How to make a meaningful contribution in computational research?

[Mépris]

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.

 

[Fightfish]

You might want to check out this book Numerical Recipes (website: http://www.nr.com/) for some ideas.

 

[chiro]

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.

 

[Mépris]

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.

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!

 

[Matrix0]

Hi there,

First of all, it's great that you are interested in learning to code and applying it to scientific research. Computational research is becoming increasingly important in many fields, and having these skills will certainly make you a valuable asset in your future career.

In order to make a meaningful contribution in computational research, there are a few things you should keep in mind. Firstly, it's important to have a strong foundation in the programming language you choose to use. Python is a great choice, especially since it is widely used in physics and has many available resources. However, if your professor is primarily using R in their research, it may be worth considering learning that language as well. Having knowledge of multiple programming languages can make you more versatile and adaptable in different research environments.

Secondly, it's important to have a solid understanding of the specific area of research you will be contributing to. In this case, it seems like your professor works on a diverse range of topics, so it would be beneficial to familiarize yourself with the basics of fluid dynamics, biostatistics, geostatistics, financial math, and numerical linear algebra. This will not only help you understand the specific project you will be working on, but also give you a broader understanding of how computational methods are applied in different fields.

As for making a convincing case to your professor, I would suggest highlighting your enthusiasm and willingness to learn. Show them that you have done your research on their work and have a genuine interest in contributing to it. You can also mention your previous experience or coursework in related subjects, as well as any specific skills or programming languages you have already learned. This will demonstrate your potential and make a strong case for why they should consider letting you contribute to their research.

Overall, the key to making a meaningful contribution in computational research is having a strong foundation in programming, a good understanding of the specific research area, and a genuine interest and enthusiasm for the project. Good luck with your endeavors!