How to make a meaningful contribution in computational research?

AI Thread Summary
Learning to code, particularly in Python and R, is essential for contributing to scientific research, especially in fields like fluid dynamics, biostatistics, geostatistics, financial math, and numerical linear algebra. Python is widely used in physics and has abundant resources, including online courses. However, R is highly recommended for quantitative analysis and modeling due to its extensive packages that facilitate various statistical techniques and data visualization. For manageable datasets, R is particularly effective for quick results. To prepare for a potential contribution to research, focus on learning R, as it aligns well with the professor's work. Resources like the book "Numerical Recipes" can provide further guidance. When starting with R, seeking beginner-friendly tutorials or books is advisable for effective learning.
Mépris
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Hey,

I will soon learn to code and would like to get a feel of how it's used in scientific research. I was thinking of using Python as it's used in physics a lot (apparently) and there are a lot of resources for it, even an OCW course.

I know a prof who might be able to let me contribute a little to his work. I haven't asked him anything yet but he's really cool and I'm sure he'll at least consider it. He works on fluid dynamics, biostatistics, geostatistics, financial math and numerical linear algebra.

My understanding is that making a meaningful contribution would be much easier in something computational as opposed to something like pure math. I don't expect to do anything extraordinary but I would just like to know what I should be learning in order to make a convincing case to that prof? I don't want to waste (too much of) his time. Apparently R is used a lot in geostats, so perhaps I should learn that instead?

Thank you.
 
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You might want to check out this book Numerical Recipes (website: http://www.nr.com/) for some ideas.
 
Mépris said:
Hey,

I will soon learn to code and would like to get a feel of how it's used in scientific research. I was thinking of using Python as it's used in physics a lot (apparently) and there are a lot of resources for it, even an OCW course.

I know a prof who might be able to let me contribute a little to his work. I haven't asked him anything yet but he's really cool and I'm sure he'll at least consider it. He works on fluid dynamics, biostatistics, geostatistics, financial math and numerical linear algebra.

My understanding is that making a meaningful contribution would be much easier in something computational as opposed to something like pure math. I don't expect to do anything extraordinary but I would just like to know what I should be learning in order to make a convincing case to that prof? I don't want to waste (too much of) his time. Apparently R is used a lot in geostats, so perhaps I should learn that instead?

Thank you.

Hey Mepris.

R is something that is a good idea to learn especially for quantitative analysis and modelling. If the data you are using is manageable (i.e. not too large or complex), then this is a very good idea.

In terms of getting results quickly, I don't think it's easy to beat something like R considering the number of packages for R is extensive and what the packages can do matches that in a good way.

There are packages for all kinds of techniques including matrix techniques, statistical and probabilistic techniques, as well as methods to create fancy graphs with customization using one or two lines of code.

You can do work in R that relates to all the areas you have mentioned, but I imagine you will have specific kinds of analyses for particular methods and particular sub-specialties within these areas themselves.

You can download R freely and all the packages and also the documentation as well.
 
Fightfish said:
You might want to check out this book Numerical Recipes (website: http://www.nr.com/) for some ideas.

Thank you. I will keep this in mind for future use.

chiro said:
Hey Mepris.

R is something that is a good idea to learn especially for quantitative analysis and modelling. If the data you are using is manageable (i.e. not too large or complex), then this is a very good idea.

In terms of getting results quickly, I don't think it's easy to beat something like R considering the number of packages for R is extensive and what the packages can do matches that in a good way.

There are packages for all kinds of techniques including matrix techniques, statistical and probabilistic techniques, as well as methods to create fancy graphs with customization using one or two lines of code.

You can do work in R that relates to all the areas you have mentioned, but I imagine you will have specific kinds of analyses for particular methods and particular sub-specialties within these areas themselves.

You can download R freely and all the packages and also the documentation as well.

Hey Chiro,

Thank you for all the information.

How would you recommend I go about learning R? I understand that you have been programming for a long time now, so perhaps you don't know of any n00b-friendly tutorials/books, but in case you know of some, throw 'em my way!
 
Hey, I am Andreas from Germany. I am currently 35 years old and I want to relearn math and physics. This is not one of these regular questions when it comes to this matter. So... I am very realistic about it. I know that there are severe contraints when it comes to selfstudy compared to a regular school and/or university (structure, peers, teachers, learning groups, tests, access to papers and so on) . I will never get a job in this field and I will never be taken serious by "real"...
Yesterday, 9/5/2025, when I was surfing, I found an article The Schwarzschild solution contains three problems, which can be easily solved - Journal of King Saud University - Science ABUNDANCE ESTIMATION IN AN ARID ENVIRONMENT https://jksus.org/the-schwarzschild-solution-contains-three-problems-which-can-be-easily-solved/ that has the derivation of a line element as a corrected version of the Schwarzschild solution to Einstein’s field equation. This article's date received is 2022-11-15...

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