Summary

In this chapter, we discussed the basic concepts of scientific computing and its definitions. Then we covered the flow of the scientific computing process. Next, we briefly discussed some examples from a few science and engineering domains. After the examples, we explained an effective strategy to solve complex problems. After that, we covered the concept of approximation, errors, and related terms.

We also discussed the background of the Python language and its guiding principles. Finally, we discussed why Python is the most suitable choice for scientific computing.

In the next chapter, we will discuss various mathematical/numerical analysis concepts involved in scientific computing. We will also cover various Python packages, toolkits, and APIs for scientific computing.