C# is pronounced “C-Sharp”, C# is a modern, object-oriented, general-purpose programming language developed by Microsoft. It was introduced in the early 2000s as part of the .NET framework and has since become one of the most popular programming languages. C# is a powerful language that can be used to create a wide variety of applications, including web applications, desktop applications, and mobile applications and more.

What are feature of C# ?

Some of the key features of C# include:

1. Object-Oriented: C# is a fully object-oriented language, allowing developers to create and use classes, objects, inheritance, polymorphism, and encapsulation.
2. Garbage Collection: C# uses automatic garbage collection to manage memory, making memory management easier for developers.
3. Interoperability: C# is interoperable with other programming languages, such as C++, Java, and Visual Basic.
4. Type Safe: C# type safe code can only access the memory location that it has permission to execute. Therefore it improves a security of the program.
5. Rich Library: C# provides a lot of inbuilt functions that makes the development fast.
6. Fast Speed: The compilation and execution time of C# language is fast.
7. Type-Safe: C# enforces strong type checking, which helps prevent type-related errors during the development process.
8. Asynchronous programming: C# supports asynchronous programming, which allows you to write code that runs in the background while the main thread of execution continues.
9. Scalable and Updateable: C# is a programming language that allows for easy updates and scalability. When we want to update our application, we replace the old files with the new ones to ensure the latest version is running. This simplicity makes it convenient to keep our software up-to-date with the latest features and improvements
10. Structured Programming Language: C# is a structured programming language in the sense that we can break the program into parts using functions. So, it is easy to understand and modify.

What is the workflow of C# ?

1. Writing Code: Start by writing C# code using a text editor or an integrated development environment (IDE) like Visual Studio.
2. Compilation: Once the code is written, it needs to be compiled into an intermediate language called Common Intermediate Language (CIL) or Microsoft Intermediate Language (MSIL). This compilation is done by the C# compiler (csc.exe).
3. Intermediate Language and JIT Compilation: The compiled code is then executed by the Common Language Runtime (CLR), which converts the CIL/MSIL code into native machine code through Just-In-Time (JIT) compilation.
4. Execution: The JIT-compiled native code is executed on the target machine.

How C# Works & Architecture?

1. Source Code (.cs files): C# code is written in text files with a .cs extension.
2. C# Compiler: The C# compiler (csc.exe) compiles the C# source code into intermediate language code (CIL/MSIL).
3. Common Language Infrastructure (CLI): CIL/MSIL code is platform-independent and runs on any system with a CLI implementation.
4. Common Language Runtime (CLR): The CLR is responsible for loading and executing the CIL/MSIL code. It provides services like memory management (garbage collection), security, and exception handling.
5. JIT Compilation: When the CIL/MSIL code is executed, the CLR uses Just-In-Time (JIT) compilation to convert it into native machine code, specific to the target platform, for better performance.

How to Install and Configure C# ?

To install and configure C#, you will need to:

1. Download and install the .NET Framework. This is the foundation on which C# is built, and it is required to run C# applications. You can download the .NET Framework from the Microsoft website: https://dotnet.microsoft.com/en-us/download/dotnet-framework.
2. Install a C# IDE. There are many different C# IDEs available, such as Visual Studio, Visual Studio Code, and JetBrains Rider. Each IDE has its own strengths and weaknesses, so you may want to try a few different ones before you decide which one you like best.
3. Configure your IDE. Once you have installed an IDE, you will need to configure it for C# development. This typically involves setting the project type to “C#” and selecting the appropriate .NET Framework version.

How to Download and Install Visual Studio for C# in Windows

Step 1) Download Visual Studio

First, visit the following Visual Studio free download link 

https://visualstudio.microsoft.com/downloads/

You can select

• Visual Studio 2022 Community Edition
• Visual Studio 2022 Professional Edition (30 Day Free Trial)

Step 2) Open the .exe file

Click on the free downloaded option

Step 3) Start the installation

In the next screen, click continue to start Visual Studio installation.

Step 4) Let the installation complete

Visual Studio will start downloading the initial files. Download speed will vary as per your internet connection.

Step 5) Choose the software version

In next screen, click install

Step 6) Select the desktop version

In next screen,

1. Select “.Net desktop development”
2. Click install

Step 7) Wait for the files to be downloaded

Visual Studio will download the relevant files based on the selection in step 6

Step 8) Reboot your PC

Once the download is done, you will be asked to reboot the PC to complete Visual Studio setup

Step 9) Open Visual Studio

Post reboot, open the Visual Studio IDE

1. Select a theme of your choice
2. Click Start Visual Studio

Step 10) Start using Visual Studio

In Visual Studio IDE, you can navigate to File menu to create new C# applications.

That’s it to Visual Studio for C# installation.

Fundamental Tutorials of C# ?

Here are the step-by-step instructions on how to write a “Hello, World!” program in C#:

1. Open a text editor and create a new file.
2. Save the file with the .cs extension.
3. Write the following code in the file:

C#

// Hello, World! program

using System;

namespace HelloWorld
{
    class Program
    {
        static void Main(string[] args)
        {
            Console.WriteLine("Hello, World!");
        }
    }
}

4. Save the file and compile it.
5. Run the program.

The program should print the following output to the console:

Hello, World!

Explanations of these code

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What is TypeScript?

TypeScript is an open-source, typed superset of the JavaScript programming language. The typeScript was created by Microsoft in 2012 to make it easier for developers to build large, scalable applications. It is widely used by companies like Slack, Asana, and Microsoft.

These are the main differences between JavaScript and TypeScript:

• TypeScript offers static typing, whereas JavaScript offers dynamic typing.
• TypeScript uses types and interfaces.
• TypeScript supports optional parameter functions, whereas JavaScript does not.

What is TypeScript Used For?

TypeScript is a modern web development framework. It extends JavaScript with static type definitions. These definitions make it easier to describe how an object is structured, which increases the readability of the code.

This is because TypeScript only extends over JavaScript. It does not replace the framework. TypeScript code is converted to JavaScript code when it is compiled.

There is no need for a developer to convert their entire application to support TypeScript. At any time, TypeScript can be added to a client- or server-side application.

Because TypeScript is based on JavaScript, any transition will involve adding TypeScript code, rather than removing JavaScript code.

Features of TypeScript:

How Long Does it Take to Learn TypeScript?

Assuming that you study at least an hour a day, it takes about a month to learn the basics of TypeScript. Expect to spend at least six months studying TypeScript before developing the skills needed to apply it in a professional development setting.

The time it takes you to learn TypeScript depends on the amount of time you devote to study. If you study full time, you will be able to learn TypeScript faster than someone who is only studying part-time

Is TypeScript Hard to Learn?.

Learning TypeScript is a little more difficult than learning Javascript. This is because TypeScript extends on JavaScript and therefore you need to have a good understanding of how JavaScript works first. But, with some practice and time, you shouldn’t have any trouble learning TypeScript.

How to Learn TypeScript?

So, you have decided to learn TypeScript. This is a great option. But, how can you learn TypeScript fast?

• Learn HTML, CSS, and JavaScript – Because TypeScript is built on top of JavaScript, you’ll need to learn how to code in HTML, CSS, and JavaScript before getting started.
• Types – This is an essential component of TypeScript, hence the name of the language. In TypeScript, types can be assigned to variables when they are declared. It allows you to write more reliable and scalable code, and also enhances the readability of your programs.
• Variables and Commenting – Since TypeScript and JavaScript variables and comments work differently, you should explore these two topics in depth.
• Functions – TypeScript provides a number of enhancements to traditional JavaScript functions. In TypeScript, you can declare a function like you would in JavaScript. But, you can also use TypeScript types as function arguments.
• Object-Oriented Programming – TypeScript is an object-oriented programming language and adds a variety of new features to improve JavaScript’s object-oriented features. For example, you can create classes using the “class” keyword in TypeScript.

Prerequisites for Learning TypeScript:

Working knowledge of JavaScript, object-oriented programming, and the command lines is assumed. The only prerequisites are a code editor and npm, the Node.js package manager.

• You should have a good understanding of OOP concepts.
• Basic JavaScript
• Basic knowledge of ES2015 JavaScript

How to Learn TypeScript Online?

The first thing you should do before learning TypeScript is to ask yourself how you learn best.

Do you prefer online tutorials, or do you prefer online courses? Do you want to work on projects as part of a team, or are you comfortable learning on your own? By considering this question in advance, you will be able to better understand what kind of material you should be looking for.

If you are planning to learn TypeScript, go with DevOpsSchool institute. Will provide you online and classroom training and certification course Program by an expert. To get in-depth knowledge of TypeScript along with its various applications, check out our interactive, live-online training.

Here you can see the TypeScript Course Agenda:

This course covers the basics of TypeScript and its main features. You will develop a critical understanding of why TypeScript is useful and how it can be combined with other web development frameworks. You will learn about the basics of TypeScript and its advantages. Then, you’ll cover more advanced topics like integrating TypeScript with other frameworks.

Because TypeScript is so popular, there is an incredibly active community of developers who know how to code using TypeScript.

When you start learning TypeScript, you should try to join one or two developer communities. These communities are places where developers talk about their learning and answer each other’s questions.

Hopefully, I think this information is helpful for you.

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What makes Python a top choice in the Data Science community?

Python has become the most used programming language for data science practices. Developed by Guido van Rossum and launched in 1991, it is an interactive and object-oriented programming language similar to PERL or Ruby. Its inherent readability, simplicity, clean visual layout, less syntactic exceptions, greater string manipulation, ideal scripting, and rapid application, an apt fit for many platforms, make it so popular among data scientists. This programming language has a plethora of libraries (e.g., TensorFlow, Scipy, and Numpy); hence Python becomes easier to perform multiple additional tasks.

Python is an object-oriented, open-source, flexible, and easy to learn programming language.  According to a 2013 survey by industry analyst O’Reilly, 40% of data scientist respondents admitted using Python in their daily work. They join the many other programmers in all fields who have made Python one of the world’s top ten most popular programming languages ever since 2003. In fact, many surveys show it as the number one preferred language.

Why is it so Popular?

One of the main reasons why Python is widely used in the scientific and research communities is its ease of use and simple syntax that makes it easy to adapt for people without much programming or engineering background. It is also suitable for quick prototyping. Further, it allows the developer to run the code anywhere, like Windows, Mac OS X, UNIX, and Linux. And since it is a flexible programming language, it offers data scientists the facility to solve any given problem or carry projects concerning about developing machine learning models, web services, data mining, classification, etc., in less time frame than most of the programming languages. Python libraries Python Scrapy and BeautifulSoup can help to extract data from the internet, whereas Python Seaborn and Matplotlib help in data visualization or graphical representation. In data analytics helps with better insight, understanding patterns and correlates data from big datasets. Its libraries like Tensorflow, Keras, and Theano allow data scientists to develop deep learning models and Scikit-Learn helps to develop machine learning algorithms. It can also be leveraged in non-technical fields like business and advertising.

Besides, Python has a huge community base of engineers and data scientists like Python.org, Fullstackpython.com, realpython.com, etc., where Python developers can impart their issues and thoughts to the community at no cost. Also, Python has great compatibility with Hadoop, which is a renowned open-source big data platform.

Microsoft’s New Update

Microsoft has been a constant advocate of Python. It supports open-source Python in developer tools, including the Visual Studio integrated development environment (IDE), and hosts it in Azure Notebooks and uses it to build end-user experiences like the Azure command-line interface (CLI). Recently, Microsoft released a new update of its Visual Studio Code (VS Code) code editor for Windows, Windows on Arm, macOS, and Linux. In this, it launched a new version of the Python language extension for VS code editor that breaks out the Jupyter Notebooks functionality into a distinct VS Code extension. Jupyter is a free, open-source, interactive web tool, which researchers use to combine software code, computational output, explanatory text, and multimedia resources in a single document.  It draws its name from the programming languages Julia (Ju), Python (Py), and R. This means, it not only supports Python but also other popular data science languages like Julia and R.

Although Microsoft’s Python extension for VS Code has supported Jupyter Notebooks for a year now, the tech giant decided to break out Jupyter notebooks functionality to improve support for other data-science languages.

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Python, the general-purpose coding language has gained much popularity over the years. Speaking of web development, app designing, scientific computing or machine learning, Python has it all. Due to this favourability of Python in the market, python developers are also in high demand. They are required to be competent and out of the box thinkers- undoubtedly a race to win.

Are you one of those python developers? Do you find yourself lagging behind in proving your reliability? Maybe you are going wrong with some of your skills. Never mind!

I’m here to tell you of those 8 trendsetting skills you need to hone. Implement them and prove your expertise in the programming world. Come, let’s take a look!

1.   Knowing Python Libraries

Being able to use the Python Library in its full potential also decides your expertise with this programming language. Python libraries like Panda, Matplotlib, Requests, Pyglet and more consist of reusable codes that you’d wish to add to your programs. These libraries are boon to you as a developer. They will increase workflow and make task execution way easier. Nothing saves more time from having to write the whole code every time.

2.  Knowing Python Frameworks

You might know how Python omits repeated code by using pre-developed frameworks. As a developer using a Python framework, you typically write code which conforms to some kind of conventions. Because of which it becomes easy to delegate responsibilities for the communications, infrastructure and low-level stuff to the framework. You can, therefore, concentrate on the logic of the application in your own code. If you have a good knack of these Python frameworks it can be a blessing, as it allows smooth flow of development. You may not know them all, but it’s advisable to keep up with some popular ones like Flask, Django and CherryPy.

Not sure of Python frameworks? You can seek help from Python Training Courses.

3.  Object Relational Mappers

Object-relational mapping (ORM) is a programming method used to access a database. It exposes your database into a series of objects without writing commands to insert or retrieve data. It may sound complex, but can save you a lot of time, and help to control access to your database. ORM tools can also be customised by a Python developer.

4.  Knowledge of Front End Technologies

Front end technologies like the HTML5, CSS3, and JavaScript will help you collaborate and work with a team of designers, marketers and other developers. Again, this can save a lot of development time.

5.  Analytical Skills

A good Python developer should have sharp analytical skills. You are expected to observe and critically come up with complex ideas, solutions or decisions about coding. Talking of the analytical skills in Python you need to have:

• A  complete idea of algorithms to write accurate codes.
• Visualize datasets, design attractive web sites for web development.
• Optimize algorithms

Analytical skills are a mark of your additional knowledge in the field. Building your analytical skills also make you a better problem solver.

6.  Data Science Foundation

Python Developers have a bright future in Data Science. Companies on the run will prefer developers with Data Science knowledge to create innovative tech solutions. Knowing Python will also gain your knowledge of probability, statistics, data wrangling and SQL. All of these are significant aspects of Data Science.

7.  Machine Learning and AI skill

Python is the right choice to grow in the Artificial Intelligence and Machine learning domain. It is an intuitive and minimalistic language with a full-featured library line (also called frameworks) which considerably reduces the time required to get your first results.

However, to master artificial intelligenceand  machine learning with Python you need to have a strong command over Python syntax. A fair grounding with calculus, data science and statistics can make you a pro. If you are a beginner, you can gain expertise in these areas by brushing up your math skills for Python Mathematical Libraries. Gradually, you can acquire your adequate Machine Learning skills by building simple neutral networks.

8.  Deep Learning

In the coming years, deep learning professionals will be well-positioned as there is a huge possibility awaiting in this field. With Python, you should be able to easily develop and evaluate deep learning models. Since deep learning is the advanced model of Machine Learning, to be able to bring it into complete functionality you should first get hands-on:  

• Artificial Neural Network Architecture
• Natural Language Processing (NLP)
• Audio and Video Processing
• A good python developer is also a mixture of several soft skills like proactivity, communication and time management. Most of all, a career as a Python Developer is challenging, but at the same time interesting. Empowering yourself with these skill sets is sure to take you a long way. Push yourself from the comfort zone and work hard from today!

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AI programming is an elevation of technology that has brought efficiency and optimum benefits to different company’s operations and people’s lives. AI has brought another level of smart technology to different industries and the prospects of its potential still grow with the expectation that it would reach human intelligence. This is because developers are willing to explore, experiment, and implement their capabilities to satisfy more of the human and organization necessities. After all, necessity is the mother of invention.

Therefore, here is the list of top 5 programming languages that are in trend for AI developments in 2020.

Python

Python is an Interpreted language which in lay man’s terms means that it does not need to be compiled into machine language instruction before execution and can be used by the developer directly to run the program. This makes it comprehensive enough for the language to be interpreted by an emulator or a virtual machine on top of the native machine language which is what the hardware understands.

Python offers the least code among others and is in fact 1/5 the number compared to other OOP languages. No wonder it is one of the most popular in the market today.

Python has Prebuilt Libraries like Numpy for scientific computation, Scipy for advanced computing, and Pybrain for machine learning (Python Machine Learning) making it one of the best languages For AI.

R

R is a multi-paradigm language that can be called a procedural one, much like Python is. It can also support object-oriented programming, but it is not known for that feature as much as Python is.

R is considered to be a statistical workhorse, more so than Python. Once you start learning, you will understand that statistics form the base of machine learning and AI too. This means that you will need something which can suit your needs, and R is just that. R is considered to be similar to SAS and SPSS, which are other common statistical software. It is well suited for data analysis, visualization, and statistics in general. However, it is less flexible compared to Python but is more specialized too.

R is an open-source language too. This does not simply mean that it is free to use, for you – it also implies that you will have a lot of support when you start to use it. R has a vast community of users, so there is no dearth of help from expert practitioners if you ever need any.

Java

One of the best things about Java is Java Virtual Machine Technology. This technology allows developers to build a single app version that will run on all Java-enabled computing platforms. Major strengths of this programming language are as following: maintainability; portability; transparency.

AI is closely connected with search algorithms, genetic programming, and the use of artificial neural networks. Java in the artificial intelligence sphere may be more than useful. Programming AI in Java has many benefits: easy use, debugging ease, simplified work with large-scale projects, facilitated visualization, better user interaction. Another reason for programming AI in Java is the incorporation of Swing and SWT (the Standard Widget Toolkit). These features make graphics and interfaces look appealing and sophisticated.

Another reason for using Java in AI programming is the vast amount of tutorials on the Internet. Just type “how to program artificial intelligence in Java” and you’ll get a lot of pages to choose from. Java is versatile. It’s used for making multi-robot systems, sensor networks, and machine learning suites.

Scala

Just like Java, Scala belongs to the JVM family. Scala is a fairly new language in the AI space but it’s finding quite a bit of recognition recently in many corporations and startups.

It has a lot to offer in terms of convenience which is why developers enjoy working with it. Also, ScalaNLP, DeepLearning4j, etc are all tools and libraries that make the AI development process a bit easier with Scala. It’s good for projects that need scalability and combines the strengths of Functional and Imperative programming models to act as a powerful tool that helps build highly concurrent applications while reaping the benefits of an OO approach at the same time.

Scala provides good concurrency support which helps with projects involving real-time parallelized analytics. It has a good open source community when it comes to statistical learning, information theory, and Artificial Intelligence in general.

Rust

The most loved language of all is Rust, an open-source programming language that was hatched by tech pioneers at Mozilla in 2010.

In fact, Rust has been voted the most-loved language for the past four years in Stack Overflow’s annual developer surveys as solves pain points present in many other languages, providing a solid step forward with a limited number of downsides.

Rust concepts are also being used in Microsoft’s recently open-sourced Project Verona, an experimental language for safe infrastructure programming that could help Microsoft securely retain legacy C and C# code.
Mozilla Research describes Rust as a “systems programming language that focuses on speed, memory safety, and parallelism”.

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One of the most crucial decisions when writing your own test automation scripts will be your choice of programming language. Selection of a test automation language depends largely on the language an application was developed with, but there are other factors to consider.

Writing test scripts in the same language that the app was written in is typically the best path because it enables your organization’s developers to easily read and write test code. Plus, the test code can live in the same repository as the application’s source code. Every situation is different, though, and you may not be able to use the same code as your application. In some scenarios, the application may already be written and won’t be updated, so it’s irrelevant if developers need to read your test code.

There could be other reasons to write automated tests in a language different from the application. Maybe there is a much better framework for your application that’s supported only in JavaScript. Or it’s possible your testers know only Python. Let these three concepts guide your choice of test script language.

Evaluate the most common test automation languages

Java, JavaScript and Python are popular test automation frameworks. Ruby and C# are also widely used, though typically only for applications written in those languages.

Selenium WebDriver is implemented in each of these languages. It offers a base framework to interact with a browser, but for this discussion we’ll compare programming languages with feature-rich frameworks.

Java is an appealing option, mostly because of the popularity of test automation frameworks already written with the language. Java has been around since the 1990s, and it is often the first language a programmer learns. Many prebuilt test frameworks already exist in Java. Commonly used Java test automation frameworks include Selenide, JBehave and Serenity.

Python is an increasingly popular choice, resulting from the trend in software programming for code that’s more readable and that has fewer boilerplate lines.

Keep test scripts simple

To write simple test scripts that don’t involve complex concepts, such as access modifiers in Java (public, private, protected and so on), Python might be a good choice. Python treats all class objects as public, and it reduces the amount of code you’ll need to write. While Python is an object-oriented programming language, it is often referred to as a scripting language because of how easy it is to use in such applications.

Python is excellent for writing lightweight test scripts. Robot Framework and JBehave are good options for fully built frameworks written in Python.

JavaScript is becoming a popular choice for test automation, mirroring the explosion of JavaScript web frameworks. In 2019, JavaScript was used in more GitHub repositories than any other language.

The use of JavaScript may not help with simplicity, however. In fact, JavaScript can increase the complexity of test code, as the language implements complex features such as asynchronous execution, promises, callbacks and anonymous functions. Some frameworks, such as Nightwatch.js or Cypress, abstract away these complexities. Others, such as native Selenium WebDriver in JavaScript, can be difficult for beginners.

JavaScript frameworks often include many handlers and wrappers for respective front-end JavaScript frameworks, so it’s a good idea to use these frameworks to handle the testing of a front-end application with lots of JavaScript.

An interpreted language, Ruby is similar to Python. It offers many powerful, built-in methods for objects. Conveniently, everything is an object in Ruby. Ruby yields lightweight tests, and it’s easy to read. Watir, Konacha and Capybara are popular test automation frameworks in Ruby.

Some test automation frameworks are available in C#, a language created by Microsoft as a competitor to Java. For those working with other Microsoft frameworks, such as .NET, it makes sense to develop automated tests with C# and NUnit, which is the C# version of JUnit.

See which test automation language fits

Before committing to one language, run some experiments.

Take your most important automated test, and implement it with each language that you’d like to compare. You may find that one language or framework fits better than the others, allowing you to write cleaner, more maintainable tests or provide better reporting to project stakeholders. A testing project’s success depends on the tools used, so take care to make the right choice.

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Picking the right programming language can overwhelm some developers, given the sheer number of factors to consider. Python and Ruby both sit high on developers’ lists, mainly because of their ease of use, levels of power and dynamic flexibility.

However, these languages do not behave the same way, and there are a few key elements developers should consider before they choose one. Learn the backgrounds of Python and Ruby, then examine the factors — startup speed, mobile-friendliness and more — that determine which language is right for your project.

Design of Python and Ruby

Python is an object oriented programming (OOP) language that has elegant, human-readable syntax and sophisticated development features. It offers beginner-friendly tools and components, and is advanced enough for mature developers.

Python emphasizes readable code, simplicity of the underlying implementation and a single, standardized library. Programmers gravitate toward Python because of its versatility, extensive libraries and community support. Developers can deploy various frameworks, including Django, Pyramid and TurboGears. In addition to a mature and well-documented library, the Python Package Index makes thousands of supplemental libraries available. Python runs on every major OS and is used in every domain, from systems operations and web development to scientific modeling and computational analytics.

Released in 1991, Python was designed to enable application builds through a command-line interface or cross-platform GUI-based approach. At the time of publication, Python supports two versions: Python 2.7 and Python 3. The user base continues to gain supporters and Python users outnumbered Java users in 2019, according to Stack Overflow.

Ruby is also an OOP, versatile and general-purpose language suitable for small, ad hoc scripting tasks or advanced front-end and back-end application development. Similar to Python, it offers capabilities that are simple enough for beginners to learn and powerful enough to meet the demands of experienced programmers.

Ruby promotes an implicit coding approach. It is highly portable and runs on all platforms, including Unix, MacOS and Windows. For example, the Windows Ruby installation includes the Win32 API library, which enables access to all Windows dynamic link library calls. Ruby also embodies key concepts and features from languages like Perl, Smalltalk and Lisp.

Publicly released in 1995, Ruby employs the popular Ruby on Rails framework, as well as Merb, Nitro, Hanami and the domain-specific language Sinatra. Ruby on Rails is geared toward rapid prototyping, which gives users efficiencies for building both front-end and server-side elements. The framework offers support scripts, database back ends and libraries. Developers can also build applications and access libraries that include XML parsers, GUI bindings, networking protocols and game components.

The difference between Python and Ruby

In general, both languages are similar in the programming tools and features they provide. However, here are some significant differences.

A key aspect of Python is its use as glue code for server-side scripting to ensure communication between front-end and back-end components. As a scripting and automation language, Python also employs constructs that control the order and flow of task execution. Moreover, through code repeatability and automation, programmers can simplify their build processes.

Python also provides clean syntax and script automation. It’s an interpretive language, but keep in mind that interpreter overhead can create slow compile times and lags in system calls and kernel requests.

Python is not particularly effective for writing mobile applications, but tools and libraries are continually delivered to augment these capabilities.

Ruby also functions as glue code and provides developers with custom shell commands, application wrappers and rapid application prototyping. Its object-oriented approach consists of actions, known as methods, and simple naming conventions that denote the scope of certain variables. Developers can give every bit of Ruby code specific properties and actions to perform.

In terms of memory, garbage collection in Ruby eliminates the need to maintain reference counts in extension libraries. This helps to curb high memory utilization and memory fragmentation caused by multithreading processes. Also, Ruby’s extension handling features simplify the process to catch and cut errors in code.

On the other hand, the startup speed of Ruby is uniformly slow, which can negatively impact performance. And while the Ruby community is generally supportive, written documentation is lacking, which can be problematic for developers who typically rely on test suites and physical code reviews to understand program behavior.

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TLDR: Python is on the rise — and with the training in The Absolute Python Programming Certification Bundle, you’ll learn why it continues its unprecedented success with coding pros.

For nearly a decade, the pinnacle of the programming language pecking order has been deeply entrenched. Decades-old king JavaScript remained firmly at no. 1, while runner-up Java sat as a solid no. 2. That hierarchy has remained unchallenged as long as analysts at Red Monk have been ranking the world’s most used coding disciplines, beginning in 2012.

Until now. This year, Python, who spent four years in 4th place before moving up to 3rd in 2017, shockingly tied JavaScript to share 2nd place on the 2020 list. While that may not sound like a big deal, it’s at least another piece of confirmation that the user-friendly, highly-adaptable Python language continues to impact every aspect of modern-day coding.

And with those kind of credentials, this is definitely time for the uninitiated to get up to speed on everything Python can do. You’ll get a front-and-center crash course in all those talents with the training found in The Absolute Python Programming Certification Bundle. The collection is now hundreds off its original price, only $18 from TNW Deals with promo code “FLASHSAVE40” at checkout. 

Over these three courses, even first timer Python users will get a feel for this highly intuitive, user friendly language and understand why it’s making such groundbreaking waves on the 2020 programming scene.

The education starts with Introduction to Python Training, which delves into the language’s origin, its advantages, its most useful tools and unlock why it’s gained such widespread adoption over the past three decades.

Armed with the basics, Python Data Analysis with NumPy and Pandas goes deeper into some of the Python libraries that super-charge Python’s power. This course explores Python’s central role in today’s data science, including how NumPy, Pandas and Matplotlib are revolutionizing how data is gathered, organized and presented.

The third and final course, Advanced Python Training, gets into some of the expert level applications of Python, including mapping, advanced sorting, expressions, databases, object-oriented coding, and more.

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Recently, Swift 6, the team behind Apple’s popular programming language Swift, announced that it is planning to open up the programming language for machine learning. Swift has already established itself when it comes to executing at the production level with faster speed than most of the languages. 

When one hears Swift, it probably takes them to app development for iOS, and if one is familiar with machine learning, they might have heard about Swift for Tensorflow (S4TF). But, one might wonder, despite the widely used languages like Python or C++ or Java for that matter, why use Swift for Machine Learning?

Swift Over Python

For some time now, Python has been the number one choice for machine learning because of its ease of use and low barrier of entry. However, today, programing languages such as Julia, Rust, among others are getting into the market due to the requirement of high-performance computing in data science. And now Swift is rapidly moving towards machine learning to assist developers in writing production-level code that has data science capabilities.

Since Python is slow, it has opened up the opportunity for other programming languages to tap in the data science landscape. Swift, in general, is around 8.4x times faster than Python. And because the types aren’t checked in Python, the program might encounter a type error at run time and may crash. In contrast, the possibility of that happening with Swift is less because it is a statistically typed language.

Let’s take a look at what features Swift offers

The Speed

When one talks about the fastest among the programming languages, usually one name comes up, and that is C. It is highly optimised and achieves excellent speed. But, it is not easy to learn and also, the speed is a trade-off with C not being memory safe.

Now, one should know, Swift for numerical programming wasn’t explored much, so when it was established that Swift works fast on mathematical computations. Swift is as fast as C and without the memory safety issues, so naturally, it became more popular and is now touted as the next big language for machine learning. Though it is relatively new, Swift has been increasingly integrated into many systems.

The reason behind Swift being powerful is the LLVM compiler, and it has efficient optimisations that will ensure the code runs fast. 

Interoperability

Swift is at an early stage in machine learning; as a result, not a lot of machine learning libraries are available for it, but its interoperability with Python, C and C++ compensates for the lack of libraries. If one wants to implement a specific functionality into Swift, one can simply import it from corresponding C, C++ or Python. One can import Python libraries in Swift, and that does the work.

Swift’s Differentiable Code

For S4TF (Swift for TensorFlow), differentiable code is integral to the product. So, when one trains their machine learning model, and it involves calculus, which has an unreliable rule called the Chain Rule. So what differentiable programming is doing is that it is automatically computing the chain rule in the language, which makes it highly scalable and open to experimenting on new things or build on the existing libraries. It makes the training an open box in terms of giving one the freedom of tinkering with the process smoothly.

Inspecting the Code

Inside Python for TensorFlow library, it consists of C code. So, when one wants to see how some functions are implemented in Python, they won’t be able to see it, because the Python code for that function is implemented in C. In contrast, with Swift, because there is no C implementation, one can see how something is actually implemented.

Outlook

As Swift grows in popularity, it does have some problems to counter, like instability.

A lot of developers have this common problem with Swift that it is unstable. It means that whenever there is a new version of this language, one will have to rewrite the entire project in the latest version. This becomes a hassle with both app development and machine learning. 

And not only that, but Swift battles the lack of talent. Because it is relatively new, people still need to learn and move on from other popular languages like Python.

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Tech education firm O’Reilly has released its annual study of how students used its platform in the past year, and its findings deserve attention from tech decision makers. The report, which combines both usage and search data from O’Reilly’s online learning platform, paints an excellent picture of what’s trending in the tech world. By looking at what those in the trenches of development and engineering are spending time on, leaders can get an idea of what to expect in the year to come.

O’Reilly said it found five trends in its data that tech leaders should be aware of as 2020 unfolds.

1: Python is the most popular programming language

A full 10% of all use of O’Reilly’s online training is spent on Python, making it far and away the most popular programming language—and item—in O’Reilly’s library. 

The reason for Python’s popularity isn’t because of its perfection as a programming language, but rather because of its importance in machine learning (ML), artificial intelligence (AI), and data analysis.

As long as AI, ML, and analytics keep trending up, Python will probably do so as well. Tech leaders in those fields should be sure they have skilled Python coders on hand.

If you’re interested in learning Python, you might also check out the Google IT Automation with Python Professional Certificate on Coursera.

2: Software architecture, infrastructure, and operations are undergoing rapid change

O’Reilly found that the way people are learning about, and searching for, architecture, infrastructure, and operations content has shifted in a big way, and the reason is simple: The cloud. 

“Cloud native design is a new way of thinking about software and architecture,” O’Reilly said in the report. People are now searching for cloud architecture elements like microservices and containers—Kubernetes in particular saw a huge increase in the past two years. 

DevOps is on the decline, and O’Reilly finds that it may be because DevOps isn’t scaling as well as it should in many cases. Along with scaling issues, DevOps may not be producing the full-stack developers that it relies on.

3: Is interest in AI declining?

AI and ML grew at half the rate they did in 2018, which may seem like a loss of interest, but O’Reilly said that’s actually not the case at all. There have been drops in fields related to ML and AI in 2019, specifically data management and data engineering, but where are those learners going?

“The strength of ML/AI might be less evident in data-specific topics than in other topic areas, such as programming languages, where growing Python usage is—to a large degree—being driven by that language’s usefulness for and applicability to ML.”

4: The cloud’s importance remains clear

Growth in cloud learning continued, albeit at a slower rate. That’s not to say the cloud is getting boring: “This slowdown suggests that cloud as a category has achieved such a large share that (mathematically) any additional growth must occur at a slower rate,” the report said.

In 2019, O’Reilly did see changes in the interest in microservices and Kubernetes. 

Current trends in cloud that O’Reilly said this data shows are a loss of distinction between the public and private cloud and the growing importance of microservices as an essential component of cloud-native design.

5: Security, security, security

“If the sustained growth in security usage on O’Reilly is a reliable indicator, it’s possible that security may, finally, be getting the attention it deserves in an increasingly digital world,” the report said.

Security usage grew significantly in 2019, according to O’Reilly. A lot of people have taken the entry-level Security+ certification test, suggesting non-security specialists are cross training or adding security to their knowledge base. 

It’s worth hoping that the growth in security usage is a sign of the growing importance of cybersecurity, but O’Reilly isn’t necessarily taking the sustained increase as a guarantee of change. Instead, it said, this could be an indicator of cyclical security spending after a series of large-profile hacks and breaches. 

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