Data Science Training by Quality Thought Institute – Learn from Experts

Data Science Course

The demand for skilled data professionals continues to grow as businesses rely more on analytics and artificial intelligence for decision-making. Choosing the right data science course is the first step toward building a future-ready career in this fast-growing domain. Quality Thought Training Institute offers a comprehensive and industry-aligned data science training program designed to meet real-world job requirements.

Our expert-led data science online training helps learners gain practical skills, hands-on experience, and career confidence, regardless of their location.

Why Choose Quality Thought for Data Science Training?

Quality Thought Training Institute is widely recognized for delivering practical and job-oriented data science training. The program focuses on real-world applications rather than theory alone, ensuring learners are well-prepared for industry challenges.

Key advantages of our data science course include:

Live online classes conducted by experienced data professionals

Hands-on projects using real business datasets

Strong foundation in Python, statistics, and machine learning

Exposure to industry-standard tools and frameworks

Career guidance and interview preparation support

Flexible schedules for students and working professionals

Our data science online training is structured to help learners apply concepts confidently in real-time projects.

Data Science Online Training Across Major Indian Cities

Quality Thought offers data science online training to learners across India’s leading metro cities, ensuring equal access to quality education.

Data Science Course in Delhi

Our data science course in Delhi helps learners build core analytics and machine learning skills to meet the needs of enterprises and startups.

Data Science Course in Hyderabad

Hyderabad is a major analytics hub. Our data science training in Hyderabad focuses on practical learning aligned with MNC and product-based company requirements.

Data Science Course in Mumbai

Mumbai’s finance-driven ecosystem demands skilled analysts. Our data science online training equips learners with predictive analytics and data visualization expertise.

Data Science Course in Bengaluru

Bengaluru offers vast opportunities in AI and analytics. Our data science course prepares learners for high-demand roles in technology-driven organizations.

Data Science Course in Chennai

Chennai’s IT and enterprise sectors increasingly rely on data insights. Our data science training builds strong technical and analytical foundations.

Data Science Course in Kerala

With growing remote opportunities, our data science online training in Kerala helps learners gain global-ready analytics skills.

Global Demand for Data Science Professionals

A well-structured data science course opens doors to international career opportunities. Quality Thought’s data science training is designed to meet global standards, making learners job-ready for worldwide roles.

Countries with strong demand for data science professionals include:

United States

Canada

United Kingdom

Germany

Australia

Singapore

United Arab Emirates

Netherlands

What You Will Learn in This Data Science Course

Our data science course curriculum is designed to take learners from fundamentals to advanced concepts.

You will gain expertise in:

Python programming for data science

Data analysis and data visualization

Statistics and probability for analytics

Machine learning algorithms and model building

SQL and database concepts

Real-time projects and practical case studies

This hands-on data science training helps learners build a strong project portfolio and industry-ready skills.

Career Opportunities After Data Science Training

Completing data science training from Quality Thought enables learners to apply for roles such as:

Data Scientist

Data Analyst

Machine Learning Engineer

Business Analyst

AI Engineer

With businesses increasingly adopting data-driven strategies, professionals with data science online training enjoy strong career growth and global opportunities.

Enroll Now – Start Your Data Science Career Today 🚀

If you are ready to upgrade your skills or switch to a high-growth career, Quality Thought Training Institute offers the right data science course to help you succeed.

👉 Join our data science online training program

👉 Learn from experienced industry mentors

👉 Work on real-world projects and case studies

👉 Get career guidance and placement support

Take the next step toward a successful career in analytics and AI.

Enroll today in Quality Thought’s Data Science Training and become job-ready with confidence.

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 The Impact of AI on Medical Coding Jobs

Introduction

Medical coding is a critical function in healthcare, translating clinical documentation into standardized codes for billing, insurance claims, and data analysis. With the rise of Artificial Intelligence (AI) and Natural Language Processing (NLP), the medical coding profession is undergoing significant change. AI is reshaping how coding work is performed, but it is not eliminating the need for human coders.

How AI Is Used in Medical Coding

AI-powered systems can:

Analyze clinical notes using NLP

Automatically suggest ICD-10, CPT, and HCPCS codes

Identify documentation gaps

Detect coding errors and inconsistencies

Speed up claim processing

These systems are often referred to as Computer-Assisted Coding (CAC) tools.

Positive Impacts of AI on Medical Coding Jobs

1. Increased Productivity

AI reduces manual data entry and repetitive tasks, allowing coders to process more records in less time.

2. Improved Accuracy

AI helps:

Reduce human errors

Flag potential compliance issues

Improve coding consistency

This leads to fewer claim denials and faster reimbursements.

3. Shift to Higher-Value Work

Human coders increasingly focus on:

Complex and ambiguous cases

Auditing and quality assurance

Clinical documentation improvement (CDI)

Compliance and regulatory review

4. Better Work-Life Balance

Automation can reduce workload pressure and burnout by handling routine coding tasks.

Challenges and Concerns

1. Job Displacement Fears

Entry-level or routine coding roles are more likely to be automated, raising concerns about job security.

2. Dependence on Data Quality

AI systems rely on accurate clinical documentation. Poor or incomplete notes can lead to incorrect coding suggestions.

3. Training and Skill Gaps

Coders must adapt by learning:

How to work with AI tools

Data validation and auditing skills

Healthcare analytics basics

Why Human Coders Are Still Essential

AI cannot fully replace human expertise because:

Medical language is complex and context-dependent

Clinical judgment is often required

Regulations and guidelines frequently change

Ethical and compliance oversight needs human review

Human coders play a crucial role in validating AI outputs and handling exceptions.

Future of Medical Coding Careers

Expected Trends

Hybrid human–AI workflows

Increased demand for certified, experienced coders

Growth in auditing, compliance, and CDI roles

Need for continuous upskilling

Skills That Will Be in Demand

Advanced coding knowledge

AI tool proficiency

Data quality and compliance expertise

Communication with clinicians

How Medical Coders Can Prepare

Stay current with coding certifications

Learn to use AI-assisted coding tools

Develop auditing and analytical skills

Embrace continuous education

Conclusion

AI is transforming medical coding by improving efficiency and accuracy, not by eliminating the profession. While some routine tasks are being automated, skilled medical coders remain essential for quality control, compliance, and complex decision-making. Those who adapt and upskill will find strong opportunities in the evolving healthcare landscape.

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🌍 Industry Trends & News in Medical Coding

How to Organize Your Workspace as a Remote Coder

How to Use Code Books Like a Pro

The Future of Automation in Coding

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 Introduction to Dockerized Selenium Grid

What Is Selenium Grid?

Selenium Grid is a tool that allows you to run automated browser tests in parallel across multiple machines, browsers, and operating systems. It is widely used in test automation to reduce execution time and improve test coverage.

What Does “Dockerized” Mean?

Dockerization means running applications inside lightweight, portable containers. A Dockerized Selenium Grid runs Selenium Grid components (Hub and Nodes) inside Docker containers instead of installing them manually on physical or virtual machines.

Why Use a Dockerized Selenium Grid?

Key Benefits

Easy setup – No complex manual installation

Scalability – Spin up multiple browser nodes quickly

Consistency – Same environment across all machines

Parallel execution – Faster test execution

Isolation – Each browser runs in its own container

Core Components of Selenium Grid

1. Hub

Central controller

Receives test requests

Distributes tests to available nodes

2. Nodes

Run the actual browser instances

Can be Chrome, Firefox, Edge, etc.

Multiple nodes can run simultaneously

How Dockerized Selenium Grid Works

Docker starts the Selenium Hub container

Docker starts browser Node containers

Nodes register themselves with the Hub

Test scripts send requests to the Hub

The Hub routes tests to available Nodes

Test results are returned to the test framework

Common Docker Images

Official Selenium Docker images include:

selenium/hub

selenium/node-chrome

selenium/node-firefox

selenium/node-edge

These images are maintained by the Selenium project.

Typical Architecture

Test Scripts

     |

     v

Selenium Hub (Docker)

     |

     v

Browser Nodes (Docker Containers)

Using Docker Compose (Overview)

Docker Compose simplifies running multiple containers together:

Defines Hub and Nodes in a single file

Allows easy scaling of browser nodes

Ideal for local testing and CI/CD pipelines

Use Cases

Cross-browser testing

Regression test automation

CI/CD pipeline integration

Distributed test execution

Advantages Over Traditional Selenium Grid

Traditional Grid Dockerized Grid

Manual setup Automated setup

Hard to scale Easily scalable

Environment drift Consistent environments

Slower provisioning Fast container startup

Challenges and Considerations

Requires basic Docker knowledge

Resource usage on local machines

Network configuration in CI environments

Best Practices

Use Docker Compose for simplicity

Limit browser container resources

Clean up containers after test runs

Monitor container performance

Conclusion

A Dockerized Selenium Grid provides a modern, scalable, and efficient way to run automated browser tests. By combining Selenium Grid with Docker, teams can achieve faster execution, better reliability, and easier integration into CI/CD pipelines.

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Setting Up Selenium Grid for Distributed Testing

Parallel Test Execution with Selenium Grid

Running Selenium Tests in Headless Mode

🔁 Test Execution & Management in Selenium JAVA

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 Setting Up Your Java Development Environment (IDE)

Introduction

A proper Java development environment allows you to write, compile, debug, and run Java applications efficiently. This guide covers installing Java, choosing an IDE, and configuring essential tools for development.

Step 1: Install the Java Development Kit (JDK)

Why You Need the JDK

The JDK (Java Development Kit) includes:

Java compiler (javac)

Java Runtime Environment (JRE)

Core libraries and development tools

Recommended Versions

Java 17 or 21 (LTS – Long Term Support)

Installation

Download the JDK from:

Oracle JDK

OpenJDK (Adoptium Temurin recommended)

Install the JDK following platform instructions.

Verify installation:

java -version

javac -version

Step 2: Set Environment Variables

On Windows

Set JAVA_HOME to the JDK installation path

Add %JAVA_HOME%\bin to the PATH variable

On macOS / Linux

Add to .bashrc, .zshrc, or equivalent:

export JAVA_HOME=/path/to/jdk

export PATH=$JAVA_HOME/bin:$PATH

Step 3: Choose a Java IDE

Popular Java IDEs

1. IntelliJ IDEA

Best-in-class features

Excellent code completion and refactoring

Community (free) and Ultimate (paid) versions

2. Eclipse

Free and open-source

Highly extensible with plugins

Widely used in enterprise environments

3. Visual Studio Code

Lightweight and fast

Requires Java extensions

Good for polyglot development

Step 4: Install and Configure the IDE

IntelliJ IDEA Setup

Download from the official website

Install and launch

Set the JDK path

Create a new Java project

Eclipse Setup

Download Eclipse IDE for Java Developers

Install and launch

Configure the JDK under Preferences → Java → Installed JREs

VS Code Setup

Install VS Code

Install Java Extension Pack

Set JAVA_HOME

Open or create a Java project

Step 5: Build Tools (Optional but Recommended)

Maven

Dependency management

Project build lifecycle

Gradle

Faster builds

Flexible configuration

Both tools integrate seamlessly with modern IDEs.

Step 6: Version Control

Install Git for source code management:

Track changes

Collaborate with teams

Integrate with GitHub or GitLab

Step 7: Test Your Setup

Create a simple Java program:

public class Main {

    public static void main(String[] args) {

        System.out.println("Java environment is ready!");

    }

}

Run it from the IDE to confirm everything works correctly.

Best Practices

Use an LTS Java version

Keep IDE and plugins updated

Follow coding standards

Use built-in debugging tools

Conclusion

Setting up your Java development environment correctly is the foundation for productive and efficient Java programming. With the right JDK, IDE, and tools, you are ready to build robust Java applications.

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Using Lombok to Reduce Boilerplate Code in Java

How to Use Maven in a Java Project

Maven vs Gradle – Build Tools Compared

Git Commands Every Developer Should Know

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Policy as Code with Open Policy Agent

 Policy as Code with Open Policy Agent (OPA)

Introduction

Policy as Code is the practice of defining and managing policies using machine-readable code instead of manual or static rules. Open Policy Agent (OPA) is an open-source, general-purpose policy engine that enables organizations to enforce policies consistently across cloud-native and distributed systems.

What Is Open Policy Agent (OPA)?

Open Policy Agent is a policy decision engine that separates policy logic from application code. Policies are written in Rego, OPA’s declarative policy language, and evaluated at runtime.

OPA can be used with:

Kubernetes

CI/CD pipelines

APIs and microservices

Cloud infrastructure

Service meshes

Why Use Policy as Code?

Key Benefits

Consistency: Policies are enforced uniformly

Automation: Policies can be tested and deployed like code

Scalability: Works across multiple systems and teams

Auditability: Policies are version-controlled

Security: Reduces manual configuration errors

Core Concepts in OPA

Policies

Rules written in Rego that define what is allowed or denied.

Input

Data provided to OPA for evaluation (e.g., user roles, request context).

Data

External information such as configuration or role mappings.

Decisions

OPA evaluates policies and returns allow/deny or structured decisions.

Rego Policy Example

package authz

default allow = false

allow {

    input.user.role == "admin"

}

This policy allows access only to users with the "admin" role.

How OPA Works

A request is sent to the application

The application sends input data to OPA

OPA evaluates policies

OPA returns a decision

The application enforces the decision

Common Use Cases

1. Kubernetes Admission Control

Enforce security and compliance rules

Validate resource configurations

2. API Authorization

Role-based and attribute-based access control

Centralized authorization logic

3. CI/CD Policy Enforcement

Prevent insecure deployments

Enforce infrastructure standards

4. Infrastructure as Code (IaC)

Validate Terraform and cloud configurations

Enforce cost and security policies

Policy Testing and Management

Unit testing Rego policies

Version control with Git

CI/CD integration

Policy bundles for distribution

Advantages of OPA

Language-agnostic

High performance

Declarative and expressive policies

Cloud-native and extensible

Challenges and Considerations

Learning curve for Rego

Policy design complexity

Requires good governance practices

Best Practices

Keep policies modular and reusable

Use clear naming conventions

Test policies extensively

Separate policy logic from application logic

Conclusion

Policy as Code with Open Policy Agent enables organizations to manage policies in a scalable, automated, and auditable way. By decoupling policy decisions from application logic, OPA improves security, compliance, and operational consistency across modern systems.

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ChatOps: Automating Operations via Chat

AI and ML in DevOps: Opportunities and Risks

Edge Computing and DevOps

Event-driven DevOps Pipelines

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 Why Choose Full Stack .NET Development Over Other Stacks?

Introduction

Full Stack .NET development is a popular choice for building secure, scalable, and enterprise-grade applications. It uses Microsoft’s technology ecosystem, typically including C#, ASP.NET Core, .NET, SQL Server, and modern frontend frameworks like Angular or React. Compared to other stacks, Full Stack .NET offers strong performance, long-term stability, and excellent tooling.

1. Strong Performance and Scalability

ASP.NET Core is fast and highly optimized

Excellent support for asynchronous programming

Scales well for large enterprise applications

Suitable for cloud-native and microservices architectures

2. Unified Technology Ecosystem

Single language (C#) across backend, services, and logic

Seamless integration between components

Reduces context switching and development complexity

3. Enterprise-Level Security

Built-in authentication and authorization

Strong support for OAuth, OpenID Connect, and Azure AD

Secure data access and encryption features

Regular updates and long-term support (LTS)

4. Cross-Platform Development

.NET runs on Windows, Linux, and macOS

Supports Docker and Kubernetes

Ideal for modern cloud and DevOps environments

5. Excellent Tooling and Developer Experience

Visual Studio and Visual Studio Code

Powerful debugging and profiling tools

Integrated testing frameworks

Strong IDE support improves productivity

6. Seamless Cloud Integration

Native integration with Microsoft Azure

First-class support for cloud services

Easy deployment and monitoring

Strong DevOps pipelines with GitHub Actions and Azure DevOps

7. Long-Term Stability and Support

Backed by Microsoft and a large community

Clear release roadmap

Widely used in government and enterprise systems

8. Rich Libraries and Frameworks

ASP.NET Core MVC and Web API

Entity Framework Core for data access

Blazor for full-stack C# web apps

Extensive NuGet package ecosystem

9. High Demand in the Job Market

Strong demand in enterprise and corporate sectors

Competitive salaries

Long-term career growth opportunities

10. Comparison with Other Stacks

Stack Key Difference

MERN Faster prototyping, but less structured

Java Spring Similar enterprise strength, more verbose

Python Easier for beginners, slower performance

PHP Quick setup, less scalable for large systems

Conclusion

Full Stack .NET development is an excellent choice for developers who want to build robust, secure, and scalable applications with strong enterprise support. It stands out for performance, tooling, security, and long-term reliability compared to many other stacks.

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The Role of Full Stack .NET Developers in Agile Teams

How to Keep Your Skills Up-to-Date as a Full Stack .NET Developer

Exploring the Salary Trends for Full Stack .NET Developers

How to Prepare for Full Stack .NET Developer Interviews

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 A Guide to Regularization in Generative Models

Introduction

Generative models learn the underlying distribution of data in order to generate new samples. Common examples include Variational Autoencoders (VAEs), Generative Adversarial Networks (GANs), Normalizing Flows, and Diffusion Models.

Regularization is essential in generative modeling to improve generalization, stability, and sample quality, and to prevent overfitting or mode collapse.

Why Regularization Is Important in Generative Models

Generative models are often:

High-capacity networks

Trained on limited or noisy data

Prone to instability during training

Regularization helps:

Control model complexity

Stabilize optimization

Encourage meaningful latent representations

Common Regularization Techniques

1. Weight Regularization

Penalizes large weights to reduce model complexity.

L2 regularization (weight decay)

L1 regularization (encourages sparsity)

Used in:

VAEs

GAN generators and discriminators

2. Dropout

Randomly drops neurons during training.

Reduces overfitting

Less common in GANs due to training instability

More effective in VAEs and autoregressive models

3. Data Augmentation

Increases effective dataset size by applying transformations.

Image flips, crops, noise injection

Text perturbations

Audio time stretching

Widely used in GANs and diffusion models.

4. Latent Space Regularization

a. KL Divergence (VAEs)

Encourages the latent distribution to follow a prior (usually Gaussian).

Improves smoothness and interpolation

Prevents overfitting to training samples

b. Latent Norm Constraints

Penalizes large latent vectors to maintain stability.

5. Adversarial Regularization (GANs)

a. Gradient Penalty

Ensures smooth discriminator gradients.

WGAN-GP

Reduces training instability

b. Spectral Normalization

Constrains the Lipschitz constant of network layers.

Improves convergence

Prevents discriminator domination

6. Noise Injection

Adds noise to:

Inputs

Latent vectors

Intermediate layers

Benefits:

Improves robustness

Encourages diversity in generated samples

7. Label Smoothing

Prevents the discriminator from becoming overconfident.

Softens real/fake labels

Improves GAN stability

8. Entropy Regularization

Encourages diversity in outputs by maximizing entropy.

Reduces mode collapse

Useful in GANs and autoregressive models

9. Early Stopping

Stops training before overfitting occurs.

Useful when validation metrics degrade

Common in VAEs and diffusion models

Regularization in Specific Generative Models

Variational Autoencoders (VAEs)

KL divergence

Beta-VAE (scaled KL term)

Dropout and weight decay

Generative Adversarial Networks (GANs)

Gradient penalty

Spectral normalization

Data augmentation

Noise injection

Diffusion Models

Noise scheduling

Weight decay

Data augmentation

Normalizing Flows

Jacobian regularization

Weight normalization

Choosing the Right Regularization Strategy

Match regularization to model type

Avoid over-regularization (loss of sample quality)

Monitor training stability and diversity

Tune hyperparameters carefully

Conclusion

Regularization plays a crucial role in making generative models stable, robust, and capable of producing high-quality samples. The right combination of regularization techniques depends on the model architecture, dataset size, and training objectives.

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Unsupervised vs. Supervised Learning in Generative AI

What is the Role of Optimization in Generative AI Models?

Activation Functions in Generative AI: A Deep Dive

The Role of Backpropagation in Training Generative Models

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 Writing Efficient Python Code for Full Stack Web Applications

Introduction

In full stack web applications, Python is commonly used on the backend with frameworks like Django, Flask, or FastAPI. Writing efficient Python code is essential for improving performance, scalability, maintainability, and user experience.

1. Choose the Right Framework

Different frameworks serve different needs:

Django – Best for large, feature-rich applications

Flask – Lightweight and flexible

FastAPI – High performance with async support

Choosing the right framework helps avoid unnecessary overhead.

2. Optimize Database Interactions

Database operations are often the main performance bottleneck.

Best Practices:

Use ORM efficiently (avoid N+1 queries)

Add proper database indexes

Use bulk inserts and updates

Cache frequent queries

Example:

# Use select_related or prefetch_related in Django

users = User.objects.select_related('profile').all()

3. Use Asynchronous Programming

Async programming improves performance for I/O-bound tasks.

Use async and await

Prefer async frameworks (FastAPI, Django async views)

Avoid blocking operations

4. Write Clean and Readable Code

Readable code is easier to optimize and maintain.

Follow PEP 8 standards

Use meaningful variable and function names

Break logic into small reusable functions

5. Optimize Python Logic

Use list/dictionary comprehensions

Avoid unnecessary loops

Use built-in functions (map, filter, sum)

Prefer generators for large datasets

6. Caching Strategies

Reduce repeated computations and database calls.

In-memory caching (Redis, Memcached)

HTTP caching

Template caching

7. Efficient API Design

Use pagination for large datasets

Limit response size

Compress API responses

Use proper HTTP status codes

8. Background Tasks and Queues

Move heavy tasks off the main request cycle.

Use Celery or RQ

Process emails, reports, and notifications asynchronously

9. Security with Performance

Efficient code must also be secure:

Use secure password hashing

Validate inputs

Avoid excessive logging of sensitive data

10. Testing and Profiling

Measure performance before optimizing.

Use profiling tools (cProfile, line_profiler)

Write unit and integration tests

Monitor application performance in production

11. Deployment Optimization

Use WSGI/ASGI servers (Gunicorn, Uvicorn)

Enable load balancing

Use containerization (Docker)

Conclusion

Writing efficient Python code for full stack web applications requires a balance between performance, readability, scalability, and security. By optimizing database usage, leveraging async programming, and following best practices, developers can build robust and high-performing web applications.

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Optimizing Database Queries in Full Stack Python Apps

Performance Optimization Techniques for Full Stack Python

How to Write Clean and Readable Code in Python

Best Practices for Full Stack Python Developers

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 How Cognitive Bias Affects Security Decision-Making

Introduction

Cognitive bias refers to systematic patterns of thinking that influence human judgment and decision-making. In the context of security—including cybersecurity, physical security, and organizational risk management—cognitive biases can lead to poor assessments of threats, incorrect prioritization of risks, and ineffective security controls.

Common Cognitive Biases in Security Decision-Making

1. Confirmation Bias

People tend to favor information that confirms their existing beliefs.

Security Impact:

Ignoring indicators of compromise that do not fit prior assumptions

Overlooking new attack methods

Example:

Assuming a system is secure because it has never been breached before.

2. Availability Bias

Decisions are influenced by information that is most recent or memorable.

Security Impact:

Overreacting to highly publicized attacks

Neglecting less visible but more probable threats

Example:

Focusing on ransomware because it is in the news while ignoring insider threats.

3. Optimism Bias

The belief that negative events are less likely to happen to oneself.

Security Impact:

Underestimating the likelihood of a breach

Delaying security investments

Example:

“Our organization is too small to be targeted.”

4. Anchoring Bias

Relying too heavily on initial information when making decisions.

Security Impact:

Using outdated threat models

Failing to adjust risk assessments when conditions change

5. Status Quo Bias

Preference for maintaining existing systems and processes.

Security Impact:

Resistance to security updates

Continued use of legacy systems with known vulnerabilities

6. Overconfidence Bias

Overestimating one’s own knowledge or system capabilities.

Security Impact:

Inadequate testing and monitoring

Poor incident response preparation

7. Normalcy Bias

Assuming things will continue as they always have.

Security Impact:

Failure to prepare for rare but high-impact attacks

Slow response to emerging threats

Consequences of Cognitive Bias in Security

Weak risk assessments

Inefficient allocation of security budgets

Increased vulnerability to attacks

Delayed incident response

Poor policy enforcement

Mitigating Cognitive Bias in Security Decisions

1. Use Data-Driven Risk Analysis

Rely on metrics, threat intelligence, and historical data rather than intuition.

2. Implement Structured Decision Frameworks

Risk matrices

Threat modeling (STRIDE, ATT&CK)

Red teaming and tabletop exercises

3. Encourage Diverse Perspectives

Cross-functional teams reduce groupthink and blind spots.

4. Continuous Training and Awareness

Educate teams about cognitive biases and their impact on security.

5. Automate Where Possible

Automation reduces human error in:

Threat detection

Incident response

Compliance enforcement

Conclusion

Cognitive biases significantly influence security decision-making by shaping how risks are perceived and addressed. Recognizing and mitigating these biases is essential for building resilient security strategies. By combining human awareness with structured processes and automation, organizations can make more effective and objective security decisions.

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The Neuroscience of Social Engineering Attacks

Understanding Cyber Risk Perception and User Behavior

How Decision Fatigue Impacts Online Security Behavior

The Psychology Behind Insider Threats

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🔍 Advanced Topics in MERN

 🔍 Advanced Topics in MERN (MongoDB, Express, React, Node.js)

Introduction

The MERN stack is widely used for building full-stack web applications. Advanced MERN topics focus on performance, scalability, security, maintainability, and real-world deployment, going beyond basic CRUD applications.

1. Advanced React Concepts

Custom Hooks for reusable logic

Context API vs Redux / Redux Toolkit

React Query / TanStack Query for server-state management

Code splitting and lazy loading

Performance optimization using memoization

Error boundaries

2. State Management at Scale

Redux Toolkit architecture

Normalized state design

Middleware (Thunk, Saga)

Global vs local state decisions

3. Advanced Node.js & Express

Event loop and non-blocking I/O

Cluster mode and worker threads

API rate limiting and throttling

Request validation and schema enforcement

Centralized error handling

4. MongoDB Advanced Features

Indexing strategies

Aggregation framework

Transactions and ACID compliance

Schema design patterns

Sharding and replication

5. Authentication & Authorization

JWT with refresh tokens

OAuth 2.0 and social login

Role-based access control (RBAC)

Secure password hashing (bcrypt)

Session vs token-based authentication

6. Security Best Practices

Protecting against XSS, CSRF, and SQL/NoSQL injection

Secure HTTP headers

Environment variable management

API gateway security

7. API Design & Architecture

REST best practices

GraphQL integration

API versioning

Pagination, filtering, and sorting

Microservices vs monolithic architecture

8. Performance Optimization

Server-side rendering (SSR) with Next.js

Caching strategies (Redis)

Load balancing

Database query optimization

9. Testing in MERN Applications

Unit testing (Jest)

Integration testing

End-to-end testing (Cypress, Playwright)

Mocking APIs and databases

10. DevOps & Deployment

CI/CD pipelines

Docker and containerization

Kubernetes basics

Cloud deployment (AWS, Azure, GCP)

Monitoring and logging

11. Real-Time Features

WebSockets (Socket.IO)

Real-time notifications

Chat and collaboration apps

12. Scalable Project Architecture

Clean architecture

MVC vs feature-based structure

Monorepos

Environment-based configurations

Conclusion

Advanced MERN topics prepare developers to build production-grade, scalable, and secure applications. Mastering these concepts is essential for senior-level development and real-world projects.

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MERN Blogging Platform from Scratch

Building a Forum or Commenting System

Building a Fitness Tracker in MERN

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 DataOps, Governance & Quality Engineering

Introduction

DataOps, Data Governance, and Data Quality Engineering are critical disciplines that ensure data is reliable, secure, well-managed, and delivered efficiently across an organization. Together, they enable data-driven decision-making by improving trust, speed, and consistency in data systems.

DataOps

What is DataOps?

DataOps is a set of practices that combines data engineering, DevOps, and agile methodologies to improve the speed, reliability, and collaboration of data pipelines.

Key Objectives

Faster data delivery

Automation of data workflows

Improved collaboration between teams

Continuous integration and deployment (CI/CD) for data

Core Practices

Automated data pipelines

Version control for data and code

Monitoring and logging

CI/CD for ETL/ELT processes

Tools Commonly Used

Apache Airflow

dbt

Apache Kafka

Git

Docker and Kubernetes

Data Governance

What is Data Governance?

Data Governance defines the policies, roles, standards, and processes that ensure data is used responsibly, securely, and consistently across the organization.

Key Components

Data ownership and stewardship

Data policies and standards

Metadata management

Data privacy and compliance (GDPR, HIPAA, etc.)

Benefits

Improved data consistency

Regulatory compliance

Better data accountability

Reduced risk and misuse

Data Quality Engineering

What is Data Quality Engineering?

Data Quality Engineering focuses on building systems and processes that ensure data is accurate, complete, timely, consistent, and reliable throughout its lifecycle.

Key Dimensions of Data Quality

Accuracy

Completeness

Consistency

Timeliness

Validity

Uniqueness

Quality Engineering Practices

Automated data validation

Data profiling and anomaly detection

Schema enforcement

Data quality monitoring and alerts

Tools

Great Expectations

Monte Carlo

Soda

Deequ

How They Work Together

Area Role

DataOps Delivers data pipelines efficiently

Data Governance Defines rules and ownership

Data Quality Engineering Ensures data meets quality standards

Together, they create trusted, scalable, and compliant data ecosystems.

Use Cases

Enterprise data platforms

Cloud data warehouses

Real-time analytics systems

AI and machine learning pipelines

Best Practices

Embed data quality checks into pipelines

Automate governance enforcement

Assign clear data ownership

Monitor data continuously

Treat data as a product

Conclusion

DataOps, Governance, and Quality Engineering are essential for modern data platforms. They ensure data is delivered quickly, managed responsibly, and trusted by users, enabling better business decisions and scalable analytics.

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 Hardware & Experimental Quantum Computing

Introduction

Hardware and experimental quantum computing focuses on the physical realization of quantum computers and the experimental techniques used to control, measure, and improve quantum systems. Unlike theoretical quantum computing, this field deals with building real quantum devices and validating their performance in laboratory environments.

Quantum Computing Hardware Basics

Qubits

The fundamental unit of quantum information is the qubit. Unlike classical bits, qubits can exist in superposition and become entangled.

Common qubit technologies include:

Superconducting qubits

Trapped ions

Photonic qubits

Spin qubits (quantum dots, NV centers)

Neutral atoms

Major Quantum Hardware Platforms

1. Superconducting Quantum Computers

Operate at millikelvin temperatures

Use Josephson junctions

Fast gate operations

Used by IBM, Google, and Rigetti

2. Trapped Ion Quantum Computers

Use ions confined by electromagnetic fields

High-fidelity gates

Slower operation compared to superconducting qubits

Used by IonQ and Quantinuum

3. Photonic Quantum Systems

Use photons as qubits

Operate at room temperature

Ideal for communication and networking

4. Spin-Based Quantum Systems

Use electron or nuclear spins

Compatible with semiconductor fabrication

Promising for scalable architectures

5. Neutral Atom Systems

Use laser-cooled atoms

Highly scalable arrays

Flexible qubit connectivity

Experimental Components and Infrastructure

Cryogenics

Dilution refrigerators for superconducting qubits

Essential for reducing thermal noise

Control and Readout Electronics

Microwave signal generators

Arbitrary waveform generators

FPGA-based control systems

Measurement and Calibration

Qubit state readout using resonators or fluorescence

Continuous calibration to reduce error

Experimental Quantum Computing Workflow

1. Device Fabrication

Nanofabrication of qubit structures

Cleanroom processes (lithography, deposition, etching)

2. System Integration

Packaging and wiring

Thermal anchoring

Shielding from electromagnetic noise

3. Calibration and Control

Gate tuning

Frequency calibration

Crosstalk minimization

4. Experiment Execution

Running quantum circuits

Collecting measurement statistics

5. Error Characterization

Decoherence time measurement

Gate fidelity benchmarking

Noise analysis

Key Challenges

Decoherence and noise

Scalability of qubit systems

Error correction overhead

Hardware reliability and yield

Tools and Software

Qiskit

Cirq

QuTiP

LabVIEW

Python-based control frameworks

Applications

Quantum algorithm validation

Quantum simulation

Materials science

Secure communication

Fundamental physics research

Conclusion

Hardware and experimental quantum computing bridges physics, engineering, and computer science. It plays a critical role in advancing quantum technologies by turning theoretical models into working quantum devices.

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Quantum Algorithms for Solving Linear Systems

Quantum Walks and Their Application in Computing

How Quantum Computing Is Used in Cryptography Today

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 Visualizing Complex Networks and Graphs

Introduction

Complex networks and graphs are used to represent relationships between entities in many fields such as data science, computer science, biology, social networks, transportation, and cybersecurity. Network visualization helps analysts understand structure, patterns, and behavior within complex systems.

Basics of Networks and Graphs

A graph consists of:

Nodes (Vertices): Represent entities (e.g., people, devices, web pages)

Edges (Links): Represent relationships or interactions between nodes

Graphs can be:

Directed or undirected

Weighted or unweighted

Static or dynamic

Importance of Network Visualization

Visualizing networks helps to:

Identify key nodes and influencers

Detect communities or clusters

Understand connectivity and flow

Discover anomalies or bottlenecks

Communicate complex relationships clearly

Common Types of Network Visualizations

1. Node-Link Diagrams

The most common graph visualization.

Nodes are shown as points

Edges are shown as lines

Best for small to medium-sized networks

2. Force-Directed Layouts

Uses physical forces to position nodes.

Connected nodes are pulled together

Unrelated nodes are pushed apart

Helps reveal clusters naturally

3. Adjacency Matrices

Relationships are shown in a matrix format.

Scales better for large networks

Reduces visual clutter

Useful for dense graphs

4. Hierarchical and Tree Layouts

Used when data has a parent-child structure.

Organizational charts

File systems

Decision trees

5. Geospatial Network Visualizations

Nodes are placed on maps.

Transportation networks

Communication infrastructure

Migration and trade networks

Challenges in Visualizing Complex Networks

Scalability: Large networks become cluttered

Overplotting: Too many edges overlap

Interpretability: Complex layouts can confuse users

Performance: Rendering large graphs is computationally expensive

Techniques to Improve Network Visualization

Filtering and sampling nodes or edges

Aggregating nodes into communities

Using color, size, and shape encoding

Interactive zooming and panning

Highlighting important nodes (centrality measures)

Tools and Libraries for Network Visualization

Programming Libraries

NetworkX + Matplotlib (Python)

Graph-tool

D3.js (JavaScript)

Plotly

Bokeh

Specialized Tools

Gephi

Cytoscape

Neo4j Bloom

Graphistry

Applications

Social network analysis

Biological networks (gene/protein interactions)

Recommendation systems

Fraud detection

Knowledge graphs

Best Practices

Define the goal of the visualization clearly

Choose the right layout for the data

Avoid unnecessary visual elements

Use legends and annotations

Provide interaction for exploration

Conclusion

Visualizing complex networks and graphs transforms abstract relational data into meaningful insights. By using the right techniques, tools, and design principles, analysts can uncover hidden patterns and communicate complex relationships effectively.

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A Guide to holoviews for Interactive Data Exploration

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The Power of Geospatial Data Visualization

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 VLSI Design Using SkyWater 130nm PDK

Introduction

The SkyWater 130nm Process Design Kit (PDK) is an open-source semiconductor technology released by SkyWater Technology in collaboration with Google. It enables students, researchers, and engineers to design and fabricate integrated circuits (ICs) using a 130nm CMOS technology node. This PDK is widely used for learning, prototyping, and open-source silicon projects.

Key Features of SkyWater 130nm PDK

Open-source and publicly available

Mature and stable 130nm technology

Supports analog, digital, and mixed-signal designs

Compatible with open-source EDA tools

Proven fabrication through MPW (Multi-Project Wafer) shuttles

VLSI Design Flow Using SkyWater 130nm PDK

1. Specification and Design Planning

The process begins with defining:

Functional requirements

Performance targets (speed, power, area)

Technology constraints

Example: Designing a simple processor, ADC, or digital controller.

2. RTL Design

The circuit functionality is described using Hardware Description Languages (HDLs):

Verilog

SystemVerilog

At this stage:

Logic behavior is defined

No physical details are considered

3. Functional Simulation

RTL code is verified using simulation tools such as:

Icarus Verilog

Verilator

This ensures the design behaves correctly before synthesis.

4. Logic Synthesis

The RTL code is converted into a gate-level netlist using:

Yosys (open-source synthesis tool)

The synthesis uses SkyWater standard cell libraries to map logic gates.

5. Floorplanning

Floorplanning defines:

Chip dimensions

Placement of macros and I/O pins

Power distribution strategy

This step impacts performance and routability.

6. Placement and Routing

Using tools like OpenROAD:

Standard cells are placed

Signal routing is completed

Clock trees are synthesized

The result is a complete physical layout.

7. Physical Verification

Design correctness is verified using:

DRC (Design Rule Check)

LVS (Layout vs Schematic)

Tools:

Magic

Netgen

This ensures the layout follows SkyWater design rules and matches the schematic.

8. Timing, Power, and Signal Integrity Analysis

Analysis includes:

Static Timing Analysis (STA)

Power estimation

Signal integrity checks

This confirms the design meets performance goals.

9. GDSII Generation (Tape-out)

After successful verification:

The final layout is exported as a GDSII file

This file is sent for fabrication

10. Fabrication and Testing

The chip is fabricated using SkyWater’s 130nm process.

Post-silicon steps include:

Packaging

Functional testing

Performance validation

Tools Commonly Used

Yosys – Synthesis

OpenROAD – Physical design

Magic – Layout and DRC

Netgen – LVS

OpenLane – Automated RTL-to-GDS flow

Applications

Educational VLSI projects

Research prototypes

Analog and mixed-signal ICs

Open-source silicon development

Conclusion

VLSI design using the SkyWater 130nm PDK provides a complete, real-world IC design experience using open-source tools. It is ideal for learning semiconductor design, building portfolios, and developing low-cost silicon prototypes.

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Free VLSI Tools for Students

Exploring Intel Quartus for FPGA

Working with Xilinx Vivado

Mentor Graphics Tools for Verification

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Projects & Portfolios procces in Data Anlytics

 Projects & Portfolios Process in Data Analytics

In Data Analytics, projects and portfolios are used to demonstrate skills, experience, and problem-solving ability using real or realistic data. They are important for learning, career growth, and job applications.

1. Understanding the Problem

Every data analytics project starts with a clear business or research question.

This step includes:

Defining objectives

Identifying stakeholders

Understanding success criteria

Example:

“How can sales be increased in the next quarter?”

2. Data Collection

Data is gathered from different sources such as:

Databases

Excel/CSV files

APIs

Surveys

Web scraping

The quality of data at this stage directly affects the results.

3. Data Cleaning and Preparation

Raw data is often incomplete or inconsistent.

This step involves:

Removing duplicates

Handling missing values

Correcting errors

Formatting data

This is one of the most important stages in data analytics.

4. Data Exploration and Analysis

Exploratory Data Analysis (EDA) is used to understand patterns and trends.

Techniques include:

Descriptive statistics

Data visualization

Correlation analysis

Tools commonly used:

Python (Pandas, NumPy, Matplotlib)

R

SQL

5. Modeling and Advanced Analysis (Optional)

Depending on the project, this may include:

Predictive modeling

Machine learning algorithms

Statistical testing

This step is not required for all projects but adds value to advanced portfolios.

6. Data Visualization and Storytelling

Insights are communicated through:

Dashboards

Charts and graphs

Reports

Tools:

Tableau

Power BI

Excel

The goal is to make insights clear and actionable for non-technical audiences.

7. Insights and Recommendations

The analyst explains:

Key findings

Business impact

Data-driven recommendations

This shows analytical thinking and decision-making skills.

8. Documentation and Presentation

The project is documented with:

Problem statement

Methodology

Results

Conclusion

Presentation formats:

GitHub repositories

PDFs

Blog posts

Portfolio websites

Building a Data Analytics Portfolio

A portfolio is a collection of well-documented projects that show:

Technical skills (SQL, Python, visualization tools)

Problem-solving ability

Communication skills

Best Practices:

Include 3–6 strong projects

Use real-world datasets

Clearly explain your thought process

Show visuals and code

Highlight business value

Conclusion

Projects help you practice data analytics, while portfolios help you prove your skills. A strong data analytics portfolio increases credibility and improves job opportunities.

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Data Visualization Best Practices

Introduction to Statistical Analysis for Data Analysts

Exploratory Data Analysis (EDA): Step-by-Step

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MERN Blogging Platform from Scratch

 Building a MERN (MongoDB, Express, React, Node.js) blogging platform from scratch is a great way to learn how to build full-stack applications. I'll walk you through the basic steps and key concepts, and by the end, you'll have a functional blogging platform!

Here's a high-level roadmap to building the blog platform:

1. Set Up the Development Environment

Before starting, make sure you have the following installed:

Node.js and npm (Node package manager)

MongoDB (You can use MongoDB Atlas for a cloud database if you don’t want to install MongoDB locally)

VS Code or any code editor you're comfortable with

2. Backend: Node.js + Express + MongoDB

We'll start by setting up the backend with Node.js, Express, and MongoDB. The backend will handle all the CRUD operations (Create, Read, Update, Delete) for the blog posts.

a. Initialize Node.js Project

mkdir mern-blog

cd mern-blog

npm init -y

b. Install Dependencies

We’ll need some packages for the backend:

npm install express mongoose cors dotenv body-parser

express: For the server

mongoose: For interacting with MongoDB

cors: To handle cross-origin requests (so React can talk to the backend)

dotenv: For environment variables

body-parser: To handle incoming JSON data

c. Create the Server (index.js)

Create a new file called index.js in the root directory and set up a basic Express server:

const express = require('express');

const mongoose = require('mongoose');

const cors = require('cors');

require('dotenv').config();

const app = express();

app.use(cors());

app.use(express.json());

// Connect to MongoDB

mongoose.connect(process.env.MONGO_URI, { useNewUrlParser: true, useUnifiedTopology: true })

  .then(() => console.log('MongoDB connected'))

  .catch((err) => console.log('Error connecting to MongoDB:', err));

// Define a basic route

app.get('/', (req, res) => {

  res.send('Welcome to the MERN Blog!');

});

// Start the server

const port = process.env.PORT || 5000;

app.listen(port, () => {

  console.log(`Server running on port ${port}`);

});

d. Create a .env File

Create a .env file to store sensitive information like your MongoDB URI:

MONGO_URI=your_mongodb_connection_string

e. Define Blog Post Model

Create a folder called models and add a BlogPost.js file to define the schema for the blog posts:

const mongoose = require('mongoose');

const blogPostSchema = new mongoose.Schema({

  title: {

    type: String,

    required: true

  },

  content: {

    type: String,

    required: true

  },

  author: {

    type: String,

    required: true

  },

  createdAt: {

    type: Date,

    default: Date.now

  }

});

const BlogPost = mongoose.model('BlogPost', blogPostSchema);

module.exports = BlogPost;

f. Create Routes for CRUD Operations

In your index.js file, add the routes to handle the creation, reading, updating, and deleting of blog posts.

const BlogPost = require('./models/BlogPost');

// Create a new blog post

app.post('/api/posts', async (req, res) => {

  try {

    const { title, content, author } = req.body;

    const newPost = new BlogPost({ title, content, author });

    await newPost.save();

    res.status(201).json(newPost);

  } catch (err) {

    res.status(400).json({ error: err.message });

  }

});

// Get all blog posts

app.get('/api/posts', async (req, res) => {

  try {

    const posts = await BlogPost.find();

    res.json(posts);

  } catch (err) {

    res.status(500).json({ error: err.message });

  }

});

// Get a single blog post by ID

app.get('/api/posts/:id', async (req, res) => {

  try {

    const post = await BlogPost.findById(req.params.id);

    if (!post) {

      return res.status(404).json({ error: 'Post not found' });

    }

    res.json(post);

  } catch (err) {

    res.status(500).json({ error: err.message });

  }

});

// Update a blog post

app.put('/api/posts/:id', async (req, res) => {

  try {

    const { title, content, author } = req.body;

    const updatedPost = await BlogPost.findByIdAndUpdate(req.params.id, { title, content, author }, { new: true });

    res.json(updatedPost);

  } catch (err) {

    res.status(400).json({ error: err.message });

  }

});

// Delete a blog post

app.delete('/api/posts/:id', async (req, res) => {

  try {

    await BlogPost.findByIdAndDelete(req.params.id);

    res.status(204).json({ message: 'Post deleted' });

  } catch (err) {

    res.status(500).json({ error: err.message });

  }

});

3. Frontend: React

Now, let's build the front-end using React.

a. Create the React App

Inside the mern-blog folder, create the React application:

npx create-react-app client

cd client

npm start

b. Install Axios

We’ll use Axios to make HTTP requests to the backend API.

npm install axios

c. Create Blog Components

Create a components folder inside the src folder and add components for listing posts, creating new posts, and viewing individual posts.

App.js: The main component that will route to different parts of the application.

import React from 'react';

import { BrowserRouter as Router, Route, Routes } from 'react-router-dom';

import PostList from './components/PostList';

import CreatePost from './components/CreatePost';

import ViewPost from './components/ViewPost';

function App() {

  return (

    <Router>

      <div className="App">

        <Routes>

          <Route path="/" element={<PostList />} />

          <Route path="/create" element={<CreatePost />} />

          <Route path="/post/:id" element={<ViewPost />} />

        </Routes>

      </div>

    </Router>

  );

}

export default App;

PostList.js: Display a list of all blog posts.

import React, { useEffect, useState } from 'react';

import axios from 'axios';

import { Link } from 'react-router-dom';

function PostList() {

  const [posts, setPosts] = useState([]);

  useEffect(() => {

    axios.get('http://localhost:5000/api/posts')

      .then(response => setPosts(response.data))

      .catch(error => console.log(error));

  }, []);

  return (

    <div>

      <h1>Blog Posts</h1>

      <Link to="/create">Create a New Post</Link>

      <ul>

        {posts.map(post => (

          <li key={post._id}>

            <Link to={`/post/${post._id}`}>{post.title}</Link>

          </li>

        ))}

      </ul>

    </div>

  );

}

export default PostList;

CreatePost.js: Form to create a new blog post.

import React, { useState } from 'react';

import axios from 'axios';

import { useNavigate } from 'react-router-dom';

function CreatePost() {

  const [title, setTitle] = useState('');

  const [content, setContent] = useState('');

  const [author, setAuthor] = useState('');

  const navigate = useNavigate();

  const handleSubmit = async (e) => {

    e.preventDefault();

    const newPost = { title, content, author };

    try {

      await axios.post('http://localhost:5000/api/posts', newPost);

      navigate('/');

    } catch (error) {

      console.log(error);

    }

  };

  return (

    <div>

      <h1>Create a New Post</h1>

      <form onSubmit={handleSubmit}>

        <input

          type="text"

          placeholder="Title"

          value={title}

          onChange={(e) => setTitle(e.target.value)}

        />

        <textarea

          placeholder="Content"

          value={content}

          onChange={(e) => setContent(e.target.value)}

        />

        <input

          type="text"

          placeholder="Author"

          value={author}

          onChange={(e) => setAuthor(e.target.value)}

        />

        <button type="submit">Create Post</button>

      </form>

    </div>

  );

}

export default CreatePost;

ViewPost.js: View the details of a single post.

import React, { useEffect, useState } from 'react';

import axios from 'axios';

import { useParams } from 'react-router-dom';

function ViewPost() {

  const [post, setPost] = useState(null);

  const { id } = use

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