HTML, CSS, JAVASCRIPT - Deepstash

HTML, CSS, JAVASCRIPT

Android applications may be produced with the Adobe PhoneGap framework, which is powered by Apache Cordova, utilizing HTML, CSS, and JavaScript. The PhoneGap convenient platform permits the use of web programming abilities to create hybrid apps that are displayed through “WebView” but are bundled as an application.

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MORE IDEAS FROM Expand Your Skill Set And Lead To Growth With These Languages For Android Development - The European Business Review

Android applications may be produced with the Adobe PhoneGap framework, which is powered by Apache Cordova, utilizing HTML, CSS, and JavaScript.

The PhoneGap convenient platform permits the use of web programming abilities to create hybrid apps that are displayed through “WebView” but are bundled as an application.

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JAVA

Initially, Java was the official language for Android App Development, and as a result, it is the most commonly used language. Java is used to build many of the apps in the Play Store, and it is also Google’s oldest approved language.

In addition to all of this, Java offers a fantastic online community for help in the event of a disruption.

However, Java is a difficult language for a newcomer to learn since it includes sophisticated issues such as constructors, null port exceptions, parallelism, checked exceptions, and so on.

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Since 2019, Google has designated Kotlin to be the official language for Android app development. Kotlin is a cross-platform programming language that may be seen as a replacement for Java for developing Android apps. In 2017, it was also launched as a supplementary “official” Java language.

Kotlin is compatible with Java and operates on the Java Virtual Machine. The only significant distinction is that Kotlin eliminates unneeded Java features such as null pointer exceptions. It also eliminates the need to terminate each line with a semicolon.

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The Android Native Development Kit (NDK) can be used to develop Android apps using C++. The NDK is used to implement parts of the app in C++ native code, as an app cannot be constructed solely using C++. This aids in the use of C++ code libraries for the program as needed.

While C++ can be beneficial for Android App Development in some circumstances, it is far more complicated to set up and far less versatile. Because of the increasing complexity, it may also result in additional bugs.

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Because C# is so comparable to Java, it is great for developing Android apps. C#, like Java, employs trash collection to reduce the likelihood of buffer overflows. Furthermore, C# has a crisper and clearer syntax than Java, making it easier to code with.

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Even though Android does not enable native Python programming, Python may be used to create Android apps. This may be accomplished using a variety of tools that transform Python programs into Android Packages that can be executed on Android smartphones.

Kivy, an open-source Python library used for constructing mobile apps, is an example of this. It supports Android and fosters speedy app creation. Unfortunately, because Kivy isn’t natively supported, there will be no native benefits for it.

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Because C# is so comparable to Java, it is great for developing Android apps. C#, like Java, employs trash collection to reduce the likelihood of buffer overflows.

Furthermore, C# has a crisper and clearer syntax than Java, making it easier to code with.

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RELATED IDEA

• Scala : Scala was created to address some of Java’s inherent issues. Since then, it has grown in popularity and is widely used globally used by software developers. Scala provides support to both object-oriented and functional programming. It allows concise and compact codes to be written and becomes a more powerful language with elegant syntax.

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  • Python is a general-purpose, object-oriented programming language.
  • It emphasises code readability by using white space.
  • It is easy to learn.
  • It is a favourite of programmers and developers.
  • Python is very well suited for use in machine learning at a large scale.
  • Its suite of specialised deep learning and machine learning libraries includes tools like scikit-learn, Keras and TensorFlow. It enables data scientists to develop sophisticated data models that plug directly into a production system.

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