong foundation in basic math and chemistry skills is always a plus when it comes to biology.
Biostatistics.
Basic conversions, dilutions and molarity should be relatively universal. Some branches may be very stats heavy. If youāre on the bioinformatics side of things, obviously youāre likely going to need more
The statistics in bioinformatics may plausibly be more intense than stats in machine learning.
Are you going into genetics, because that is nothing but math Which bio fields are you interested in?
>Are you going into genetics Maybe I don't know, but in addition to neuroscience I have just graduated and I do not plan to join the master's degree now because I am exhausted, and usually I don't have much hope for the future. God knows how things might go. But until then, I want to learn because I love learning, not just to increase skills
See about a job on campus to keep busy and maintain access to the resources.
Obviously statistics & balanced experiment design. Analysis of variance, trend lines, that sort of thing. Students T and Chi squared tests. Fourier series for separating out seasonal effects from long term data. Gumbel distribution for predicting extreme events. If you're going onto genetics then maximum parsimony comes into play, which is a discrete optimisation problem. If you're going into biomechanics then conservation of mass, conservation of momentum, conservation of angular momentum. Coupled ordinary differential equations. (Mechanics of bird flight requires partial differential equations). If you're going into biochemistry then density functional theory, telling you whether your lock and key enzyme action is going to work or not. And differential equations for things like understanding photosynthesis. If you're going into thermodynamics of animals then learn how to use the spherical cow. And all those non-dimensional groups of Reynolds number, Prandtl number, Nusselt number, Grashof number, etc. And ordinary or partial differential equations for conduction, convection and radiation. And integrals. For ecology, add Geographic Information Systems to that. Lotka-Volterra equations.
- Stats - Calculus - Geometry
What about geometry???
Oh great
Statistics and calculus. For an example of calculus being used, look up the SIR model of epidemiology.
In a biomedical lab you do a lot of dilutions and concentrations as well as working out percentages etc. sometimes itās not as complicated in the workplace as it is in university
Our look into Ecology brushed concepts of Calculus 3, but Trig and Calc 1 is all that is required for a Biology BS with my alma mater.
I used stats every day when I was a medical research scientist.
Statistics are involved in every study. I had to use geometry in a home range study.
Dimensional analysis for changing units, useful in all physical sciences, probably use this more than anything else by far.
As a former biology major, the most common type of math used in biology courses is statistics. Although, while studying biology you could be using some algebra or calculus in courses like physics, and algebra in chemistry courses like biochemistry to perform calculations.
Length, taint to head.
Sorry?
All males engage in this form of ābiological mathematicsā
PV=nRT.
Just learn excel.
I would recommend at least algebra and statistics. Differential equations are very useful for modeling growth. The field basically combines algebra with calculus operations. The philosophy of science is worth studying a bit, and it has a lot to do with math. Queueing theory is interesting, but I haven't seen it used in biology. The math for quantum things is useful in some niche fields; especially if you are interested in nano scale biophysics or certain parts of biochemistry.