Data Structures are the programmatic way of storing data so that data can be used efficiently. Almost every enterprise application uses various types of data structures in one or the other way. This tutorial will give you a great understanding of the Data Structures needed to understand the complexity of enterprise-level applications and the need for algorithms and data structures. Coding can prove helpful in making your data structure journey fruitful. Visit the Edureify website now to know about the courses that we provide.
Why Learn Data Structure and Algorithms?
As applications are getting complex and data-rich, there are three common problems that applications face nowadays.
- Data Search − Consider an inventory of 1 million(106) items of a store. If the application is to search an item, it has to search an item in 1 million(106) items every time slowing down the search. As data grows, the search will become slower.
- Processor speed − Processor speed although very high, falls limited if the data grows to billion records.
- Multiple requests − As thousands of users can search data simultaneously on a web server, even the fast server fails while searching the data.
To solve the above-mentioned problems, data structures come to the rescue. Data can be organized in a data structure in such a way that all items may not be required to be searched, and the required data can be searched almost instantly.
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Applications of Data Structure and Algorithms
The algorithm is a step-by-step procedure, which defines a set of instructions to be executed in a certain order to get the desired output. Algorithms are generally created independent of underlying languages, i.e. an algorithm can be implemented in more than one programming language. Join the coding courses provided by Eduriefy to know about all of this in a detailed manner.
From the data structure point of view, the following are some important categories of algorithms −
- Search − Algorithm to search an item in a data structure.
- Sort − Algorithm to sort items in a certain order.
- Insert − Algorithm to insert an item in a data structure.
- Update − Algorithm to update an existing item in a data structure.
- Delete − Algorithm to delete an existing item from a data structure.
The following computer problems can be solved using Data Structures:-
- Fibonacci number series
- Knapsack problem
- Tower of Hanoi
- All pair shortest path by Floyd-Warshall
- Shortest path by Dijkstra
- Project scheduling
Execution Time Cases
Three cases are usually used to compare various structures’ execution times relatively.
- Worst Case − This is the scenario where a particular data structure operation takes the maximum time it can take. If an operation’s worst-case time is ƒ(n) then this operation will not take more than ƒ(n) time where ƒ(n) represents the function of n.
- Average Case − This is the scene depicting the average execution time of an operation of a data structure. If an operation takes ƒ(n) time in execution, then m operations will take mƒ(n) time.
- Best Case − This is the scene depicting the least possible execution time of an operation of a data structure. If an operation takes ƒ(n) time in execution, then the actual operation may take time as the random number which would be maximum as ƒ(n).
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Basic Terminology
- Data − Data are values or sets of values.
- Data Item − Data item refers to a single unit of values.
- Group Items − Data items that are divided into sub-items are called Group Items.
- Elementary Items − Data items that cannot be divided are called Elementary Items.
- Attribute and Entity − An entity is that which contains certain attributes or properties, which may be assigned values.
- Entity Set − Entities of similar attributes form an entity set.
- Field − A field is a single elementary unit of information representing an attribute of an entity.
- Record − A record is a collection of field values of a given entity.
- File − A file is a collection of records of the entities in a given entity set.
Algorithms
The algorithm is a step-by-step procedure, which defines a set of instructions to be executed in a certain order to get the desired output. Algorithms are generally created independent of underlying languages, i.e. an algorithm can be implemented in more than one programming language.
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Characteristics of an Algorithm
Not all procedures can be called an algorithm. An algorithm should have the following characteristics −
- Unambiguous − The algorithm should be unambiguous. Each of its steps (or phases), and their inputs/outputs should be clear and must lead to only one meaning.
- Input − An algorithm should have 0 or more well-defined inputs.
- Output − An algorithm should have 1 or more well-defined outputs and should match the desired output.
- Finiteness − Algorithms must terminate after a finite number of steps.
- Feasibility − This should be feasible with the available resources.
- Independent − An algorithm should have step-by-step directions, which should be independent of any programming code.
How to Write an Algorithm?
There are no well-defined standards for writing algorithms. Rather, it is problem and resource-dependent. Algorithms are never written to support a particular programming code.
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We write algorithms in a step-by-step manner, but it is not always the case. Algorithm writing is a process and is executed after the problem domain is well-defined. That is, we should know the problem domain, for which we are designing a solution.
Example
Let’s try to learn algorithm-writing by using an example.
Problem − Design an algorithm to add two numbers and display the result.
Step 1 − START
Step 2 − declare three integers a, b & c
Step 3 − define values of a & b
Step 4 − add values of a & b
Step 5 − store output of step 4 to c
Step 6 − print c
Step 7 − STOP
Result:-
Step 1 − START ADD
Step 2 − get values of a & b
Step 3 − c ← a + b
Step 4 − display c
Step 5 − STOP
Algorithm Analysis
The efficiency of an algorithm can be analyzed at two different stages, before implementation, and after implementation. They are the following −
- A Priori Analysis − This is a theoretical analysis of an algorithm. The efficiency of an algorithm is measured by assuming that all other factors, for example, processor speed, are constant and have no effect on the implementation.
- A Posterior Analysis − This is an empirical analysis of an algorithm. The selected algorithm is implemented using a programming language. This is then executed on the target computer machine. In this analysis, actual statistics like running time and space required, are collected.
Frequently Asked Questions (FAQs)
Question:- What are the data structures and algorithms?
Answer:- A data structure is a named location that can be used to store and organize data. And, an algorithm is a collection of steps to solve a particular problem. Learning data structures and algorithms allow us to write efficient and optimized computer programs.
Question:- Which language is best for DSA?
Answer:- Most competitive programmers use C++ because of its efficiency for DSA. That being said, the language is just a medium and any language that you are affluent with is appropriate for you to implement DSA.
Question:- Which data structure is easiest?
Answer:- An array is the simplest and most widely used data structure. Other data structures like stacks and queues are derived from arrays.
