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Ashish Kumar

Stacks in Data Structures:Implementation

Stacks are a form of linear data structure that allows insertion and deletion operations from the top end and uses the Last-In-First-Out (LIFO) concept. Contiguous memory, an array, and non-contiguous memory, a linked list, can be used to implement the stack. Many apps rely heavily on the stack.

This illustration enables you to carry out activities solely from one end, similar to adding and removing books from the top of a stack. This indicates that insertion and deletion are only possible in the stack data structure from the top of the stack. You can only reach the top of the stack at any given time.

Push operations refer to the act of adding a new element to the stack.
The word "pop operation" refers to removing or deleting elements from the stack.

The representation of stacks in data structures in real-time will then be demonstrated.

Representation of Stacks in Data Structures

Stack operation in data structures

Let's say you have a stack of books now.

Only the top book, number 40, which is kept at the top of the stack, is visible.
You must update the top before inserting a new text if you wish to insert a new book first, say 50.

Additionally, you must first remove the top book from the stack to reach any other book besides the 40th-highest book in the stack. After that, the top will then point to the subsequent top book.

After working on the representation of stacks in data structures, you will see some fundamental operations being performed on data structure stacks. For detailed knowledge, head over to data structure training led by MAANG experts.

Simple Stack Operations in Data Structures

The operations that are used with the stack are as follows.
Operation by Push

In a push operation, additional components are added to the stack. It adds the new element to the top of the stack because you can only add a unique element from one end.

Operation Pop
Since there is only one end of the stack to complete, the term "pop operation" refers to taking the element out of the stack once again. Pop operation refers to removing an element from the top of the stack.

Operation Peek
When performing a peek action, the highest member in the stack is retrieved without being taken out of the collections of data elements.

Stack application in data structures

The top 7 uses of the stack in data structures are as follows:

Evaluation and Conversion of Expression
Calling the backtracking function in parentheses examining the syntax of string reversal Memory Management Parsing

You can now understand each programme individually.

Evaluation and Conversion of Expression
In programming, there are three different expression types that you can employ:

Infix Expression: An infix expression consists of a single letter or operator followed by two consecutive single infix strings.

A prefix expression consists of one letter or one operator followed by two prefix strings.

Postfix Expression: A postfix expression is a single letter or operator preceded by two postfix strings (also known as reverse polish notation).
The stack similarly evaluates and converts certain expressions, such as infix to prefix or infix to postfix.

Going backwards
The recursive algorithm mechanism of backtracking is employed to address optimization issues.

There are several ways to solve the optimization problem with backtracking; it is irrelevant which one is right. You keep the previously calculated issues in the stack and use that solution to address the following problems as you search through all potential solutions while backtracking.

Backtracking is a recursive algorithm that is used to solve problems, and the N-queen problem is an example.

Call a Function
In programming, the reference of the caller function is stored in the stack whenever a function is called from another function. With the aid of references saved in the stack, the programme control returns to the function call after the function call has ended.
Therefore, the stack is crucial when you call a function from another function.

Parentheses Checking
When using matching opening and closing brackets in programming, stack in data structures is used to determine whether parentheses like () are legitimate or not.
It manages the program's flow by storing all of these parentheses in the stack.
For instance, ((a + b) * (c + d)) is correct, but a+b))*(b+d)] is incorrect.

Reversing a String
String reversal is a fascinating use of the stack. A string's individual characters are saved in the stack.
After performing the pop operation, the string is inverted because the initial character of the string is kept at the bottom of the stack, and the last character of the string is kept at the top.

Parsing of syntax
Since many computer languages lack context, many compilers use the stack to parse the syntax.

Memory Control
The stack is frequently used to manage memory because it is a crucial operating system component.
This brings us to the conclusion of our discussion of stacks in data structures. If you’re looking for resources to master these data structure stack, Sign up for Learnbay’s data structure course right away and get IBM certificates for your next career!

Stacks in Data Structures_ Implementation

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This illustration enables you to carry out activities solely from one end, similar to adding and removing books from the top of a stack. This indicates that insertion and deletion are only possible in the stack data structure from the top of the stack. You can only reach the top of the stack at any given time.

I like - Comment - Share - Permalink - 12/12/2022 11:37 - Public

Ashish Kumar

Data structures and algorithms are the foundation of programming. Algorithms are used to solve problems by utilizing the data stored in data structures.

This blog will explain, using examples, why every programmer should learn data structures and algorithms. This article is for people who are just beginning to understand data structure and algorithms and are unsure how much of an impact it will have on their programming skills or career. It is also intended for those who wonder why well-known companies like Google, Facebook, and Amazon hire programmers who are skilled at algorithm optimization.

What are Algorithms?
Informally, a problem-solving algorithm is nothing more than a list of steps. In essence, they offer a solution.
For instance, the algorithm below could be used to solve the factorials problem:
Making Your Code Scalable Through the Use of Data Structures and Algorithms

Assume that Alice and Bob are attempting to find the sum of the first 1011 natural numbers to solve a simple problem. Alice put the programme into practice while Bob was still creating it, demonstrating how easy it is to criticize Donald Trump.

Alice and Bob are overjoyed with how quickly they were able to construct something of their own. Let's enter their workspace covertly and overhear their talk.
More on Scalability
Scalability is defined as "scale plus ability," which refers to an algorithm's or system's capacity to address problems of greater size.
Think about the challenge of organizing a classroom for 50 pupils. Booking a room, obtaining a blackboard, and a few chalks are one of the most basic solutions.

What happens, though, if the scale of the problem grows? What would happen if there were 200 students?

Your chosen code determines how many instructions you receive, and your computer's hardware and compiler determine how long it takes to run each instruction.

The answer is still correct, but additional resources are required. In this situation, a much larger space (perhaps a theater), a projection screen, and a digital pen will all be necessary.
What would happen if there were 1000 students?
When the problem gets bigger, the solution either doesn't work or requires a lot of resources. This means your solution was not scalable.
So what exactly is a scalable solution?
Our second solution was far more time-efficient and capable of handling problems of various sizes. The term "constant-time algorithms" refers to them.
Example 1: Age Group Problem
A slightly modified version of the binary search method can be used to handle problems like discovering persons who fit a specific age range (assuming that the data is sorted).
For a detailed explanation on this, refer to the popular data structure course and familiarize yourself with the DSA techniques.
Example 2: Rubik's Cube Problem
Consider that you are creating a programme to solve a Rubik's cube.
43,252,003,274,489,856,000 positions make up this adorable puzzle, and they are just positions! Consider all the possible routes one could take to end up in the incorrect place.
Example 3: DNA Problem
DNA is a genetic information-carrying molecule. Roman letters A, C, T, and G are used to symbolize the smaller units that make them up.
Consider a job in the bioinformatics industry. Your task is to determine whether a specific pattern appears in a DNA strand.
Your method is now 100 times faster because the pattern contains 100 characters. The KMP algorithm would be nearly 1000 times faster if your design included 1000 characters. In other words, if you previously needed 1 second to find the occurrence of a pattern, it will now just take 1 ms. Another way to state this is; you can match 1000 strands of data instead of just one.
Final Words
In general, software development requires daily technology learning. While utilizing these technologies in one of your projects, you get to learn most of them. Algorithms, on the other hand, do not work that way.

You won't be able to tell if the code you are producing right now can be optimized if you don't have a solid understanding of algorithms. You are expected to be aware of them beforehand and to use them when needed. With Learnbay’s DSA training you can master the essential data structures and algorithms concepts for your next tricky tech interview.

Why Study Algorithms and Data Structures

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Informally, a problem-solving algorithm is nothing more than a list of steps. In essence, they offer a solution. For instance, the algorithm below could be used to solve the factorials problem:

I like - Comment - Share - Permalink - 07/12/2022 11:20 - Public

Ashish Kumar

Methods For Solving Problems in Data Structures and Algorithms

An algorithm is a plan for resolving the coding problem. Every solution begins with a strategy. Therefore, to complete the task, programmers must learn to create an effective algorithm and translate this "algorithm" into the appropriate code.

However, there are several coding issues with data structures and algorithms, and most of the time, we are unfamiliar with these issues. As a result, programmers need to become self-assured problem solvers who are not deterred by a problem's complexity. Our long-term objective should be straightforward: to acquire the ability to quickly and correctly create code.

As we continue to practice, we will get more problem-solving expertise, and our work will become simpler.

For every coding problem, we should practice the following fundamental skills:

Creating a strategy for understanding the problem
Considering a sound fundamental solution
Creating an elaborate pseudocode solution
Examining a solution's effectiveness
Furthering the solution's optimization
Converting erroneous code from pseudocode

The crucial question at this point is — Is there a simple, step-by-step technique for approaching and resolving coding problems? If so, what are the necessary actions?
Let's consider and research!

Step 1: Understanding the problem

Every little thing counts when designing an adequate answer to a problem. The approach to the solution can occasionally differ if the question is slightly altered. Spending a little extra time on the problem will boost our confidence afterwards. The truth is that we never want to discover in the middle of a project that we misunderstood the issue.

Whether or not we have read through a question, we should still read it numerous times. So get a piece of paper, and while you work through the issue, get everything down. By looking at a few samples, we can better understand how many situations our algorithm can handle in any potential input-output patterns. We should also investigate the case of huge input, edge cases, and invalid input. A problem description may occasionally have one or more of the following deficiencies:

The problem description can be predicated on some unstated premises.
The problem description can be vague or lacking.
There are several contradictions in the problem description.

For detailed and technical information, refer to the data structure training, and become a DSA expert.

The abstract explanation of the problem description in our natural languages may be to blame for these deficiencies. Therefore, we must recognize this deficit and cooperate with the interviewer or problematic provider to clarify it. These are the questions that we should start by looking for answers to:

What are the input and output?
What kind of data is offered?
What is the input's dimension or scale?
How are files stored? Which data structure is it?
Are there any unique requirements or arrangements in the data?
What guidelines apply to using the data?

Step 2: Formulating correct basic solution

The best method would be to think of a correct solution that comes immediately into our thought. It does not matter, even if it is in an inefficient method, i.e. having a valid and inefficient answer is much better than an incorrect solution or significant delay in the solution. This might help us in many ways:
Help us to start off with good motivation or confidence.
Give the interviewer a great talking point to get the conversation going.
Here are some illustrations of patterns using brute force: Three nested loops, two nested loops, extra memory, sorting, double traversal of the binary tree, taking into account every sub-array or substring, exhaustive search, etc., are all examples of solutions.
Step 3: Designing efficient pseudocode solutions
The best problem-solving knowledge and techniques, along with various problem-solving techniques, should be used at this stage. One applicable reality is that it can be challenging to transition from a simple algorithm to the most effective algorithm in a single step. Every time, the preceding algorithm must be optimized, and we must end when there is no more room for improvement. Further optimization can be significantly aided by reviewing the problem description again and looking for new information. For instance:

We can consider applying a single loop, a two-pointers technique, the concept of binary search, etc., if the input array is sorted or has a similar sorted order.
If the input array is not sorted, we can use the divide-and-conquer strategy, hash tables, sorting with two pointers, etc.
If a subarray of size k requires some output, we can consider using a hash table, sliding window concept, etc.
The concept would be straightforward: we should learn how to apply effective problem-solving strategies to a variety of data types. The core idea is continuously thinking, analyzing, and looking for a better solution.
Step 4: Transforming pseudocode into a correct, clean, and optimized code

Finally, we need to write actual code in our preferred programming languages, such as C++, Java, Python, C#, JavaScript, etc., for each line of pseudocode. Remember to verify that the actual output matches the expected output while testing the existing code with sample test data. Before writing the code, discuss the sample data or test cases with the interviewer.

The code may need to be simplified and optimized through a few observational rounds. Once we have finished coding the code, we need to consider the following:
Does this code successfully handle all inputs, even the most unlikely ones?
Can we make the code even more efficient? Can you eliminate a loop, some variables, or some extra space?

Hope you got the answers to how to solve problems with data structures methods. If you’re looking for resources to learn DSA for your tech career, join the best data structure course and become certified by IBM.

Folder: 2022-12-05 17h03

Do You Need to Know Data Structures for Machine Learning

The distinction between iteration and recursion

Memoization in Python_ The Essence of Dynamic Programming

Introduction to Trees in Data Structure

Binary Heaps

See all…

I like - Comment - Share - Permalink - 05/12/2022 11:33 - Public

Ashish Kumar

Nodes in a tree can be represented using an array by numbering them starting at 0-(n-1) or 1-n (this indexing is used here).

Binary Heaps

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The (binary) heap data structure is an object in an array that can be thought of as a nearly finished binary tree. An element of the array is represented by each node in the tree. A.length, which (as normal) shows the number of elements in the array, and A.heap-size, which indicates how many elements from the heap are stored within array A, are the two properties of an object called an array A that represents a heap. Given the index I of a node, we can simply calculate the indices of its parent, left child, and right child because the tree's root is A[1].

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Ashish Kumar

Memoization in Python: The Core of Dynamic Programming

Overview of Dynamic programming
Richard Bellman created the technique known as "dynamic programming" in the 1950s. The primary goal of dynamic programming in data structure is to recursively divide a challenging problem into simpler subproblems.

Dynamic programming is a method of data structure which is used to solve several optimization issues in computer science and programming. Dynamic programming is a principle that applies to all programming languages, not just one in particular.

I like - Comment - Share - Permalink - 30/11/2022 12:47 - Public

Ashish Kumar

We occasionally run upon issues that are too complicated to be resolved immediately. Most of the time, we try to divide such issues into smaller parts. Then, we can work out a way to resolve these more straightforward issues before putting the whole thing together. Recursion is based entirely on this concept.

The distinction between iteration and recursion

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Ashish Kumar

Do Data Structures Matter for Machine Learning?
Machine learning is a subset of artificial intelligence, the capacity of computers to think, and it refers to algorithms that learn by training data rather than programming. By enabling computers to learn from data, machine learning aims to specifically increase corporate value through forecasts and solutions.
Overview of Machine Learning and Data Structures
Artificial intelligence is the idea that a task can be completed by a computer rather than a human, such as employing a virtual assistant or a self-driving vehicle. Artificial intelligence is a subset of machine learning.

I like - Comment - Share - Permalink - 28/11/2022 11:44 - Public

Ashish Kumar

I like - Comment - Share - Permalink - 28/11/2022 11:44 - Public

Ashish Kumar

Do You Need to Know Data Structures for Machine Learning

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Artificial intelligence is the idea that a task can be completed by a computer rather than a human, such as employing a virtual assistant or a self-driving vehicle. Artificial intelligence is a subset of machine learning.

I like - Comment - Share - Permalink - 28/11/2022 11:43 - Public

Ashish Kumar

Did you know that API Integrations are used in several ways by almost all companies that use contemporary technology to interface with databases or obtain data?

Before we learn about Payment gateway and API Integration, we should first understand how these are connected to DSA. Data integration in DSA is the process of combining data from various sources into a single dataset. Its main objective is to give users consistent access to and delivery of data across a wide range of topics and structure types. Application developers create, connect, and integrate apps faster and at scale thanks to an API's established communication protocol. This article will let you know what is Payment Gateway and API Integration in detail.

Introduction To Payment Gateway

A payment gateway is a service that authorizes credit card transactions between the customer and the merchant. The payment gateway integration is a system that allows online retailers to accept credit cards and other payments from customers. A payment gateway API is the most crucial part of a payment system which allows for communication between the merchant and the payment gateway. It also enables customers to complete their purchases without leaving the site. Payment gateways are a core part of any e-commerce website. They provide a secure, reliable, and scalable way to accept customer payments. For detailed information, check out Learnbay's DSA training, which offers comprehensive training for working professionals in the development domain.

Top 6 Payment Gateways to Know
Now you're aware of payment gateway integration, let's talk about the top 6 payment gateways.

Authorize.Net:

Authorize.Net is an electronic payment processing system for merchants, enabling them to receive credit card and electronic check payments securely online or by phone.

PayPal:

PayPal also offers a wide range of services, including person-to-person payments, digital goods, micropayments, money transfers, business payments for online auctions or other commercial transactions, sending money internationally, and withdrawing funds in 36 currencies worldwide through local banks.
PayPal's service, also called PayPal, connects to a user's bank account or credit card and enables users to send or receive money online in more than 100 currencies worldwide from almost any device (computer, tablet, mobile phone).

Braintree:
Braintree provides services for businesses of all sizes—from startups to Fortune 500 companies—to accept payments anywhere in the world. This company provides software services to both online merchants and mobile app developers who want to accept payments in their applications or on their websites from customers without having to build their systems from scratch.

Stripe API:
One of the best web APIs for developers. It has a simple, straightforward, and powerful API that supports many languages and platforms. Stripe is an internet-based service that allows businesses to accept payments over the Internet via credit cards or bank transfers.

Razorpay:
It offers a product suite to enable organizations to accept, process, and transfer payments. All payment methods, including credit cards, debit cards, net banking, UPI, and well-known wallets like PayUmoney, FreeCharge, Airtel Money, Ola Money, PayZapp, JioMoney, and Mobikwik, are accessible through it. Additionally, Razorpay has an excellent user interface, and signing up is a simple process. Even if you merely want to test Razorpay's user interface, you must supply a legitimate bank account number for it to function.

Pay U:
PayU (formerly PayU Money) is an Indian online payment company service. It is another rival to Razorpay. It acts as one of the greatest and simplest methods for making payments online. PayU was created to address the gaps left by more complicated service providers. They have a simple sign-up and rapid onboarding procedure, requiring little effort from the developer. The user interface seems good. Users can start accepting local payments through the service's single integrated solution in any location where it conducts business. There are 250 local payment methods, and a variety of currencies are available in PayU.

Now, we will talk about API Integration and its importance in the field of Data Structures and Algorithms.

What is an API Integration?
An API or Application Programming Interface is a set of protocols allowing two software applications to communicate. APIs are used for a wide range of purposes, including data access and data management.

API Integration is a process of integrating the payment gateway to the e-commerce website with the help of API. This can be done by using an API to retrieve data from the service or by using an API to send data to the service. An e-commerce site needs to integrate data structures and algorithms to make payments more secure and efficient. The API is a set of standards for building software applications that are composed of multiple parts. These standards define how software components should connect and communicate with one another.

The Importance of an API Integration:
- It provides a way to connect different software systems
- It allows developers to share code
- It helps in building applications faster

Examples of API Integration :

A good example of API integration is when you want to use your application as a map provider, so you need to integrate Google Maps into your project.
API integration is an essential part of your digital marketing strategy. It is a way to connect different systems and data sources, which then allows you to extract and use valuable data.

The API also allows for information about the order and customer to be passed back and forth between the two systems. There are many different APIs that can be used for making payments.

The first step in API integration is to identify the APIs that need to be integrated. These are usually identified by looking at the data structures and algorithms used in each API. The next step is to decide on an authentication scheme for the integration, depending on the type of data being shared between the two APIs and how sensitive it is.

An example of this would be connecting your CRM (customer relationship management) software with your eCommerce website so that you can track customer interactions with the website and send them targeted offers based on their browsing history on the site.

Conclusion:
Now you have a better understanding of the Top 6 Payment Gateways and the importance of API Integration. The DSA career path is best for you if you have a solid background in math and physics. To enroll in DSA course, candidates should be comfortable with any programming language, preferably Java or C++. When it comes to DSA classes, Learnbay has the finest course content. The professional instructors here will teach you the in-demand DSA skills you need to succeed in the MAANG Interviews.
If you want to learn more Please visit
https://www.learnbay.co/dsa-system-design

Payment Gateway and APIs

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Did you know that API Integrations are used in several ways by almost all companies that use contemporary technology to interface with databases or obtain data?

I like - Comment - Share - Permalink - 25/11/2022 09:44 - Public