• Collections
• Abstraction
• Abstract Data Type
• Recursion  
• Performance 
• Algorithms  

 

1. Collections

DSA Collections operates on a commission basis, based on the amount of debt recovered. All letters, calls, and basic skip tracing are included; you only pay if we collect. If legal action is required, we will discuss the costs and procedures with you and obtain your permission before proceeding.

 

Collections are made up of entities and relationships.

 

• SET: Distinct elements, unsorted, no relationships
• SEQUENCE: Ordered elements, successor relationship, elements may be sorted 

• TREE: Ordered/unordered elements, parent/child relationship
• GRAPH is (V,E) :V element set, E edge set 

 

1. Abstraction

The definition of Abstract data type only mentions what operations must be performed, not how these operations will be carried out. It is called “abstract.” 

1. Abstract Data Type

In other words, abstract data types are entities that define data and operations but do not include implementation details. In this case, we know what data is being stored and what operations can be performed on it, but we don’t know the implementation details. The lack of implementation details is due to the fact that each programming language has a unique implementation strategy. 

 

An ADT is implementation-independent.

•  it is implemented as an entity + attributes + relationships
• The actual data structure is hidden using get/set functions for each attribute + creating an entity to provide a reference to an entity.
•  Other operations are implemented as implementation-independent (abstract) functions (methods).

As a result, an abstract sequence can be used as

• Stack – add/remove restricted to position  
• Queue – add restricted to the last position / remove restricted to the first position

 

Refer to the data structures and algorithms online course for more information on data types. 

1. Recursion  

Recursion is the process by which a function directly or indirectly calls itself, and the corresponding function is referred to as a recursive function. Certain problems can be easily solved using a recursive algorithm. A recursive function solves a specific problem by calling itself and solving smaller subproblems of the original problem. As and when needed, many more recursive calls can be generated. It is critical to understand that we must provide a specific case in order to end this recursion process. So we can say that the function calls itself with a simplified version of the original problem every time.

 

Properties of Recursion 

• Performing the same operations with different inputs multiple times.
• We try smaller inputs at each step to make the problem smaller.
• A base condition is required to terminate the recursion; otherwise, an infinite loop will occur.

 

1. Performance 

Predicting the resources an algorithm requires to perform its task is called algorithm performance.

There are numerous options for getting from city “A” to city “B.” We can travel by plane, bus, train, or bicycle. We select the one that best suits us based on availability and convenience. Similarly, multiple algorithms exist in computer science to solve a problem. When we have multiple algorithms to solve a problem, we must choose the best one. Performance analysis assists us in selecting the best algorithm from a set of algorithms to solve a problem.

 

The following factors determine the performance of an algorithm:

• Is that algorithm providing an exact solution to the problem?
• Is it simple to understand?
• Is it simple to implement?
• How much is memory (space) required to solve the problem?

 

1. Algorithms  

The term “algorithm” is being used much more frequently than in the past. One of the reasons is that scientists have discovered that computers can learn on their own if given a few simple instructions. Algorithms are essentially mathematical instructions.

 

Characteristics of an Algorithm

• Clear and Unambiguous
• Well-Defined Inputs
• Well-Defined Outputs
• Finite-ness
• Language Independent

• Sequence

• Sort algorithms
• Search algorithms
• Hashing & collision detection
• Heap

• Tree

• Navigation
• General tree

• Graph 

• Depth‐first search & spanning forest
• Dijkstra’s (+SPT), Floyd, Warshall, Prim

 

Summary

Data Structures is a broad topic encompassing more than just stacks, queues, and linked lists. Other data structures include Maps, Hash Tables, Graphs, and Trees. Each data structure has advantages and disadvantages that must be balanced against the application’s needs. To learn more about DSA, check out the top data structures and algorithms course designed for working professionals. 

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