Data Structures And Algorithms In Dart
This series will be about Data Structures and Algorithms in Dart . I have been a Flutter developer for three years now and decided to write this series to help my fellow developers understand Data Structures and Algorithms in Dart. I believe programming is like dancing and we dance to the ALGO-RYTHMS
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What are Data Structures?
Data structures are ways of organizing and storing data in a computer so that it can be accessed and modified efficiently. Different kinds of data structures are suited to different kinds of applications, and some are highly specialized to specific tasks.
What are Algorithms ?
An algorithm is a set of steps for solving a specific problem or achieving a desired goal
Why Data Structures?
Data structures are important because they enable efficient organization and manipulation of data. Different data structures are suited to different kinds of applications, and choosing the right data structure for a particular problem can have a big impact on the efficiency of the program.
For example, suppose you want to store a large number of records, such as a list of names and addresses. If you use an array to store the data, you can access any element in the array directly using an index. This makes it easy to look up a specific record, but it is not very efficient for inserting or deleting records, since you would have to shift all of the elements after the insertion or deletion point.
On the other hand, if you use a linked list to store the data, you can easily insert and delete elements anywhere in the list, but you have to access elements sequentially, which is not as efficient as using an index.
By understanding the strengths and limitations of different data structures, you can choose the one that is best suited to the needs of your application. This can help you write more efficient, effective code.Data structures are important because they enable efficient organization and manipulation of data. Different data structures are suited to different kinds of applications, and choosing the right data structure for a particular problem can have a big impact on the efficiency of the program.
For example, suppose you want to store a large number of records, such as a list of names and addresses. If you use an array to store the data, you can access any element in the array directly using an index. This makes it easy to look up a specific record, but it is not very efficient for inserting or deleting records, since you would have to shift all of the elements after the insertion or deletion point.
On the other hand, if you use a linked list to store the data, you can easily insert and delete elements anywhere in the list, but you have to access elements sequentially, which is not as efficient as using an index.
By understanding the strengths and limitations of different data structures, you can choose the one that is best suited to the needs of your application. This can help you write more efficient, effective code.
How do we classify Data Structures?
There are several ways to classify data structures:
Linear vs. Non-Linear: Linear data structures arrange data elements in a linear sequence, while non-linear data structures do not have a specific order. Examples of linear data structures include arrays, linked lists, and stacks. Examples of non-linear data structures include trees and graphs.
Sequential vs. Non-Sequential: Sequential data structures access data elements in a fixed order, while non-sequential data structures do not have a specific order for accessing elements. Examples of sequential data structures include arrays and linked lists. Examples of non-sequential data structures include trees and hash tables.
Static vs. Dynamic: Static data structures have a fixed size and cannot be modified once created, while dynamic data structures can change size and can be modified. Examples of static data structures include arrays, which have a fixed size once created. Examples of dynamic data structures include linked lists and trees, which can change size as elements are added or removed.
Homogeneous vs. Heterogeneous: Homogeneous data structures contain elements of the same type, while heterogeneous data structures can contain elements of different types. Examples of homogeneous data structures include arrays, which can only contain elements of the same type. Examples of heterogeneous data structures include trees, which can contain elements of different types at different nodes.
Simple vs. Complex: Simple data structures have a basic structure and are easy to implement, while complex data structures have a more intricate structure and may be more difficult to implement. Examples of simple data structures include arrays and linked lists, while examples of complex data structures include trees and graphs.
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