Automation
Automation requires putting models (abstraction of real-world objects/phenomena) into action to solve problems. This is achieved by creating algorithms, implementing the algorithms in program code (instructions), implementing
the models in data structures, and executing the code.
Modelling
Computer science is about building clean abstract models (abstractions) of messy, noisy, real-world objects or phenomena. Computer scientists have to choose what to include in models and what to discard, to determine the
minimum amount of detail necessary to model in order to solve a given problem to the required degree of accuracy. The degree of accuracy required for the success of the Philae lander project that put a vehicle on a moving
comet would be far higher than that required for a delivery drne robot.
Uses of models
The weather forecasts we rely on are based on complex meteorological models. Businesses make use of tools such as spreadsheets to model potential business scenarios and see how much profit can be made. Simulators can allow pilots
to train in a safe environment before they ever actually take control of a real plane. Wind tunnels and similar simulators can help look at the effects of the environment on a design.
Knowledge check
There are not enough questions for ths topic. A modelling question will be included in the next knowledge check.