As a newbie myself, I can see how this topic can be a little daunting or even pointless at times. However, understanding how algorithms and data structures work fundamentally will lay the foundation for all of your future work in the information technology field.
All computers depend on fundamental algorithms and data structure, so better understanding
them helps you better understand the machine. The structure of knowledge and the algorithm
function together since they are interdependent. Before designing the actual algorithm, the
choice of proper data structures is needed.
Only for specific data structures can certain algorithmic designs and data structure techniques
be used, while some for some data structures cannot be used. To construct the most effective
structure, in order to optimize the data structure, you must decide which algorithm is most
appropriate for that particular data structure. With that being said, each data system has its
own advantages and disadvantages. In all cases, it's uncommon to have a data structure that is
better for use than another structure. The inferior data structure would finally be forgotten if
this were the case and replaced by the superior data structure. Queue, stack, tree, connected
lists, etc are some general examples of data structures (Dourish, Et al, 2016). It can decide
which data structure should be used for each case based on the necessary usage of the
software, as well as the choice of the developer, as they are not universal algorithms.
The designer needs to follow a design strategy as the design process includes taking the
requirements and developing solutions to the problems. It should function correctly in all
situations when designing a solution plan. The individuals who use the framework are not
aware of the nature of the software that you have implemented. There is therefore a machine
manual that is a detailed guide to how the design was accomplished. Through following one
of the two decomposition methods, top-down or bottom-up, a large program should be
divided into small modules and submodules (Bianconi, Filippucci & Buffi, 2019). Execution
time or storage requirements are other significant factors on which software may be
measured.
References
Dourish, P. (2016). Algorithms and their others: Algorithmic culture in context. Big Data &
Society, 3(2), 2053951716665128.
De Veaux, R. D., Agarwal, M., Averett, M., Baumer, B. S., Bray, A., Bressoud, T. C., ... &
Kim, A. Y. (2017). Curriculum guidelines for undergraduate programs in data
science. Annual Review of Statistics and Its Application, 4, 15-30.
Bianconi, F., Filippucci, M., & Buffi, A. (2019). Automated design and modeling for mass-
customized housing. A web-based design space catalog for timber structures. Automation in
construction, 103, 13-25.