1.3: Computing

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The computing side of Scientific Computing is generally very important as well. As mentioned above, most of the problems studied get much too large or complicated to solve analytically (generally as a mathematical problem) and some sort of approximation is needed.

In many cases, there are existing algorithms that are developed to solve some underlying mathematical problem and the main focus is on setting up the problem is the right way. For example, if there is a differential equation to solve, a package can be loaded to use a particular differential equation solver and then the results need to be analyzed.

But in general, those that succeed in Scientific Computation are good at the coding/computation side of things. It takes such skills to manage problems common in this fields.

I want to emphasize that Scientific computation, however, is not just computer programming. It takes a blend of skills to succeed at this. One needs a fairly deep knowledge of the field they are working in to understand how solving the problem using some algorithm is generating the correct answer.