Trending February 2024 # 5 Ai Applications To Optimize Healthcare Data Management # Suggested March 2024 # Top 3 Popular

You are reading the article 5 Ai Applications To Optimize Healthcare Data Management updated in February 2024 on the website We hope that the information we have shared is helpful to you. If you find the content interesting and meaningful, please share it with your friends and continue to follow and support us for the latest updates. Suggested March 2024 5 Ai Applications To Optimize Healthcare Data Management

Artificial intelligence (AI) has proven to have several benefits across different industries and businesses. One sector that has benefitted from the use of AI is the healthcare industry. This sector is always full of patient information, health records, and other important data crucial to patients and hospitals. 

Major problems facing healthcare data are cyberattacks, losing the information, and improper handling, leading to mixing up the records. These mistakes always have devastating effects on the healthcare sector as these medical procedures and other treatments are dependent on these data. In addition, there are other procedures outside the health industry that are dependent on these data. Therefore, properly managing healthcare data is fundamental in the healthcare industry.

The importance of these data has led to the adoption of AI in hospitals to help in the management. Here are some of the applications of AI in optimizing data management: 

Convenient Data Transmission

Health records are constantly subjected to several transfers among patients, hospitals, remote workers, and other legally entitled parties. When transferring this data, there needs to be a convenient and streamlined way to reach all the desired recipients in time. For example, you may opt to use faxing services, like


, and several others to send the faxes digitally without the need for printing and scanning. 

These modes of data transmission ensure that the records are sent faster and securely. This helps reduce cases of alterations or sending to wrong addresses. With AI, the sharing of information is simplified.

Data Security 

Several cyberattacks are lodged on these records during these transfers as criminals try to steal or change the records. These attacks are a major concern for the healthcare sector. 

Moreover, even when being stored, patient information is always vulnerable to attacks from hackers. Covering all these attack points manually could be next to impossible, considering the amount of data being held by the information system. 

Automation Of Data Flow

When patients enter a medical facility, their records are always taken by the hospital from time to time. Each process of their treatment is dependent on the information from the previous step to avoid any cases of errors. The number of patients in the hospital could be challenging to handle if the data flow is done manually. Moreover, handling data manually can lead to confusion.

In contrast, AI automates the data flow from one point to the other, streamlining the whole process. Once the information is entered at the first stage, it becomes accessible for authorized personnel in the hospitals. These records are always entered against a patient’s identity, which means very minimal cases of errors. It also becomes easy for return patients to continue their treatment as the complete information is already recorded in the system. 

Optimizing Data Storage

First, once a record is added, deleting or changing is difficult unless new paperwork is filed. Secondly, paper is limited in storage, and very little information can be stored on a piece of paper. Finally, once you lost these records, it would be difficult to retrieve them due to a lack of backups.

Fortunately, AI changes all these and optimize data storage in many ways. For example,

cloud storage

can help hospitals store large quantities of data in only one system. In addition, these cloud services have data backup where you can retrieve any lost information. It’s also possible to change any medical data without altering the other record elements when storing it in a system.

Data Analysis And Decision Making 

Another important use of AI when handling health data, especially in big data, is analyzing and interpreting the data. With AI, it’s possible to deduce important data points from health records, analyze them, and then present them to understand the chart. This can help in decision-making regarding medical procedures or genetic mapping for patients.


The healthcare sector is crucial due to the information stored in the systems and their value. Therefore, there’s the need to have an efficient data management system that can ensure information security and streamline any process that depends on these data. 

You're reading 5 Ai Applications To Optimize Healthcare Data Management

Integrate Mailchimp Using Ai With 1000+ Applications

Email marketing is one of the oldest ways to promote and market any brand. With time, it has evolved from sending a plain text to engaging graphics. It is not only about sending emails to thousands of people and waiting them to respond. It is about owning a quality time in customers’ inbox.

In this blog, let us discuss major MailChimp integrations and how they can benefit your business.

Major MailChimp Integrations You Need to Know

You might be curious to know- if MailChimp is efficient enough to manage email marketing campaigns alone, then why in the first place we need to integrate it with other software!

In this technology-driven era, if you integrate two business-related software, and set up a trigger with some expected action (with the help of third-party software), then several workflows can be automated.

Similarly, if you integrate MailChimp with various useful software like Google Sheets, Salesforce, Slack, etc., then you can save your time and cost spent in accomplishing routine tasks. Let us explore some of the major MailChimp integrations that can truly help you in automating marketing campaigns and other relatable tasks.

MailChimp Salesforce Integration

Salesforce is the cloud-based Customer Resource Management (CRM) software that helps in understanding customers and maintaining long-lasting relations with them. It helps in assuring that every customer interaction will contribute to the revenues of your business.

For instance, you are using MailChimp for your latest email campaign. Your marketing team is rigorously sending emails to a targeted audience and at the same time informing sales time about day-to-day status. Sometimes, it consumes lot of time to update status. For better coordination among the marketing and sales team and automating the data syncing between them, you can integrate Salesforce with MailChimp.

Connect MailChimp With Salesforce

MailChimp HubSpot Integration

Integrating MailChimp with HubSpot can help your business in automatically syncing data for better customer conversion rates, categorizing the targeted audience, and handling errors in databases. This integration will help in keeping track of every new user connected through email campaign and adding every new contact to an email list of a campaign that is added to HubSpot CRM.

Connect MailChimp With HubSpot

MailChimp Facebook Integration

Integrating MailChimp with Facebook can help you in driving the major traffic for the growth of your business. You can automatically inform your followers and potential customers on Facebook about the new email campaigns you are going to start with MailChimp.

For instance, you are going to launch a new product of your brand. You can announce the grand launch on your Facebook Page with the help of attractive videos, eye-catchy visuals, and live sessions. This could help you attract millions of new people and update the old ones. By integrating Facebook Page with MailChimp, you can automatically inform people about anything related to your brand with help of emails and ask them to follow you on Facebook.

Connect MailChimp With Facebook

MailChimp Gmail Integration

Gmail is the most popular free web-based email service offered by Google. It has a crisp, clean, and clear interface that allows every individual to easily send and receive emails and further categorize them into different categories.

Integrating MailChimp with Gmail can help your business give a personalized touch to all the people mentioned in your mailing list. After setting up the integration, you can automate sending emails to the personal accounts of your subscribers for giving them regular updates. Further, it would automatically update your mailing once you label emails in Gmail.

Connect MailChimp with Gmail

MailChimp PayPal Integration

Paypal is one of the leading online payment gateways that allow individuals and businesses to send and receive money worldwide. You can easily pay for your cab services, online shopping, and much more in more than 100 currencies with PayPal.

Integrating MailChimp with PayPal can help your business automate the process of manually entering the details of every customer in the mailing list used by MailChimp. Every time new users get connected with your website and complete the payment process; the relevant details get automatically added to the mailing list for further communication.

Connect MailChimp with PayPal

Summing Up

In this technology-driven era, you cannot ignore the importance of workflow automation in your business. You need to automate various workflows for increasing the efficiency and productivity of your business.

You can easily automate routine workflows, just by integrating two relevant software. For integrating and automation, we recommend you the leading software- Appy Pie Connect. You can easily connect over hundreds of software with Appy Pie Connect. You need to code a single line for workflow automation.

Try Appy Pie Connect Now!

What Is Big Data? Introduction, Uses, And Applications.

This article was published as a part of the Data Science Blogathon.

What is Big Data?

Big data is exactly what the name suggests, a “big” amount of data. Big Data means a data set that is large in terms of volume and is more complex. Because of the large volume and higher complexity of Big Data, traditional data processing software cannot handle it. Big Data simply means datasets containing a large amount of diverse data, both structured as well as unstructured.

Big Data allows companies to address issues they are facing in their business, and solve these problems effectively using Big Data Analytics. Companies try to identify patterns and draw insights from this sea of data so that it can be acted upon to solve the problem(s) at hand.

Although companies have been collecting a huge amount of data for decades, the concept of Big Data only gained popularity in the early-mid 2000s. Corporations realized the amount of data that was being collected on a daily basis, and the importance of using this data effectively.

5Vs of Big Data

Volume refers to the amount of data that is being collected. The data could be structured or unstructured.

Velocity refers to the rate at which data is coming in.

Variety refers to the different kinds of data (data types, formats, etc.) that is coming in for analysis. Over the last few years, 2 additional Vs of data have also emerged – value and veracity.

Value refers to the usefulness of the collected data.

Veracity refers to the quality of data that is coming in from different sources.

How Does Big Data Work?

Big Data helps corporations in making better and faster decisions, because they have more information available to solve problems, and have more data to test their hypothesis on.

Customer Experience Machine Learning

Machine Learning is another field that has benefited greatly from the increasing popularity of Big Data. More data means we have larger datasets to train our ML models, and a more trained model (generally) results in a better performance. Also, with the help of Machine Learning, we are now able to automate tasks that were earlier being done manually, all thanks to Big Data.

Demand Forecasting

Demand forecasting has become more accurate with more and more data being collected about customer purchases. This helps companies build forecasting models, that help them forecast future demand, and scale production accordingly. It helps companies, especially those in manufacturing businesses, to reduce the cost of storing unsold inventory in warehouses.

Big data also has extensive use in applications such as product development and fraud detection.

How to Store and Process Big Data?

The volume and velocity of Big Data can be huge, which makes it almost impossible to store it in traditional data warehouses. Although some and sensitive information can be stored on company premises, for most of the data, companies have to opt for cloud storage or Hadoop.

Cloud storage allows businesses to store their data on the internet with the help of a cloud service provider (like Amazon Web Services, Microsoft Azure, or Google Cloud Platform) who takes the responsibility of managing and storing the data. The data can be accessed easily and quickly with an API.

Hadoop also does the same thing, by giving you the ability to store and process large amounts of data at once. Hadoop is an open-source software framework and is free. It allows users to process large datasets across clusters of computers.

Apache Hadoop is an open-source big data tool designed to store and process large amounts of data across multiple servers. Hadoop comprises a distributed file system (HDFS) and a MapReduce processing engine.

Apache Spark is a fast and general-purpose cluster computing system that supports in-memory processing to speed up iterative algorithms. Spark can be used for batch processing, real-time stream processing, machine learning, graph processing, and SQL queries.

Apache Cassandra is a distributed NoSQL database management system designed to handle large amounts of data across commodity servers with high availability and fault tolerance.

Apache Flink is an open-source streaming data processing framework that supports batch processing, real-time stream processing, and event-driven applications. Flink provides low-latency, high-throughput data processing with fault tolerance and scalability.

Apache Kafka is a distributed streaming platform that enables the publishing and subscribing to streams of records in real-time. Kafka is used for building real-time data pipelines and streaming applications.

Splunk is a software platform used for searching, monitoring, and analyzing machine-generated big data in real-time. Splunk collects and indexes data from various sources and provides insights into operational and business intelligence.

Talend is an open-source data integration platform that enables organizations to extract, transform, and load (ETL) data from various sources into target systems. Talend supports big data technologies such as Hadoop, Spark, Hive, Pig, and HBase.

Tableau is a data visualization and business intelligence tool that allows users to analyze and share data using interactive dashboards, reports, and charts. Tableau supports big data platforms and databases such as Hadoop, Amazon Redshift, and Google BigQuery.

Apache NiFi is a data flow management tool used for automating the movement of data between systems. NiFi supports big data technologies such as Hadoop, Spark, and Kafka and provides real-time data processing and analytics.

QlikView is a business intelligence and data visualization tool that enables users to analyze and share data using interactive dashboards, reports, and charts. QlikView supports big data platforms such as Hadoop, and provides real-time data processing and analytics.

Big Data Best Practices

To effectively manage and utilize big data, organizations should follow some best practices:

Define clear business objectives: Organizations should define clear business objectives while collecting and analyzing big data. This can help avoid wasting time and resources on irrelevant data.

Collect and store relevant data only: It is important to collect and store only the relevant data that is required for analysis. This can help reduce data storage costs and improve data processing efficiency.

Ensure data quality: It is critical to ensure data quality by removing errors, inconsistencies, and duplicates from the data before storage and processing.

Use appropriate tools and technologies: Organizations must use appropriate tools and technologies for collecting, storing, processing, and analyzing big data. This includes specialized software, hardware, and cloud-based technologies.

Establish data security and privacy policies: Big data often contains sensitive information, and therefore organizations must establish rigorous data security and privacy policies to protect this data from unauthorized access or misuse.

Leverage machine learning and artificial intelligence: Machine learning and artificial intelligence can be used to identify patterns and predict future trends in big data. Organizations must leverage these technologies to gain actionable insights from their data.

Focus on data visualization: Data visualization can simplify complex data into intuitive visual formats such as graphs or charts, making it easier for decision-makers to understand and act upon the insights derived from big data.


1. Data Growth

Managing datasets having terabytes of information can be a big challenge for companies. As datasets grow in size, storing them not only becomes a challenge but also becomes an expensive affair for companies.

To overcome this, companies are now starting to pay attention to data compression and de-duplication. Data compression reduces the number of bits that the data needs, resulting in a reduction in space being consumed. Data de-duplication is the process of making sure duplicate and unwanted data does not reside in our database.

2. Data Security

Data security is often prioritized quite low in the Big Data workflow, which can backfire at times. With such a large amount of data being collected, security challenges are bound to come up sooner or later.

Mining of sensitive information, fake data generation, and lack of cryptographic protection (encryption) are some of the challenges businesses face when trying to adopt Big Data techniques.

Companies need to understand the importance of data security, and need to prioritize it. To help them, there are professional Big Data consultants nowadays, that help businesses move from traditional data storage and analysis methods to Big Data.

3. Data Integration

Data is coming in from a lot of different sources (social media applications, emails, customer verification documents, survey forms, etc.). It often becomes a very big operational challenge for companies to combine and reconcile all of this data.

There are several Big Data solution vendors that offer ETL (Extract, Transform, Load) and data integration solutions to companies that are trying to overcome data integration problems. There are also several APIs that have already been built to tackle issues related to data integration.

Advantages of Big Data

Improved decision-making: Big data can provide insights and patterns that help organizations make more informed decisions.

Increased efficiency: Big data analytics can help organizations identify inefficiencies in their operations and improve processes to reduce costs.

Better customer targeting: By analyzing customer data, businesses can develop targeted marketing campaigns that are relevant to individual customers, resulting in better customer engagement and loyalty.

New revenue streams: Big data can uncover new business opportunities, enabling organizations to create new products and services that meet market demand.

Privacy concerns: Collecting and storing large amounts of data can raise privacy concerns, particularly if the data includes sensitive personal information.

Risk of data breaches: Big data increases the risk of data breaches, leading to loss of confidential data and negative publicity for the organization.

Technical challenges: Managing and processing large volumes of data requires specialized technologies and skilled personnel, which can be expensive and time-consuming.

Difficulty in integrating data sources: Integrating data from multiple sources can be challenging, particularly if the data is unstructured or stored in different formats.

Complexity of analysis: Analyzing large datasets can be complex and time-consuming, requiring specialized skills and expertise.

Implementation Across Industries 

Here are top 10 industries that use big data in their favor – 

IndustryUse of Big dataHealthcareAnalyze patient data to improve healthcare outcomes, identify trends and patterns, and develop personalized treatmentRetailTrack and analyze customer data to personalize marketing campaigns, improve inventory management and enhance CXFinanceDetect fraud, assess risks and make informed investment decisionsManufacturingOptimize supply chain processes, reduce costs and improve product quality through predictive maintenanceTransportationOptimize routes, improve fleet management and enhance safety by predicting accidents before they happenEnergyMonitor and analyze energy usage patterns, optimize production, and reduce waste through predictive analyticsTelecommunicationsManage network traffic, improve service quality, and reduce downtime through predictive maintenance and outage predictionGovernment and publicAddress issues such as preventing crime, improving traffic management, and predicting natural disastersAdvertising and marketingUnderstand consumer behavior, target specific audiences and measure the effectiveness of campaignsEducationPersonalize learning experiences, monitor student progress and improve teaching methods through adaptive learning

The Future of Big Data

The volume of data being produced every day is continuously increasing, with increasing digitization. More and more businesses are starting to shift from traditional data storage and analysis methods to cloud solutions. Companies are starting to realize the importance of data. All of these imply one thing, the future of Big Data looks promising! It will change the way businesses operate, and decisions are made.


In this article, we discussed what we mean by Big Data, structured and unstructured data, some real-world applications of Big Data, and how we can store and process Big Data using cloud platforms and Hadoop. If you are interested in learning more about big data uses, sign-up for our Blackbelt Plus program. Get your personalized career roadmap, master all the skills you lack with the help of a mentor and solve complex projects with expert guidance. Enroll Today!

Frequently Asked Questions

Q1. What is big data in simple words?

A. Big data refers to the large volume of structured and unstructured data that is generated by individuals, organizations, and machines.

Q2. What is big data in example?

A. An example of big data would be analyzing the vast amounts of data collected from social media platforms like Facebook or Twitter to identify customer sentiment towards a particular product or service.

Q3. What are the 3 types of big data?

A. The three types of big data are structured data, unstructured data, and semi-structured data.

Q4. What is big data used for?

A. Big data is used for a variety of purposes such as improving business operations, understanding customer behavior, predicting future trends, and developing new products or services, among others.

The media shown in this article are not owned by Analytics Vidhya and is used at the Author’s discretion. 


Big Data In Healthcare: Where Is It Heading?

Big data is making huge strides in the healthcare sector and is transforming medical treatment

Big data continues to revolutionize the way we analyze, manage, and use data across industries. It’s no surprise that one of the most notable sectors where data is making big changes in healthcare.

In fact, the onset of a global pandemic has accelerated innovation and adoption of digital technology, particularly in big data and big data analytics. This enabled healthcare providers to reduce treatment costs, avoid preventable diseases, predict outbreaks of epidemics, and improve the overall life quality. On the flip side, the same events have also exposed many weaknesses of the healthcare sector. Here we outline the impact of big data and data analytics in healthcare as well as give a few examples of key applications of big data in the healthcare sector.

Big Data in Healthcare: Promise and Potential

A report from IDC shows that big data is expected to grow faster in healthcare than in other industries like financial services, manufacturing, or media. It’s estimated that the healthcare data will see a compound annual growth rate of 36% by 2025.

The international big data market in the healthcare sector is estimated to reach $34.27B through 2023 at a CAGR of 22.07%. Globally, it’s estimated that the big data analytics sector will reach more than $68.03B by 2024, driven massively by ongoing North American investments in practice management technologies, health records, and workforce management solutions. Recent findings from McKinsey & Co hint that big data in healthcare can save us between $300B to $450B each year.

4 Key Applications of Big Data Analytics in Healthcare

Information obtained from big data analytics provides healthcare experts with valuable insights that were not possible before. A great amount of data is applied at every step of the healthcare cycle: from medical investigation to patient experience and outcome.

1. Big Data in Diagnostic Predictions

Thanks to data analytics and big data, it’s possible to diagnose the disease quickly and accurately. Normally, medical providers need to examine patients, discuss their ailments, and compare their symptoms to diseases they already know. But, because there’s always more than can meet the eye, big data ensures a smarter way to diagnose complex cases. For example, physicians can simply collect patient data and feed it into a system that will suggest possible diagnoses. These algorithms then propose high-value tests and minimize the excess of unnecessary tests.

2. Big Data in Personal Injury Claims

Usually, when a personal injury lawsuit is filed, the injured person attaches documents, including a medical report, a police report, and medical expenses. But to sue someone and win the case, legal professionals have to appoint an expert to evaluate all the records and ensure they’re valid, process the claim, and pay it out. However, this process isn’t just unnecessarily long but also very tedious since it’s reliant on human labour.

Predictive analytics reduces the amount of time needed to process the information, making it more time-efficient and saving on salaries. AI-powered systems use the generated data to predict the outcome of personal injury cases that are ordinary and simple to handle.

This process involves feeding AI systems with data on past cases that are similar in order to analyze and identify patterns in how the past personal injury claims were solved.

3. Big Data Improves Patient Engagement

Increasingly more consumers– and hence, potential patients – are interested in wearables that record every step they take, sleeping quality, their rates, etc., on a daily basis. All this critical data can be coupled with other trackable data to uncover potential health risks lurking. Tachycardia and chronic insomnia can signal the risk of heart diseases, for instance.

Today, a number of patients are directly involved in monitoring their own health, and incentives from health insurers can encourage them to lead a healthier lifestyle (such as giving money back to people using wearables).

The application of IoT devices and smart wearables, which healthcare providers now recommend, is among key healthcare technology trends. These technologies automatically collect health metrics and offer valuable indications, removing the need for patients to travel to the nearest medical facility or for patients to collect it themselves. It’s clear that the latest tech helped generate tons of valuable data that can help doctors better diagnose and treat common and complex health problems.

4. Big Data in Telemedicine

We can’t talk about telemedicine without mentioning big data and its role. With the application of high-speed real-time data, medical providers can perform operations while physically being miles away from the patient. While this might sound strange, it’s as real and possible as it could be. Big data has made possible not only robot-assisted surgeries but also accurate diagnosis, virtual nursing assistance, and remote patient monitoring.

Big data and telemedicine have made it possible for patients and doctors to :

Avoid waiting for lines

Reduce unnecessary consultations and paperwork

For patients to be consulted and monitored anywhere and anytime

Prevent hospitalization

Improve the quality of service and reduce costs

5 Top Vulnerability Management Trends In 2023

Vulnerability management seeks to lower risk by identifying and dealing with any possible lines of incursion into a network by cybercriminals.

The field of vulnerability management includes automated scans, configuration management, regular penetration testing, patching, keeping track of various metrics, and reporting. The category has been evolving rapidly within cybersecurity, and here are some of the top trends in the vulnerability management market:

Vulnerability management is all about identifying, prioritizing, and remediating vulnerabilities in software.

As such, it encompasses far more than the running of vulnerability scans repeatedly to look for known weaknesses lurking within the infrastructure. Traditionally, vulnerability management also includes patch management and IT asset management. It addresses misconfiguration or code issues that could allow an attacker to exploit an environment as well as flaws or holes in device firmware, operating systems, and applications running on a wide range of devices.

“These vulnerabilities can be found in various parts of a system, from low-level device firmware to the operating system all the way through to software applications running on the device,” said Jeremy Linden, senior director of product management, Asimily.

See more: A holistic approach to vulnerability management solidifies cyber defenses

Some analysts and vendors stick strictly to the NIST definition when they’re talking about vulnerability management. Others include security information and event management (SIEM) with vulnerability management as part of larger suites. And a few combine it with threat intelligence, which prioritizes actions and helps IT to know what to do and in what order.

Gartner recently originated the new term attack surface management (ASM). The analyst defines ASM as the “combination of people, processes, technologies, and services deployed to continuously discover, inventory, and manage an organization’s assets.”

ASM tools are said to go beyond vulnerability management. The aim is to improve asset visibility, understand potential attack paths, provide audit compliance reporting, and offer actionable intelligence and metrics.

The as-a-service trend has invaded so many areas of IT, so it’s no wonder that vulnerability management as a service has emerged.

“With more than 20K vulnerabilities found and published in a single year, vulnerability management has become an enormous task,” said Michael Tremante, product manager, Cloudflare.

“This is made worse for large enterprises who also have the challenge of not necessarily knowing the full set of software components being used internally by the organization, potentially putting the company at risk. A big trend is adoption of managed services/SaaS environments, as they are externally managed, and offloading of vulnerability management to third parties.”

Thus, a growing set of products are hitting the market that help companies tackle vulnerability management via managed services of one kind or another.

See more: Vulnerability Management as a Service (VMaaS): Ultimate Guide

Containers and Kubernetes have become largely synonymous with modern DevOps methodologies, continuous delivery, deployment automation, and managing cloud-native applications and services.

However, the need to secure containerized applications at every layer of the underlying infrastructure — from bare-metal hardware to the network to the control plane of the orchestration platform itself — and at every stage of the development life cycle — from coding and testing to deployment and operations — means that container security must cover the whole spectrum of cybersecurity and then some, said KuppingerCole.

See more: Securing Container and Kubernetes Ecosystems

Due to the way the threat landscape is evolving, the way vulnerability management platforms are shifting, and the fast pace of innovation as evidenced by containerization, digitalization, and the cloud, a new approach is needed, according to Ashley Leonard, CEO, Syxsense.

“Businesses possess incredibly powerful processors inside storage equipment, servers, and desktops, which are underutilized in many cases” Leonard said.

For example, Syxsense has been incorporating more features into its vulnerability management tools. This includes more orchestration and automation capabilities, stronger endpoint capabilities, and mobile device management. These augment existing patch management, vulnerability scanning, remediation, and IT management capabilities.

See more: 12 Top Vulnerability Management Tools

Dynamic Character To Web Applications

Introduction to AngularJS Application

Web development, programming languages, Software testing & others

Angular JS used HTML language to extend its syntax and helps in creating applications more efficiently. Angular JS is used to make it dynamic as HTML is mainly used as a static language. Angular JS follows the concept of MVC (Model View Controller). The main idea behind MVC is to make a differentiation between data, logic, and view layer. The view receives data from the model, which is used to display to the user. When the user interacts with the application by performing actions then the controller has changed the data in the model and after that view displays the information after it tells the model about the changes. In Angular JS, data is stored in properties of an object, controllers are JS classes and the view is DOM (Document Object Model).

Concepts of AngularJS Application

The concepts of AngularJS Application with their examples are as follows:

1. Directives to extend HTML attributes 2. Scope

It is used for the communication between controller and view. It binds the view to the view model and functions defined in the controller Angular JS supports nested or hierarchical scopes. It is the data source for AngularJS and it can add or remove properties when required. All the data manipulation and assignment of data happens through scope objects when doing CRUD operations.

3. Controllers

These are used to define the scope for the views and scope can be thought of as variables and functions that view may use some binding.

First Name: Last Name: Full Name: {{firstName + ” ” + lastName}} var app = angular.module(‘myApp’, []); app.controller(‘myCtrl’, function ($scope) { $scope.firstName = ”James”; $scope.lastName = ”Anderson”; });

4. Data Binding

Example: When the user types into the text box the changed value shows in upper and lower case in the label that is two-way data binding.

5. Services 6. Routing

t helps in dividing the app into multiple views and bind multiple views to controllers. It divides SPA into multiple views to logically divide the app and make it more manageable.

default route. App.config(['$routeProvider', function($routeProvider) { $routeProvider. when('/List', { templateUrl: 'Views/list.html', controller: 'ListController' }). when('/Add', { templateUrl: 'Views/add.html', controller: 'AddController' ). otherwise({ redirectTo: '/List' }); }]) 7. Filters

These are used to extend the behavior of binding expression and directive. It allows formatting the data and formatting values or applying certain conditions. Filters are invoked in HTML with a pipe inside expressions.

var app = angular.module(‘myApp’, []); app.controller(“namesCtrl”, function ($scope) { $scope.friends = [ { name: ”Karl”, age: 27, city: ”Bangalore” }, { name: ”Lewis”, age: 55, city: ”Newyork” }, ]; });

8. Expressions 9. Modules

The module is the container of an application and application controllers belong to a module. It is a collection of functions and divides applications into small and reusable functional components. The module can be identified by a unique name and can be dependent on other modules.

{{ firstName + ” ” + lastName }}

10. Testing

To test angular JS code, test frameworks are widely used like Jasmine and karma. These testing frameworks mainly support mocking and are highly configurable using JSON files with help of various plugins.


Angular JS provides the framework to develop the web application in very less time and efficiently. Angular JS is always available for unit testing. It is mainly used for SPA, which makes the development faster. It is easy to understand and simple to learn for JavaScript developers. Angular JS is still useful for people who are beginners as they can grasp it easily.

Angular is getting pace for front-end development as it makes the development faster. Large applications can be easily handled in angular. It can execute better with components. Angular is having really strong areas and significant features to use. Angular has released its higher versions also with new features and better performance.

Recommended Articles

We hope that this EDUCBA information on “AngularJS Application” was beneficial to you. You can view EDUCBA’s recommended articles for more information.

Update the detailed information about 5 Ai Applications To Optimize Healthcare Data Management on the website. We hope the article's content will meet your needs, and we will regularly update the information to provide you with the fastest and most accurate information. Have a great day!