How to make a meaningful contribution in computational research?

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Discussion Overview

The discussion revolves around how to make a meaningful contribution in computational research, particularly in the context of learning programming languages like Python and R for scientific applications. Participants explore the relevance of these languages in various fields such as fluid dynamics, biostatistics, geostatistics, financial mathematics, and numerical linear algebra.

Discussion Character

  • Exploratory
  • Technical explanation
  • Conceptual clarification
  • Homework-related

Main Points Raised

  • One participant expresses interest in learning to code, specifically Python, for its applications in physics and availability of resources.
  • Another participant suggests that learning R is beneficial for quantitative analysis and modeling, especially for manageable data sets.
  • It is noted that R has an extensive number of packages that facilitate various statistical and graphical techniques, which may be advantageous for the participant's intended research areas.
  • A participant mentions the book "Numerical Recipes" as a potential resource for ideas related to computational methods.
  • There is a request for recommendations on beginner-friendly tutorials or books for learning R, indicating a desire for accessible learning resources.

Areas of Agreement / Disagreement

Participants generally agree on the utility of learning R for quantitative analysis and its advantages in terms of available packages. However, there is no consensus on the best approach to learning R or the specific resources to use, as different participants have not yet provided definitive recommendations.

Contextual Notes

The discussion reflects varying levels of experience with programming and computational research, and the suggestions made are contingent on the participant's specific research interests and data complexity.

Who May Find This Useful

This discussion may be useful for individuals interested in starting a career in computational research, particularly those looking to learn programming languages applicable to scientific fields.

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!
 

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