Abstraction is the primary way to handle complexity in programming. It allows you to escape the implementation details and focus on the essentials.
A good abstraction example is the array sort function. It doesn't matter how it's implemented; what matters is that it leads to the result we're interested in.
Another example is higher-order functions, such as map, filter, and reduce, which allow you to process collections without thinking about what goes on under the hood. Collections even don't have to be flat; you can build such functions for any complex structures, such as trees. And due to this abstraction, we can focus on the processing itself rather than the data traversal technicalities.
On the other hand, the data itself often has a complex structure. Representing a user in an unconventional system may require defining dozens or hundreds of different parameters and the associated data. In situations like this, it's a good idea to hide this structure behind a set of functions, which cover the internal complexity and simplify code support. This is called data-driven abstraction.
In this course, we'll learn some basic principles of program design, and how to model and represent objects from the real (and imaginary) world via a program. We'll design a library for working with graphical primitives, such as points, segments, and shapes. This library is lucid enough for everyone to understand (even visually), and it's pretty handy to express with code.
Main course subjects:
- Subject area
- Ontology
- Level design (abstraction barriers)
- Invariants
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