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How much math is required for grad school (biology)?


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The year I entered into my biology program (undergrad) Calc I and II, along with a basic intro Stats course were all that were required for the program.  Shortly after I matriculated the school changed this policy to Calc I and Stats only.  I have also seen biology programs (undergraduate) where only a terminal "calculus for business and social sciences" was the the required advanced math course.  

 

The programs I am looking at seem to stick to wanting only one year of Calc, where a few state they would like to see a course or two beyond Calc I&II, yet state no indication of what.  One program specifically states a year of Calc is required but they also want to see Calc through Diff. Eq. in the least. 

 

I am asking the question because I recently came across an article and discussion by current and former biology grad students that make the plea for potential biology students (grad) to take Calc I&II, Linear Algebra, Diff. Eq. and either mathematical modeling of some sort or computer programming.  For the current grad students, is this true?  

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I think that this really depends on your reserach area. As a math/computational sort of person involved with biophyical modeling reserach, I of course highly recommend some basic programming courses along with linear algebra. I also think that a course in physical chemistry and/or statistical mechanics is helpful. We have some undergrad bio majors in our lab and they need programming and phyics knowdege to do well in their reserach.

 

In general, I am just in favor of a well rounded undergrad education. I wish that I would have taken more chemistry and physics courses (I was a math major who focused on biomath research). I would think about the research areas that you may want to pursue and then definitely consider taking some extra courses in other departments that would round out your knowledge of that research area. This may be some additional math or computer courses but it may be extra physics or chemistry or animal science, etc instead. I would focus on preparing yourself based on your research interests, not necesarily just taking extra math without a reason for taking it. Also, if you find that you have gaps in knowedge in grad school (by if, i really mean when) then you can buy some extra books and learn the material and it isnt a big deal. This is what I am doing with orgranic chem right now!

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On a related note: it doesn't matter what subfield you are in, chances are you are going to take that breadth course with that that professor who doesn't want you to use excel and wants you to use R,Matlab, Python ect to solve the problems. This happened when my MSc advisor taught a class: Statistics for Earth Scientists (which I am sure there will be a biology equivalent) which included some basic programming a probably intermediate level statistics (writing own code to bootstrap, markov chains ect). The students (all the ones who weren't in my advisors lab) who weren't comfortable with at least a high level programming language and some linear algebra (row-reduction, pretty easy to learn) had a lot of trouble with assignments that took the ones who were comfortable less than an hour. So while they didn't need previous experience, it certainly made their semester a lot rough than it had to be: you don't need to be a mathematician or a computer scientist, but going slightly beyond the requirements when you have some free time might payoff in a big way. 

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If you are interested in additional computer classes, I definitely agree that the ones suggested by GeoDude (Matlab, R, and Python) should be the first go to ones to learn. They are all extremely easy to pick up. I use R and Matlab daily for things like sorting data or making charts that would take forever in excel. Python is my go to scripting language for file manipulation or messing around with data. Shell scripting and unix commands are also great, especially if you will do any sort of cluster computing.

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I can say that I personally took a calculus and a statistics course in undergrad and didn't run into any problems during application season last year.

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Definitely agree with GeoDUDE! and bsharpe269 about picking up a programming language like Python, R, MATLAB. I'd favour the free ones though, because not every school you go to in the future will always have licenses for everyone.

 

I TA a stats class for my advisor and we noticed a huge difference in students who were able to program and those who were not familiar with programming concepts. Ability to write code was a pre-req for the class though (it's a grad class) but we didn't require them to use any particular language. However, we surveyed the class and those who knew how to code typically spent about 6 hours on homework per week (the amount we want them to spend) but those who did not often spent over 10-15 hours per week. So, I agree that programming ability is something very useful for many quantitative fields!

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  • 2 weeks later...

Since posting my OP I have downloaded R, Octave, Maxima, iPython, Scilab, Sage, and FreeMat.  I am really digging R, but haven't played around with it too much lately.  Python/iPython looks awesome, but way above my abilities.  I have had problems with the rest and despite being "community" projects documentation and tutorials seems scarce.  Those that do exist for beginners seem to be aimed at the type of beginner who already somewhat knows what is going on.  I have been using Linux off and on since 2007 and to be frank I am tired of the "if it is not working you are doing it wrong" attitudes found amongst the masses of the open source communities.  

 

I have also recently purchased Matlab with the Symbolic Toolkit because the student version was cheap enough.   There seems to be tons of documentation and the attitudes form the "community" towards newbies seems a heckofalot more welcoming.  I would like to keep R, Python, and Matlab for now and potentially get into one of the others more intensely in the future.   

 

In terms of what I would like to do I am fairly certain computer modeling/simulation would play a role.  

 

Thanks for the replies.   

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OP, Im glad your enjoying R! My one bit of advice is to not just go with GUI stuff like R and matlab. It is a great place to start and matlab is great IF you actually program with it (as in .m files where you actually open and write to files, manipulate vectors, you can even do parrelization stuff with matlab), not just make plots and multiply numbers and all on the interface. Python is my go to scripting language and I at least write a few scripts a week with it and I had never heard of ipython until you mentioned it. Maybe I am just out of loop but I would challenge you to focus less on stuff like the ipython GUI and actually just open up the terminal, emacs a .py file and write a program. Honestly, you probably will not find good tutorials and I dont think thats the best way to learn programming anyway. I would find a website that has python program assignment ideas for beginners and learn by doing those. They will take a while and you will have to google every fucntion at the beginning but you will slowly learn. Please feel free to PM me if you need help getting started. Programming is one of those things that seems REALLY hard to start and as soon as you write your first few programs, you will feel much more comfortable and will be able to learn it on your own.

 

Also, there will be a coursera on python starting next month which might be a great way to get started! There may be other programming courses on there as well which could help. Here is the link:

https://www.coursera.org/course/interactivepython

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I believe I have a Coursera account...

 

I am familiar with CLI from my time with Linux and have been using it more and more with Terminal (Mac).  I have Xcode installed and even went as far as setting up an Apple Developer ID so I could download the XCode CLI; the .m files I have saved so far will open in XCode unless I retrieve them from within Matlab.  

 

I am okay with using Terminal to do some basic stuff but I am totally lost on how to use for computational computing or scripting. I can do some basic arithmetic, and that is about all.  

 

I will check out that Coursera course.  

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