Artificial Intelligence (AI)

• It’s becoming increasingly common in a wide range of industries, including healthcare, finance, retail, and transportation.
• AI has the potential to automate repetitive and time-consuming tasks, freeing up humans to focus on more creative and strategic work.
• AI can help businesses better understand their customers and personalize their products and services.
• AI-powered chatbots and voice assistants are becoming more common and advanced, improving customer service and interactions.
• AI is also being used to improve cybersecurity and detect fraud, which is becoming increasingly important in today’s digital world.

Blockchain

• It’s the technology behind cryptocurrencies like Bitcoin, which have gained significant attention in recent years.
• Blockchain offers a secure, decentralized, and transparent way to store and transfer data, which has implications for many industries beyond finance.
• It can be used to create smart contracts, which can automate and streamline business processes.
• Blockchain has the potential to improve supply chain management and tracking.
• It’s also being used to create new business models and disrupt traditional industries.

Cloud Computing

• It allows for on-demand access to computing power, storage, and other IT resources, without the need for companies to invest in expensive hardware and software.
• It provides scalability, enabling businesses to easily scale up or down their computing needs based on demand.
• It offers increased security, as cloud providers can invest more heavily in security measures than individual businesses.
• It promotes collaboration, making it easier for teams to work together in real-time, regardless of location.
• It enables remote work, which has become increasingly important in the post-pandemic world.

Data Analytics

• The amount of data being generated is growing exponentially, and businesses need to be able to make sense of it to stay competitive.
• Data analytics allows for insights into customer behavior, trends, and patterns, which can be used to improve marketing, product development, and operations.
• Predictive analytics, which uses historical data to make predictions about future trends and outcomes, is becoming increasingly important.
• AI and machine learning are making data analytics faster and more efficient, enabling real-time insights and recommendations.
• The need for data-driven decision-making is becoming increasingly critical in today’s digital economy.

Data Science

• Data science uses advanced statistical techniques and machine learning algorithms to uncover hidden patterns and insights from data.
• It enables businesses to make better-informed decisions and develop new products and services.
• Companies are realizing that they need to have a strong data science team in order to remain competitive in today’s data-driven world.
• The use of big data and cloud computing has made it possible to process and analyze huge amounts of data at unprecedented speeds.
• Data science is also becoming increasingly important in fields like healthcare, finance, and cybersecurity, where accurate predictions can have a huge impact.

Digital Marketing

• With more people using the internet than ever before, digital marketing offers a wider reach than traditional marketing methods.
• It allows for highly targeted marketing, making it more cost-effective than traditional methods.
• It’s highly measurable, so businesses can track and measure the performance of their campaigns and adjust accordingly.
• Social media marketing, which is a subset of digital marketing, has become a key platform for businesses to engage with their customers and build brand awareness.
• Digital marketing also enables real-time engagement with customers, allowing for instant feedback and personalization.

Cybersecurity

• Cyber threats are constantly evolving, and businesses need to stay ahead of the curve to protect themselves and their customers.
• The rise of remote work and cloud computing has created new attack vectors for cyber criminals, making cybersecurity even more crucial.
• Regulations and laws related to data privacy and security, like GDPR and CCPA, are becoming more stringent, and businesses need to be compliant.
• The cost of a data breach can be massive, not just financially, but also in terms of reputation and customer trust.
• The demand for cybersecurity professionals is high, and it’s expected to grow even more in the coming years.

DevOps

• DevOps is all about automating and streamlining the software development and deployment process, which can make organizations more efficient and agile.
• With the rise of cloud computing and microservices, DevOps is becoming even more important for rapid delivery and scalability.
• It promotes collaboration between developers and operations teams, leading to better communication and increased productivity.
• Automation tools and continuous integration and delivery (CI/CD) pipelines are becoming the norm in software development, making DevOps skills highly sought after.
• DevOps is also playing a crucial role in enabling organizations to adopt new technologies like AI, machine learning, and IoT.

Software Development

• As the world becomes increasingly digital, there’s a growing demand for software developers to create and maintain the apps and systems that power our lives.
• With the rise of new technologies like AI, IoT, and AR/VR, there’s a need for developers who can work with these new technologies.
• Software development offers a wide range of career opportunities, from mobile app development to back-end web development to systems programming.
• Companies are recognizing the importance of software development and are investing heavily in it, making it a lucrative career path.
• Software development is also a creative and challenging field that allows developers to constantly learn and grow their skills.

Web Development

• The demand for websites and web applications is still high, and many businesses are realizing the importance of having a strong online presence.
• Web development is constantly evolving, with new technologies and frameworks emerging all the time, making it an exciting field to be in.
• With the rise of mobile devices, there’s a growing demand for responsive and mobile-friendly websites.
• Web development is a highly transferable skill that can be applied to many different industries and applications, making it a versatile career choice.
• The job outlook for web developers is strong, with employment projected to grow faster than the average for all occupations.

Machine Learning (ML):

A branch of AI that allows systems to learn and improve over time with data.

• ML algorithms are becoming more powerful and efficient, allowing for more accurate predictions and better decision-making.
• ML is being applied to a wide range of industries, from healthcare and finance to retail and manufacturing, creating a lot of opportunities for skilled professionals.
• The demand for ML skills is growing faster than the supply, leading to high salaries and job security.
• The rise of big data and the increasing availability of computing power have made ML more accessible and easier to implement.
• ML is driving innovation in a wide range of areas, from self-driving cars to personalized medicine, making it an exciting field to be in.

Internet of Things (IoT): 

Connected devices that communicate with each other to automate tasks and collect data.

• The number of connected devices is growing exponentially, creating new opportunities for data collection, analysis, and automation.
• IoT is enabling new business models and disrupting traditional industries, from smart homes to connected cars to industrial automation.
• IoT is becoming more accessible and easier to implement, with advancements in connectivity technologies like 5G and low-power wide-area networks (LPWANs).
• The security and privacy challenges of IoT are being addressed with new technologies and regulations.
• IoT is creating new job opportunities, from developers and engineers to data scientists and security specialists.

Virtual Reality (VR) and Augmented Reality (AR): 

Technologies that create immersive, interactive experiences.

• VR technology is becoming more advanced and affordable, making it more accessible to consumers.
• VR is being used for a wide range of applications, from entertainment and gaming to training and education.
• The healthcare industry is exploring VR for its potential to enhance patient care and reduce costs.
• The gaming industry is using VR to create more immersive and interactive gaming experiences.
• The entertainment industry is using VR for immersive storytelling and creating unique experiences for audiences.
• The education sector is using VR for distance learning and virtual field trips, providing new ways to engage and educate students.

User Experience (UX) Design: 

The design of digital products and services to be user-friendly, intuitive, and engaging.

• As the digital world expands, the need for well-designed and user-friendly interfaces is growing.
• UX designers are instrumental in creating intuitive and seamless experiences for users, which is becoming increasingly important for businesses.
• UX design is a multidisciplinary field that involves research, psychology, design, and technology.
• The job market for UX designers is strong, with a forecasted growth rate of 22% between 2020 and 2030.
• Companies are investing more in UX design to differentiate themselves in a crowded market and to create products that stand out.
• UX design is about creating a holistic user experience that goes beyond just the visual design of a product or service.

Conclusion: 

These are all areas with a high demand for skilled professionals and a competitive salary range. If you’re looking to advance your IT career, these are some great areas to consider. In conclusion, mastering these 15 high-income IT skills can set you up for a successful and profitable career in technology. These skills are in high demand, and will likely continue to be so in the coming years. By dedicating time and effort to learning and improving in these areas, you’ll be well-positioned to take advantage of the exciting opportunities available in the tech industry. So if you’re looking to stay ahead of the curve and be at the forefront of technological innovation, these are the skills to focus on.

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Automation Testing Tools are software, applications that minimize the need for human input and can be used in a variety of ways in almost any industry. At the most basic, automation software is designed to turn repeatable, routine tasks into automated actions.

Some Examples of Automated Testing Include-  

• Unit testing
• Integration testing
• Functional testing
• Regression testing
• Acceptance testing

What are the Benefits of Automation Testing Tools

• Faster Feedback Cycle
• Testing on Multiple Platforms in Parallel
• Reusability of Test Script
• Easy Data-driven Testing
• Test Insights
• Maximum Test Coverage
• 24X7 Test Execution
• Scalability
• Low Business Costs and Better Utilization of Human Resources
• Enhance the Quality of Manual Testing
• Effective Smoke Testing
• Better Regression Testing
• Reduce the Time to Release
• Execute Lengthy Test Scenarios
• Achieve Excellent Return on Investment (ROI)

The Limitations of Automation Testing Tools

• Tests Only Check What’s Been Programmed
• Early-Stage Time
• It Is Expensive
• Loss of the Human Element
• Continuous Maintenance of Code
• Inability to Test UX

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Best in Demand and Trending Tools Used for Automation Testing

As we have discussed what automation testing tools are? Now let’s come to the trendy automation testing tools-

– Selenium

Selenium is an open-source tool that automates web browsers. It provides a single interface that lets you write test scripts in programming languages like Ruby, Java, NodeJS, PHP, Perl, Python, and C#, among others.

– Katalon Studio

Katalon Studio is an all-in-one platform for low-code web, API, mobile, and desktop (Windows) automated testing. With the prerequisites of coding lowered, Katalon Studio is ultimately designed to help teams speed up the SLDC and do quality assurance more productively.

– Appium

Appium is the most popular open-source framework for mobile app automation testing. It allows QA to automate tests for popular mobile platforms like Android, iOS, and Windows. Appium uses the mobile JSON wire protocol (an extension of Selenium JSON wire protocol) to drive native, mobile web, and hybrid applications.

– TestComplete

TestComplete is an automated testing tool which is easy to create automated tests. It provides special features for creating tests, automating test actions, running tests, etc. A single tool that handles all your test automation needs, with a variety of languages, regardless of skill level. One only needs to Create test scripts, no coding, with record-and-replay. Speed up your testing cycles and tone down your quality concerns.

– Micro Focus Unified Functional Testing-

QTP was renamed as UFT (Unified Functional Testing) by Micro-focus. The tool is primarily used for functional, regression, and service testing. Using UFT, testers can automate user actions on a web or client-based computer application and test and identify bugs that may appear when those actions are being performed.

– Cucumber

Cucumber is an open-source software testing tool written in Ruby. Basically, Cucumber testing is a type of behavior-driven development (BDD) testing that uses the Cucumber tool to define, automate, and execute test cases in a human-readable format.

– Watir

Watir is an open-source web application testing framework that is designed to make writing Selenium tests simple and efficient. Built on Selenium’s Ruby language bindings, Watir is able to drive the browser in the same way humans do.

– Cypress 

Cypress is a JavaScript-based testing automation solution used for modern web automation. It enables teams to create web test automation scripts and enables frontend developers and test automation engineers to write web tests in the de-facto web language that is JavaScript for web test automation.

– TOSCA

Tosca is an automation tool for functional and regression testing of various software products. It also includes GUI, CLI (command line interface), integrated test management, and API. The tool was developed by TRICENTIS Technology & Consulting GmbH, an Austrian company. The tool is created in VB6, C#, and Java.

Silk Test identifies all windows and controls of the application under test as objects and defines all of the properties and attributes of each window. Thus, it supports an object-based implementation. Silk Test supports both recording and descriptive programming methods to identify and verify the elements in the AUT.

What are the Latest Testing Technologies?

Software testing technologies are methods, used to design and execute tests to evaluate software applications. Some of the testing technologies commonly used- 

• Software Performance testing helps to identify how well an application can handle a growing number of users, transactions, and data volumes. Doing so enables businesses to plan and implement scalability measures early, avoiding unexpected failures and ensuring that the application can handle growing demand.

• Regression testing refers to a type of software testing that is used to verify any modification or update in software without affecting the overall working functionality of the said software. Regression testing is performed when there is a code change in a software application.

• Integration testing is the phase in software testing in which individual software modules are combined and tested as a group. Integration testing is conducted to evaluate the compliance of a system or component with specified functional requirements. It occurs after unit testing and before system testing.

• Usability testing is a technique used in user-centered interaction design to evaluate a product by testing it on users. This can be seen as an irreplaceable usability practice since it gives direct input on how real users use the system.

• Machine learning tests, on the other hand, go beyond evaluating the models’ performance on subsets of data. It ensures that the composite parts of the ML system are working effectively to achieve the desired level of quality results.

Let’s discuss the future of software testing

The future of software testing is bright, with the emergence of new technologies and trends that will help businesses remain competitive and secure. We can expect to see greater testing process automation, AI-enabled testing tools, more efficient and effective testing methods, and more comprehensive test suites with the help of below mention techniques-

• Machine Learning– Machine Learning is used to create trends that help machines comprehend data and make judgments driven by data. This technology is likely to spread in the coming years, especially in 2023 and 2024. Sources suggest that 35% of companies report they’ve been using AI in their businesses.

• IoT Testing– The Internet of Things is the umbrella term for anything that connects to the Internet. By 2025, it is estimated that there will be more than 21 billion IoT devices. Cybersecurity will continue to be a big concern as more devices will connect directly to the 5G network.

• User Experience Test– Without UX testing, it’s impossible to know if your products are really meeting the user’s needs and providing a positive experience. The UX design trends we’re seeing in 2023 look toward creating more engaging and imaginative experiences through the use of new technologies, and moving away from minimalist visuals and toward uniquely bright and bold UI.

• Test Automation– Software automation testing in the future is likely to move away from using scripts and code. Instead, more focus will be on ensuring the user experience is excellent and using new technologies like AI, ML, and blockchain.

• Agile and DevOps– The adoption of DevOps helps organizations to improve software quality, reduce development cycle time, and increase delivery speed. Through the use of automation and continuous integration and delivery (CI/CD) practices, DevOps streamlines the development process, enhances transparency, and promotes agile practices. 

• Performance Engineering– Performance Engineering has a much broader scope. It’s about injecting performance into your corporate DNA, implementing a performance mindset into your culture, and integrating performance at every opportunity throughout the Software Development Life Cycle (SDLC).

Trends in Software Testing in 2023

• Agile and DevOps
• Test Automation
• API and Services Test Automation 
• Artificial Intelligence for Testing
• Mobile Test Automation
• Test Environments and Data
• Integration of Tools and Activities

In conclusion, the Future of software testing is bright because it can be an excellent career choice for those interested in the tech industry, who enjoy problem-solving, and attention to detail. With job growth, competitive salaries, continuous learning, job satisfaction, flexibility, and job security, software testing offers many benefits.

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What is a Constructor in Python?

In Python, the constructor is defined using the init() method. The method’s name must be init(), and it must take at least one argument, which is usually self.

Types of Constructors in Python

There are two types of constructors in Python:

1. Default Constructor
2. Parameterized Constructor

Default Constructor

A default constructor in Python is a constructor that takes no arguments. It is created automatically if no constructor is defined for a class. It initializes the attributes of a class with default values. For example, if we define a class without a constructor, a default constructor will be created automatically:

class MyClass: pass

The above code defines a class called MyClass without a constructor. A default constructor will be created automatically. If we create an object of MyClass, the default constructor will be called:

obj = MyClass()

Parameterized Constructor

A parameterized constructor is a constructor that takes one or more arguments and is used to initialize the attributes of a class with user-defined values. For example, let’s define a class with a parameterized constructor:

class MyparameterisedConst: def __init__(self, arg1, arg2): self.attribute1 = arg1 self.attribute2 = arg2

The above code defines a class called MyparameterisedConst with a parameterized constructor. The constructor takes two arguments, arg1, and arg2, and initializes the attributes attribute1 and attribute2 with these values. If we create an object of MyparameterisedConst with arguments, the parameterized constructor will be called:

obj = MyparameterisedConst(“value1”, “value2”)

In the above code, we create an object of MyparameterisedConst with arguments “value1” and “value2”. The parameterized constructor will be called, and the attributes attribute1 and attribute2 will be initialized with these values.

Rules for Constructors in Python

Some rules that you will learn in the Python training course and that must be followed while defining constructors in Python:

1. init() must be the name of the constructor.
2. The constructor must take at least one argument, which is usually self.
3. The constructor can take any number of arguments, but the first argument must always be self.
4. The constructor can have default values for its arguments.
5. The constructor can be overloaded, meaning multiple constructors can be defined for a class with different numbers and types of arguments.

Let’s discuss these rules in more detail:

1 – init() must be the name of the constructor:

In Python, init() is a special method that is called automatically when an object of the class is created. If the constructor is named anything other than init(), it will not be called automatically.

2 – The constructor must take at least one argument, which is usually self:

The constructor must take at least one argument, which is usually self. The self-argument refers to the object itself and is used to access the attributes and methods of the object. Without the self-argument, the constructor will not be able to access the attributes and methods of the object.

3 – The constructor can take any number of arguments, but the first argument must always be self:

The constructor can take any number of arguments, but the first argument must always be self. This is because the self-argument refers to the object itself, and it must always be the first argument in any method of a class.

The constructor can have default values for its arguments.

The constructor can have default values for its arguments. This means that if an argument is not passed when creating an object of the class, the default value will be used instead. For example:

class MyClass:

def init(self, arg1=”default_value”):

self.attribute1 = arg1

In the above code, the parameterized constructor takes one argument, arg1, which has a default value of “default_value”. If we create an object of MyClass without passing any arguments, the default value will be used:

obj = MyClass()

print(obj.attribute1) # Output: “default_value”

 4 – The constructor can be overloaded

The constructor can be overloaded, meaning multiple constructors can be defined for a class with different numbers and types of arguments. This allows us to create objects of a class with different sets of attributes. To define multiple constructors for a class, we can use the @staticmethod or @classmethod decorators.

What are Static Methods?

Static methods are methods that do not require access to the object or its attributes. They are defined using the @staticmethod decorator. To define a static method as a constructor, we can use the following syntax:

class MyClass:

def init(self, arg1, arg2):

self.attribute1 = arg1

self.attribute2 = arg2

@staticmethod def from_list(list): return MyClass(lst[0], lst[1])

In the above code, we define a parameterized constructor that takes two arguments, arg1 and arg2. We also define a static method called from_list() that takes a list as an argument and returns an object of MyClass with the first two elements of the list as its attributes. This allows us to create objects of MyClass with different sets of attributes:

obj1 = MyClass(“value1”, “value2”)

obj2 = MyClass.from_list([“value3”, “value4”])

print(obj1.attribute1, obj1.attribute2) # Output: “value1 value2”

print(obj2.attribute1, obj2.attribute2) # Output: “value3 value4”

What are class methods?

Class methods are methods that require access to the class itself rather than the object. They are defined using the @classmethod decorator. To define a class method as a constructor, we can use the following syntax:

class MyClass:

def init(self, arg1, arg2):

self.attribute1 = arg1

self.attribute2 = arg2

@classmethod def from_dict(cls, dct): return cls(dct[“key1”], dct[“key2”])

In the above code, we define a parameterized constructor that takes two arguments, arg1, and arg2. We also define a class method called from_dict() that takes a dictionary as an argument and returns an object of MyClass with the values of the keys “key1” and “key2” as its attributes. This allows us to create objects of MyClass with different sets of attributes:

obj1 = MyClass(“value1”, “value2”)

obj2 = MyClass.from_dict({“key1”: “value3”, “key2”: “value4”})

print(obj1.attribute1, obj1.attribute2) # Output: “value1 value2”

print(obj2.attribute1, obj2.attribute2) # Output: “value3 value4”

Conclusion

Constructors are an essential part of object-oriented programming in Python. In the Python training course, you will learn that they are used to initialize the attributes of objects when they are created. The constructor method is called automatically when an object is created using the class name followed by parentheses. The self-argument refers to the object itself, and it must always be the first argument in any method of a class.

In Python, constructors can have default values for their arguments, which are used if an argument is not passed when creating an object of the class. Additionally, constructors can be overloaded, meaning multiple constructors can be defined for a class with different numbers and types of arguments. This allows us to create objects of a class with different sets of attributes.

Static methods and class methods can also be used as constructors by using the @staticmethod and @classmethod decorators, respectively. Static methods do not require access to the object or its attributes, while class methods require access to the class itself.

Overall, constructors are an important part of object-oriented programming in Python, and understanding how to use them effectively can make your code more efficient and flexible.

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Lifecycle of Thread in Java

The life cycle of a thread in Java refers to the various states that a thread can be in throughout its execution. The Java Thread class provides a set of methods that allow developers to manage the lifecycle of a thread. The lifecycle of a thread in Java is as follows:

• New State
• Runnable State
• Running State
• Blocked State
• Waiting State
• Timed Waiting State
• Terminated State

Let’s look at each state in detail.

New State

When a new instance of the Thread class is created, the thread is in the New state. In this state, the thread has not yet started to execute. The thread remains in this state until the start() method of the thread is called.

Runnable State

When the start() method is called, the thread transitions to the Runnable state. The thread is ready to run in this state but may not be executed immediately. Thread scheduler decides when to schedule the thread for execution. The thread remains in the Runnable state until it is selected by the thread scheduler to execute.

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Running State

When the thread scheduler selects the thread to execute, the thread transitions to the Running state. In this state, the thread is executing its code. The thread remains Running until one of the following happens:

• The thread completes its execution.
• The thread is blocked for some reason, such as waiting for I/O or a synchronization lock.
• The thread yields the CPU voluntarily by calling the yield() method.

Blocked State

When a thread is blocked, it transitions to the Blocked state. A thread can be blocked for various reasons, such as waiting for I/O, a lock, or sleeping. In this state, the thread is not executing and is not eligible for execution until the condition that caused it to block is resolved. The thread transitions back to the Runnable state when the condition is resolved.

Waiting State

When a thread is waiting, it transitions to the Waiting state. A thread can enter the Waiting state by calling the wait() method on an object. In this state, the thread is not executing and is not eligible for execution until another thread notifies it by calling the notify() or notifyAll() method on the same object. When the thread is notified, it transitions back to the Runnable state.

Timed Waiting State

When a thread is timed waiting, it transitions to the Timed Waiting state. A thread can enter the Timed Waiting state by calling the sleep() method or the wait() method with a timeout value. In this state, the thread is not executing and is not eligible for execution until the timeout period has elapsed or another thread notifies it by calling the notify() or notifyAll() method on the same object. When the timeout period elapses or the thread is notified, it transitions back to the Runnable state.

Terminated State

When a thread completes its execution or is terminated, it transitions to the Terminated state. The thread is no longer executing in this state, and the system can reclaim its resources.

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Explaining the States of Thread in Java

Now that we have seen the various states of a thread in Java let’s understand each state in detail.

New State

New state. In this state, the thread has not yet started to execute. The thread is created using the Thread constructor, which can take a Runnable object or a ThreadGroup object as a parameter. When the thread is created, it is assigned a name and a priority. The name and priority can be set using the setName() and setPriority() methods of the Thread class.

Declaration: public static final Thread.State NEW

Runnable State

When the start() method is called, the thread transitions to the Runnable state. The thread is ready to run in this state but may not be executed immediately. Thread scheduler decides when to schedule the thread for execution. The thread remains in the Runnable state until it is selected by the thread scheduler to execute. In a multithreaded program, multiple threads in the Runnable state can occur at any given time.

Declaration: public static final Thread.State RUNNABLE

Running State

When the thread scheduler selects the thread to execute, the thread transitions to the Running state. In this state, the thread is executing its code. The thread remains Running until one of the following happens:

• The thread completes its execution.
• The thread is blocked for some reason, such as waiting for I/O or a synchronization lock.
• The thread yields the CPU voluntarily by calling the yield() method.

Blocked State

When a thread is blocked, it transitions to the Blocked state. A thread can be blocked for various reasons, such as waiting for I/O, a lock, or sleeping. In this state, the thread is not executing and is not eligible for execution until the condition that caused it to block is resolved. The thread transitions back to the Runnable state when the condition is resolved. In a multithreaded program, multiple threads can be in the Blocked state at any time.

Declaration: public static final Thread.State BLOCKED

Waiting State

When a thread is waiting, it transitions to the Waiting state. A thread can enter the Waiting state by calling the wait() method on an object. In this state, the thread is not executing and is not eligible for execution until another thread notifies it by calling the notify() or notifyAll() method on the same object. When the thread is notified, it transitions back to the Runnable state. In a multithreaded program, multiple threads can be in the Waiting state at any time.

Declaration: public static final Thread.State WAITING

Timed Waiting State

When a thread is timed waiting, it transitions to the Timed Waiting state. A thread can enter the Timed Waiting state by calling the sleep() method or the wait() method with a timeout value. In this state, the thread is not executing and is not eligible for execution until the timeout period has elapsed or another thread notifies it by calling the notify() or notifyAll() method on the same object. When the timeout period elapses, or the thread is notified, it transitions back to the Runnable state. In a multithreaded program, multiple threads can be in the Timed Waiting state at any time.

Declaration: public static final Thread.State TIMED_WAITING

Terminated State

When a thread completes its execution or is terminated, it transitions to the Terminated state. In this state, the thread is no longer executing, and its resources can be reclaimed by the system. Once a thread is in the Terminated state, it cannot be restarted. In a multithreaded program, multiple threads can be in the Terminated state at any time.

Declaration: public static final Thread.State TERMINATED

Conclusion

In this article, we have explained the lifecycle and states of a thread in Java. Understanding the thread lifecycle and states is important for writing efficient and robust multithreaded programs. By knowing the various states that a thread can be in, developers can write code that takes advantage of the concurrency features provided by Java. It is important to note that writing multithreaded programs can be challenging and error-prone. Developers should be careful when writing multithreaded code and use synchronization mechanisms, such as locks and semaphores, to ensure that threads do not interfere.

In summary, the thread lifecycle in Java consists of six states: New, Runnable, Running, Blocked, Waiting, Timed Waiting, and Terminated. Each state represents a different stage in the life of a thread, and a thread can transition between states based on its execution and synchronization needs. Understanding the thread lifecycle and states is crucial for developing efficient and reliable multithreaded programs in Java.

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Data science is an interdisciplinary field that involves the extraction, analysis, and interpretation of data using various techniques from statistics, computer science, and domain-specific knowledge. It is used to gain insights and knowledge from complex and large datasets and to inform decision-making processes. Data scientists use tools such as machine learning, data visualization, and data mining to identify patterns and trends. And correlations in data that can be used to solve real-world problems and inform business strategies.

WHO IS A DATA SCIENTIST?

A data scientist is a professional who combines statistical and mathematical skills with computer programming and domain-specific expertise to analyze complex data and extract insights. Data scientists use a variety of tools and techniques to process, clean, and analyze data. Including statistical modeling, machine learning, data mining, and data visualization.

Data scientists typically work with large datasets, using their analytical and problem-solving skills to identify patterns and trends in the data that can inform business decisions or research findings. They may work in a variety of industries, such as healthcare, finance, retail, and technology, among others.

In addition to technical skills, data scientists must also possess strong communication and collaboration skills to effectively communicate. Their findings to stakeholders and work effectively in cross-functional teams. A successful data scientist must have a deep understanding of statistical and computational concepts, as well as domain-specific knowledge that is relevant to the industry in which they work.

DO DATA SCIENTISTS REQUIRE CODING?

One of the most debated topics in data science is whether or not coding is necessary for a data scientist. Some argue that data scientists can rely solely on pre-built tools and software, while others argue that coding skills are essential to succeed in the field.

In this blog post, we will explore this topic in-depth and examine the role of coding in data science. We will also discuss the skills required for a data scientist in 2023 and the future of the field.

DOES DATA SCIENCE REQUIRE CODING?

Yes, data science needs coding. Because Data science typically requires coding skills. Programming languages such as Python, R, and SQL are commonly used by data scientists to manipulate and analyze data, build models, and create visualizations. While some data science tools may have graphical user interfaces that do not require coding, having a solid foundation in coding is still considered an essential skill for data scientists.

That being said, the level of coding expertise required may vary depending on the specific role and industry. For instance, some data science roles may involve more complex software development tasks, while others may require more basic scripting knowledge. In general, however, the ability to write and debug code is crucial for working with data and building effective models.

THE ROLE OF CODING IN DATA SCIENCE

Coding plays a critical role in data science. It allows data scientists to manipulate and analyze data using programming languages and tools. Some examples of programming languages commonly used in data science include Python, R, SQL, and Java.

Coding skills are essential for a data scientist to be able to perform tasks such as data cleaning, data exploration, data visualization, and building machine learning models. Without coding skills, a data scientist may struggle to effectively work with data, and their ability to extract insights and knowledge from it may be limited.

Furthermore, coding skills allow data scientists to customize and fine-tune their analyses and models to meet the specific needs of their organization. This can lead to more accurate and relevant insights and predictions, which can drive better decision-making.

What Programming Languages Are Used in Data Science?

If you are setting out to learn a new language specifically for Data Science, the best language to learn is Python. Some blogs highlight a whole host of languages, tools, and technologies.

DATA SCIENTIST SKILLS IN 2023

As the field of data science continues to evolve, the skills required for a data scientist are also changing. In 2023, data scientists will need to possess a mix of technical, domain-specific, and soft skills to be successful.

Technical skills required for a data scientist in 2023 may include proficiency in programming languages. Such as Python, R, and SQL, as well as knowledge of machine learning algorithms, data visualization techniques, and big data technologies.

In addition to technical skills, domain knowledge is also becoming increasingly important for data scientists. Data scientists should have a deep understanding of the industry or field they are working in, including knowledge of relevant regulations, data privacy laws, and ethical considerations.

Soft skills are one of the skills required for a data scientist. Data scientists must be able to communicate complex findings and insights to non-technical stakeholders, collaborate effectively with cross-functional teams, and possess critical thinking and problem-solving abilities.

In summary, the skills required for a data scientist in 2023 go beyond technical proficiency. Domain knowledge and soft skills will be just as important as technical skills, and data scientists must be able to work collaboratively, think critically, and communicate effectively to be successful in the field.

THE FUTURE OF DATA SCIENCE

The field of data science is rapidly evolving, and there are several predictions for its future in the coming years. One major trend is the increasing use of artificial intelligence (AI) and machine learning (ML) in data science. As these technologies continue to advance, data scientists will be able to automate more tasks and extract insights from data more efficiently.

Another trend in data science is the growing importance of ethics and privacy considerations. As data becomes increasingly valuable and sensitive, data scientists will need to be able to navigate ethical dilemmas and ensure that they are using data responsibly.

Continuous learning and upskilling will also be critical for data scientists in the future. As the field continues to evolve, data scientists must stay up-to-date with the latest technologies and techniques to remain competitive.

In summary, the future of data science will be characterized by increased use of AI and ML, a focus on ethics and privacy, and a need for continuous learning and upskilling. Data scientists who stay ahead of these trends and continuously update their skills will be well-positioned for success in the field.

CONCLUSION

In conclusion, coding is an essential aspect of data science, and data scientists require strong coding skills to be able to effectively work with data and build models. As the field of data science continues to evolve, data scientists will also need to possess a mix of technical, domain-specific, and soft skills to be successful in 2023 and beyond.

In addition to technical proficiency, domain knowledge and soft skills such as critical thinking, communication, and collaboration will be just as important. Data scientists will also need to stay up-to-date with the latest technologies and continuously update their skills to remain competitive in the field.

Overall, the role of coding in data science and the skills required for a data scientist in 2023 reflect the dynamic and rapidly evolving nature of the field. By developing a well-rounded skill set and continuously learning, data scientists can make meaningful contributions to their organizations and advance their careers in data science.

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However, data science is not a field for everyone. It requires a certain set of skills and a certain level of education and meets the requirements of data science course eligibility. In this article, we will discuss the Data Science Course Eligibility criteria for data science, including the syllabus, skills, and expected salary. You will get to know who cannot or who can do a data scientist course.

Data Science Syllabus

Data science courses cover a wide range of topics, including statistics, machine learning, programming, and data visualization. While the exact syllabus may vary depending on the institution offering the course. Here are some of the topics you can expect to cover in a typical data science course:

1 – Statistics: 

Data science is all about extracting insights from data, and statistics are the foundation of this process. You will learn about descriptive statistics, probability theory, statistical inference, and hypothesis testing.

2 – Machine learning: 

Machine learning is a subfield of artificial intelligence that focuses on algorithms that can learn from data. You will learn about different types of machine learning, including supervised learning, unsupervised learning, and reinforcement learning. You will also learn about popular machine learning algorithms like linear regression, logistic regression, decision trees, and neural networks.

3 – Artificial Intelligence: 

In a data science course, you can expect to learn about various AI techniques. Such as machine learning, deep learning, natural language processing, computer vision, and robotics. You will also learn about the algorithms and models used in AI, such as decision trees, support vector machines, neural networks, and clustering.

4 – Programming:

 Data science requires strong programming skills. You will learn how to code in languages like Python and R, which are widely used in the field. You will also learn about data structures, algorithms, and software engineering practices.

5 – Data visualization:

The technique of presenting data in a graphical or visual format is known as data visualization. You will learn about different types of charts and graphs, as well as how to use visualization tools like Matplotlib and Tableau.

6 – Data cleaning and preprocessing: 

Raw data is often messy and requires cleaning before it can be used for analysis. You will learn about techniques for cleaning and preprocessing data, including handling missing values, dealing with outliers, and normalizing data.

7 – Big data technologies: 

As datasets continue to grow in size, it is important to be familiar with big data technologies like Hadoop and Spark. You will learn about these technologies and how to use them for distributed computing.

8 – Data ethics: 

Data ethics covers a range of topics, including privacy, bias, fairness, and accountability. In a data science course, students may learn about these topics in the context of real-world case studies and practical examples.

For example, students may learn about the ethical implications of using facial recognition technology or predictive algorithms that could potentially perpetuate biases. They may also learn about the importance of obtaining informed consent from individuals whose data is being collected and used.

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Data Scientist Skills

Data science is a highly technical field that requires a specific set of skills. Here are some of the skills you will need to succeed as a data scientist:

1. Strong problem-solving skills: 

Data scientists are tasked with solving complex problems using data. You will need to be able to break down a problem into smaller components and identify the best approach to solving it.

2. Analytical thinking: 

Data scientists need to be able to analyze large amounts of data and identify patterns and insights. You will need to be able to think critically and ask the right questions to extract meaningful insights from data.

3. Programming skills: 

Python and R are two examples of computer languages that data scientists must be knowledgeable with. You will need to be able to write clean, efficient code and be familiar with data structures and algorithms.

4. Statistics knowledge: 

Data scientists need a strong foundation in statistics to be able to analyze data and draw conclusions. You will need to be familiar with probability theory, hypothesis testing, and regression analysis.

5. Data visualization:

Data scientists need to be able to present their findings clearly and concisely. You will need to be able to create visualizations that effectively communicate your insights to non-technical stakeholders.

6. Communication skills: 

Data scientists must be able to explain their findings to stakeholders who are not technical. You will need to be able to explain complex concepts in simple terms and be comfortable presenting your findings to a diverse audience.

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Data Scientist Salary

Data science is a highly lucrative field, with salaries varying depending on factors such as experience, location, and industry. The average data scientist’s salary in India is around INR 10 LPA. However, this can vary significantly depending on the company and the location.

– Depending on Experience:

Data scientists with less than a year of experience can expect to earn an average salary of around INR 8 LPA. While those with 5-9 years of experience can earn around INR 14 LPA. Senior data scientists with over 10 years of experience can earn upwards of INR 26 LPA.

– Depending on Industry:

In terms of industry, data scientists in the finance sector tend to earn the highest salaries, with an average salary of around INR 13 LPA. This is followed by the technology and healthcare sectors, with average salaries of around INR 10 LPA and INR 13 LPA, respectively.

– Depending on Location:

The location also plays a significant role in determining salaries for data scientists. Cities with high costs of living tend to offer higher salaries, while smaller cities and rural areas tend to offer lower salaries. For example, data scientists in Bangalore can expect to earn an average salary of around INR 11 LPA, while those in smaller cities can expect to earn an average salary of around INR 6 LPA.

Conclusion

In this article, we helped you figure out how to become a data scientist in India. For individuals with the appropriate skills and education, data science is a fast-expanding sector that presents interesting career options. If you are considering a career in data science, it is important to have a strong foundation in statistics, programming, and data visualization. You will also need strong problem-solving skills and the ability to think analytically.

Data science courses can provide you with the skills and knowledge you need to succeed in this field, but it is important to choose data science subjects that cover the topics that interest you and are taught by qualified teachers. Additionally, it is important to stay up-to-date with the latest trends and technologies in data science, as the field is constantly evolving.

In terms of salary, data science offers a high-paying career with plenty of opportunities for growth and advancement. However, salaries can vary significantly depending on experience, location, and industry, so it is important to do your research and understand what to expect before pursuing a career in data science.

Overall, data science is an exciting and rewarding field that offers plenty of opportunities for those with the right skills and qualifications. With the demand for skilled data scientists continuing to grow, now is a great time to consider pursuing a career in this field.

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In a database management system (DBMS), attributes refer to the characteristics or properties of an entity or object in a database. Attributes are essential elements in database design as they provide the details and specifications needed to define and organize data within a database.

Attributes can be used to identify and describe objects in a database, such as a customer’s name, address, and phone number, or a product’s price, quantity, and description. They play a crucial role in the design and structure of a database. As they help to ensure that data is stored and organized in a meaningful and efficient manner.

Understanding the different types of attributes in a DBMS is essential for effective database design and management. By carefully selecting and organizing attributes, database designers can create databases that are optimized for specific purposes. Such as inventory management, customer relationship management, or financial reporting.

WHAT ARE THE ATTRIBUTES OF DBMS?

Attributes in a DBMS refer to the specific characteristics or properties of an entity or object that are stored within a database. These attributes are used to describe and identify the objects and to organize and retrieve data from the database.

Attributes have several key characteristics, including:

• Data type: The type of data that an attribute can hold, such as text, number, or date.
• Domain: The range of values that an attribute can take on, such as a list of allowed values or a set of rules for data validation.
• Nullability: Whether or not an attribute can contain null values or missing data.
• Default value: A value that is automatically assigned to an attribute if no value is provided.
• Uniqueness: Whether or not each value of an attribute must be unique within a database or table.

Examples of attributes in a database include:

• In a customer database, attributes could include name, address, phone number, email address, and customer ID.
• In an inventory database, attributes could include product name, description, price, quantity on hand, and SKU number.
• In an employee database, attributes could include name, date of birth, hire date, job title, and department.

Overall, attributes are essential elements in a DBMS that help to organize and describe the data stored within a database. They provide a framework for effective data management and retrieval, making it easier to access and analyze the information as needed.

DEFINITION OF ATTRIBUTES IN DBMS AND ITS USE

Attributes play a crucial role in database management systems (DBMS) by defining the characteristics of data stored in a database.

Attributes provide a way to organize data into meaningful categories, allowing users to easily retrieve specific information from the database. They also help ensure data accuracy and consistency by defining the allowable values and formats for each attribute.

Attributes can be used to define relationships between entities in a database, such as foreign keys that link one table to another. They can also be used to specify constraints and rules that govern data manipulation, such as ensuring that a customer’s age is greater than 18 before allowing them to purchase alcohol.

In addition, attributes can be used to improve database performance by indexing frequently accessed data, allowing for faster searches and queries. They can also be used to aggregate data and generate reports based on specific criteria.

TYPES OF ATTRIBUTES IN DBMS

1. Simple Attributes

Simple attributes are attributes that cannot be divided or broken down into smaller components. They represent a single value or fact about an entity. Examples of simple attributes include name, age, weight, and height.

Characteristics of simple attributes include:

• They are atomic and cannot be broken down into smaller components
• They represent a single value or fact about an entity
• They are the most basic type of attribute in a database

2. Composite Attributes

Composite attributes are attributes that can be divided into smaller sub-parts or components. They represent a collection of related simple attributes. Examples of composite attributes include address, which can be broken down into street name, city, state, and zip code.

Characteristics of composite attributes include:

• They are made up of multiple related simple attributes
• They can be broken down into smaller sub-parts or components
• They represent a more complex type of attribute in a database

3. Single-Valued Attributes

Single-valued attributes are attributes that can have only one value at a time. Examples of single-valued attributes include the date of birth and social security number.

Characteristics of single-valued attributes include:

• They can only have one value at a time
• They represent a single fact or piece of information about an entity
• They are the most common type of attribute in a database

4. Multi-Valued Attributes

Multi-valued attributes are attributes that can have multiple values for a single entity. Examples of multi-valued attributes include phone numbers and email addresses.

Characteristics of multi-valued attributes include:

• They can have multiple values for a single entity
• They represent a collection of related pieces of information about an entity
• They are a more complex type of attribute in a database

5. Derived Attributes

Derived attributes are attributes that are derived from other attributes in the database. They are not directly stored in the database but are calculated or derived from other attributes. Examples of derived attributes include age, which can be derived from the date of birth.

Characteristics of derived attributes include:

• They are calculated or derived from other attributes in the database
• They are not directly stored in the database
• They provide a way to calculate or derive additional information from existing data

Explain the closure of attributes in DBMS

An attribute X’s closure is the set of all attributes that depend on X in relation to F. X+ represents what X can determine. 

Algorithm 

Algorithm to compute X+

• Step 1 − X+ =X 
• Step 2 − repeat until X+ does not change 
• For each FD Y->Z in F 
• If Y ⊆ X+ then X+ = X+ U Z 

Example   

Consider a relation R(A,B,C,D,E,F) 

E->A, 
E->D,  
A->C,  
A->D,  
AE->F,  
AG->K. 

Find the closure of E or E+

Solution

The closure of E or E+ is as follows − 

E+ = E 
=EA   {for E->A add A} 
=EAD  {for E->D add D} 
=EADC {for A->C add C} 
=EADC {for A->D D already added} 
=EADCF{for AE->F add F} 
=EADCF{for AG->K don’t add k AG ⊄ D+)

In conclusion, understanding attributes is crucial for effective database management. Attributes are the building blocks of a database and provide the means to organize and retrieve data.

There are different types of attributes in a database, including simple attributes, composite attributes, single-valued attributes, multivalued attributes, and derived attributes. Simple attributes represent a single value, while composite attributes are composed of multiple simple attributes. Single-valued attributes have only one value per entity, while multivalued attributes can have multiple values. Derived attributes are calculated based on other attributes in the database.

Each type of attribute has its own unique characteristics, and it is important to understand their differences to design a database that is efficient and effective for its intended use. By carefully considering the attributes in a database, developers can create a structure that meets the needs of its users and ensures accurate and easy access to information.

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1 – Career Advancement Opportunities:

Software testing is a critical aspect of the software development process, and IT professionals with expertise in this area are in high demand. By acquiring software testing skills, IT professionals can position themselves for career advancement, including promotions and higher salaries.

2 – Increased Confidence:

IT professionals who have undergone software testing training will have a deeper understanding of the software development process and the role of testing in it. This increased understanding can lead to increased confidence in their abilities and greater fulfillment in their careers.

3 – Better Quality Software:

Software testing is an essential part of the software development process because it helps to identify and fix defects in the software before it is released to the public. By acquiring software testing skills, IT professionals can play a key role in ensuring that the software they develop is of high quality and meets the needs of its users.

4 – Improved Problem-Solving Skills:

Software testing requires critical thinking and problem-solving skills. IT professionals who undergo software testing training will have the opportunity to develop these skills, which can be applied to other areas of their careers.

5 – Improved Job Performance:

Software testing training helps IT professionals to understand the importance of testing and how to implement it effectively. This can result in improved job performance and a better understanding of the software development process.

 6 – It’s data-driven:

Software testing training is creative and technical, which is one of its most demanding things. It requires a certain level of automation. Coding and developing as a constitutional base, they also analyze day-to-day data and product trends. Data science literacy is important because you must understand how the software works and is built.

7 – Learn new concepts:

There is always something new in the software testing online course career. They cover all the things you need. You get to learn to code, automate or create security tests, and you have a very supportive team behind you to help you succeed. You’ll never become inactive in software testing training because you’ll always be developing and improving it.

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Future of software testing training 

The field of software testing has undergone significant changes in recent years, and it is expected to continue to develop in the future. With the increasing demand for high-quality software applications, the importance of software testing training has become even more apparent. The benefits of software testing training for IT professionals is one of the main reason why an increasing number of professionals are applying for a software testing training program. So let’s explore the future of software testing training and what it will offer to IT professionals.

1 – Automation:

With the increasing use of automation in software development, the focus on software testing automation is expected to grow. Future software testing training is expected to emphasize automation techniques and tools like Selenium and Appium.

2 – Artificial Intelligence and Machine Learning:

Artificial intelligence and machine learning revolutionize how software is tested and developed. Future software tests look forward to including a deeper dive into these technologies and how they can be used in software testing.

3 – DevOps and Continuous Integration:

DevOps and continuous integration have become increasingly popular in recent years, and the trend is expected to continue. Future software testing training is anticipated to cover these topics in detail, including how they can streamline the software development and testing process.

4 – Cloud Computing:

Cloud computing has become a staple of the technology industry, and it is expected to play a significant role in the future of software testing. Future software testing training looks forward to including a section on cloud testing, including how to test applications deployed in the cloud.

5 – Agile Methodology:

Agile methodology has become the preferred approach to software development, and it is expected to continue to dominate in the future. Future software testing training should include a section on Agile testing, including how to test software in an Agile environment.

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Why Should You Attend Software Testing Training?

Why should you consider attending training? There are endless reasons why, especially for the software industry. It would help if you considered attending software testing training, as the benefits of software testing training for It professionals are numerous. Continuous learning and personal development are essential for success in any career. Whether you are just starting or have been in your field for many years, attending training is a great way to acquire new skills, knowledge, and perspectives that can help you grow and succeed in your career.

Here are some of the personal benefits of attending training:

• Networking with other students as well as with the instructor
• Increasing your productivity at work by applying the learned principles
• Staying current with updates and best practices
• Boosting your resume by working towards or earning certificates
• Seeing examples play out in front of you
• Learning real-world situations from both the instructors as well as the other students
• Cutting time in the learning process

Conclusion

The benefits of software testing training for IT professionals are clear. Software testing training is a valuable investment for IT professionals who want to enhance their careers and contribute to developing high-quality software applications. With the increasing demand for software that meets users’ needs and is free of defects, the importance of software testing has become even more pronounced. In this article, we tried our best to meet your expectations. This article will help you.

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Java is one of the most used programming languages among developers. Java was initially developed by Sun Microsystems and called OAK in the 1990s. Later in 1995, its name changed to Java. Java is a class-based, object-oriented programming language developed for having lesser implementation dependencies. It is a computing platform for application development. The objective of Sun Microsystems was to develop an open software development platform and promote  Java as an application programming language. 

Java is an acronym for “Java programming language.” The language is a statically typed language based on the idea of “packages.” Java is a fast, secure, and reliable programming language. It is popular among programmers and software developers and has become a staple of enterprise software. 

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Java is an object-oriented programming language,  i.e. it has many characteristics similar to procedural and object-oriented programming languages. Java introduced the concept of class and object, where programmers can create objects that are class instances and pass those instances to other methods or classes. The programming language creates desktop, web, server, and mobile applications. Java is popular due to its cross-platform feature that enables a Java program to run on multiple operating systems, including Windows, macOS, Linux, and more.

How is a program executed in Java?

In contrast to other compilers, the Java compiler does not create native executable files or platform-specific code. Instead, the Java compilation process creates a unique format known as Bytecode. This generated file is also known as a class file. The Java Byte Code is a set of machine instructions for the Java Virtual Machine processor chip, also called Java Virtual Machine. Java Byte Code has similar characteristics to machine language, but unlike machine language, Java byte code is the same on every platform. No platform can directly execute the byte code. Regardless of the platform on which the byte code was created, a natively executable code can be produced utilizing byte code using a JVM interpreter. The platform still requires an interpreter to run Java code that has been converted to bytes. Java is a language that can be both compiled and interpreted.

Step-by-step execution of a Java program:

1. Whenever a Java code is written, javac compiles it.
2. Instead of machine-native code, the JAVA compiler results in a .class file or the Bytecode.
3. The Bytecode generated is not machine-executable and needs an interpreter to run on a computer. The JVM serves as the interpreter; thus, the Bytecode is executed by the JVM.
4. And finally, the program runs to produce the intended output.

What does platform independence mean?

A platform is a piece of hardware or software that a program frequently uses to execute. A platform is a particular set of hardware, an operating system, and software that creates a setting where applications can execute. Java Runtime Environment is the platform to run Java programs. Platform independence means how the program executes independently from the system’s operating system. The “Write Once Run Anywhere (WORA)” approach is used. It indicates that applications can be run on various platforms. If our application is platform-dependent, in that case, the application will not run on other platforms.

Platform independence reduces planning, execution, and translation efforts during the application process. In simple words, it is not dependent on other platforms. In Java, programs are compiled into bytecode, which is platform-independent and can run on all operating systems. Java programs can be run on various systems without having to be manually rewritten for each platform or “Write Once, Run Everywhere” (WORA). This aspect makes the Java platform independent. 

Why is the Java platform independent?

The Java package comes with JDK and JRE. JRE consists of a set of libraries and other files that JVM uses at runtime. JDK contains development tools helpful in developing and compiling Java code. After compilation, it results in a .class file. This class file can run using JRE. JVM will also be installed automatically if JRE is already on the system. Java Virtual Machine(JVM) makes the Java platform independent. 

Languages like C and C++ directly generate the machine code specific to one platform. Only the operating system in which it was compiled can understand and run this compiled code. In the case of Java, the Java program is compiled into bytecode. This bytecode can run on a virtual machine that is JVM.

In-Demand Software Development Skills

This Java virtual machine provides the same environment for the byte code to execute on whatever operating systems are present. Thus, the Java code or the byte code can run anywhere, irrespective of the platform, because of JVM. There are various JVMs available for each kind of platform. JVM can read .class files or byte code. These bytes are machine-native codes because they allow us to write Java code, compile it here, and run it on any system. But, there is only one requirement: the JVM needs to be installed on the system where the byte code will be run. 

The main aim of developing Java is its platform independence feature. Although Java can be used on any platform, JVM must first be installed. JAVA is a platform-independent language, whereas the JVM is platform-dependent. The important point about Java is that the JVM depends on the operating system. Mac OS will have a different JVM, while Windows or some other operating system will have a different one. So we can conclude that JVM is platform-dependent, which is why Java is a platform-independent language.

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Why is Java not completely platform-independent?

Java is not entirely platform-independent. The Javac compiler first compiles the programmer’s High-Level program code into byte code. Although this bytecode is platform-independent, a Just In Time (JIT) interpreter/compiler is necessary. JVM runs the program by interpreting and compiling the byte code into machine code, JVM, which features JIT, runs the program.

The Java Virtual Machine interpreter is platform-specific. Various JVMs are found on various systems. For instance, the JVMs for Windows and MAC OS differ. This JVM can read the .class file or byte code.

So, Java is independent of the platform used by Java Byte Code, as can be inferred. Yet, the Java Virtual Machine, which is platform-specific, is necessary for the execution of Byte Code on any platform.

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Some types of guesses are more accurate than others, however. For example, many people know that they are more likely to hit a target if they are standing closer to it. While it’s true that distance affects accuracy in this type of situation, other factors also influence how well you can hit a bullseye at any given distance.

How to solve Guesstimates

There are a lot of questions that can be labeled as guesstimate questions. Questions about numbers, such as how many people are in a room or how much money is in an account. Or questions about causes, like why a group of things is happening or why two events are occurring in the same order. There are several ways to answer guesstimate questions. The best way is to show why your answer is the right one and to convince the other person that their answer is wrong. To do this, you need to use several types of evidence. 

There are a lot of questions that can be labeled as guesstimate questions. Questions about numbers, such as how many people are in a room or how much money is in an account. Or questions about causes, like why a group of things is happening or why two events are occurring in the same order. There are several ways to answer guesstimate questions. The best way is to show why your answer is the right one and to convince the other person that their answer is wrong. To do this, you need to use several types of evidence.

Example

For example, if you are asking the person how many people are in a room, you could start by saying, “There are 5 people in the room, and there are 8 people in the room.” Then you could give a number to prove that your answer is right. For example, you could say, “There are five people in the room and there are seven people in the room.” The reason for the difference is that I added one more person to the room and I had to add one more person to the room. Or you could show the person that your answer is wrong by giving them a counterexample.

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Top 8 Guesstimate Questions and Answers:

1 – How many washing machines are bought every year in India?

First, please specify whether we will only examine washing machines made in India or both domestically and abroad.

If we are taking both into account, we will then omit segments depending on a few criteria. While dividing the population of India, keep in mind that there are typically 4 people living in each home. Include the annual need for new refrigerators and replacements in your estimate as well.

In your calculations, take into account the typical lifespan of a refrigerator (10 years) and the anticipated annual growth rate of refrigerators.

2 – Amount of coffee consumed in India per day

Let’s say that 20 percent of the 140 crore people in the nation do not drink coffee. Out of the remaining group, 10% don’t drink coffee, 20% drink it rarely, and 30% drink it every day. Let’s assume that daily coffee drinkers might have three cups per day, whereas infrequent coffee drinkers might have two cups per week.

3 – What is the weight of a Taj hotel?

This is one of the examples of a guesstimate that the interviewer will use to gauge the importance of several considerations. You would also consider the hotel’s dimensions and the weight of the materials used to create the hotel when approximating such a question. The ultimate estimation of the hotel’s weight will also depend on whether there are people within the structure or not.

4 – Number of people that live in your housing society

Imagine a society where everyone lives in apartments. Remember that there is 40 apartments total because they are number from 1 to 40. 9 floors and 3 units make up each apartment building. 9 × 3 x 40 equals 1080 flats.

Indian homes typically have 5 people in them. Unoccupied apartments are possible. if we assume that ten percent of units are vacant. Hence, the total population of the society will be 1080 * 0.9 * 5 = 4860.

5 – How many cricket balls can fill up an average-sized room?

 You must first be aware of the size of the cricket ball. Either ask the interviewer or presume its magnitude are viable options.

Calculate the room’s volume now and divide it by the volume of the balls. Because the balls are spherical, a regular arrangement will result in space because of their shape.

The cricket ball appears to take up 4 cubic inches of space; 1000 balls would fill the same amount of space.

6 – How many hp laptop users exist in India?

First, clarify that the question includes all models of hp laptops. Second, determine the population. India has approximately 140 crore people, with about 40 percent being children and the elderly, so we won’t count them. Assume the percentage of office goers and students who use the laptop. The people below the middle class and be eliminated in this guesstimate. This gives us precisely the market of hp laptops in India.

7 – The number of cars being used in Bangalore?

Know the population of Bangalore. Consider the average family size. Know the percentage of middle and upper-class families excluding families from below the poverty line. Multiply the total families you got by the population of Bangalore.

8 – How many sanitary pads are disposed of in India every month?

We know that the population of India is 140 crores. To solve this type of question, we must also know the man-to-women ratio in India which is 1020 women for 1000 men. It is safe to say that the ratio is almost 50:50 which makes the women population in India more than 70 crores. Finding out teens and adults from it by guessing the correct percentages. If a female uses 15 pads per month. Then multiplying the population with this number will give us the number of pads.

These were the top guesstimate questions and answers whose variations can be asked of you in an interview. You need to be quick, witty, and confident to answer them to ace the interview.

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