From the system point of view, that is to say, when we consider a problem, we should not only see the problem itself and the field itself, but also link the problem to be solved with other problems in other fields. For example, to solve the problem of environmental pollution, if we just think about how to force factories and enterprises to reduce pollutant emissions by government decrees, we often fail; A smarter way is to start with the whole economic structure and industrial chain, adjust the policies of all links in the economic structure, and promote the improvement of "environmental protection" with overall changes. This is called systematic handling of problems.

The mathematical method can be simply understood as "mathematical modeling". The so-called modeling is to summarize and analyze practical problems with mathematical logic, such as finding out what are the conditions of self-change (independent variables), what are the results of change (dependent variables), and what kind of mathematical relationship exists between these changes. The initial development of mathematics is due to various difficulties in real life, and people need to develop a set of "all-inclusive" methods to solve these difficulties clearly, comprehensively and effectively. The development of physics has greatly promoted the development of mathematics, especially because physics is essentially to solve practical problems that people observe and think about.

It should not be difficult to understand how to solve problems with computers. Many practical problems we encounter now, whether natural science or humanities and social sciences, are very complicated. Secondly, we are no longer satisfied with a theoretical and abstract description, but must consider all the factors affecting practical problems as comprehensively as possible in order to make an understanding, summary and speculation as close as possible to the actual situation. This process must be solved by computer, because the advantage of computer is that its operation efficiency is much faster than that of human brain, and it is especially suitable for large-scale data processing (such as images, sounds, numerical tables, words, etc. ).

In a word, this is a very general statement, which actually sounds like "big talk". If you put it on your resume, it doesn't make any practical sense to understand, but it's good to have a strong facade.