All Language

Providing impeccable translation since 1984

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All Language has grown over the last 30 years to become Australia’s most trusted translation service. Incorporated in 1984, All Language grew steadily with the promise of producing the highest quality translation with the best technology available. Today, we continu...
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1984
Australia
All Language
Providing impeccable translation since 1984
0.00/5 (0 Reviews)
A common question we receive is why Digital Skynet chose .NET when there are other more popular platforms. As we mentioned, this framework has all of the tools for all stages of software development life cycle. It’s perfect for enterprise software because it easily supports scalability and growth due to object-oriented programming approach. The reliability is another major benefit of .NET. Ever since its release in 2002, it has improved in terms of both usability and security. The later one is guaranteed by many safeguards such as code access security (CAS), and validation and verification. It also has excellent cross-language support across C#, C++, Python, Ruby and many more.
A common question we receive is why Digital Skynet chose .NET when there are other more popular platforms. As we mentioned, this framework has all of the tools for all stages of software development life cycle. It’s perfect for enterprise software because it easily supports scalability and growth due to object-oriented programming approach. The reliability is another major benefit of .NET. Ever since its release in 2002, it has improved in terms of both usability and security. The later one is guaranteed by many safeguards such as code access security (CAS), and validation and verification. It also has excellent cross-language support across C#, C++, Python, Ruby and many more.

A common question we receive is why Digital Skynet chose .NET when there are other more popular platforms. As we mentioned, this framework has all of the tools for all stages of software development life cycle. It’s perfect for enterprise software because it easily supports scalability and growth due to object-oriented programming approach.

The reliability is another major benefit of .NET. Ever since its release in 2002, it has improved in terms of both usability and security. The later one is guaranteed by many safeguards such as code access security (CAS), and validation and verification. It also has excellent cross-language support across C#, C++, Python, Ruby and many more.

Trends in Software Development Technology  The latest trend in software development is the use of cutting-edge technologies such as AI, Blockchain, and AR/VR.  So, here is a summary of the emerging technologies that the software developers cannot ignore.    #1. Artificial Intelligence  Artificial intelligence is gradually becoming a part of both mobile and web applications. AI offers the application capability to ‘learn’ from user interactions.  Machine learning, and deep learning, combined with AI, help the app understand the user behavior and needs, and respond accordingly.  AI is helping businesses segment customers based on their needs and preferences. Thus, AI is becoming a crucial part of app localization. Businesses are leveraging AI in developing personalized advertising campaigns.  AI-empowered chatbots and virtual assistants are replacing humans in real-time customer support functions. It is making customer interactions faster, unbiased, and reliable.    #2. Blockchain  Blockchain makes business transactions secure, speedy, and transparent. With the use of blockchain, the contracts and financial transactions are becoming hassle-free by eliminating the need for third-party intermediaries.  Blockchain is particularly helpful for businesses that are going global. The technology can help cross-country businesses store and retrieve data securely.  Internationally operating businesses can benefit the most from the blockchain. This technology helps them maintain legal compliance rules digitally, and safeguard themselves against statutory and legal hassles of the different countries that they are operating in.  Last but not least, blockchain is empowering businesses with its applications in digital marketing and advertising.    #3. Augmented Reality & Virtual Reality  The terms AR and VR, often used as substitutes, refer to the most sought after technology trends in modern apps. AR & VR are a boon for the growth of industrial sectors such as interior design, architecture, fashion, etc.  As the businesses are increasingly becoming customer-centric, they have realized the need for showcasing their products or services directly at the customers’ location. AR helps users get a feeling of using the product while the product could actually be miles away.  AR creates an overlay of the product such as furniture, jewelry, etc., on the environment of the user. For this, the customer just needs simple equipment like a mobile phone, and an AR enabled-app.  On the contrary, virtual reality is most widely used in the gaming industry. It requires special headsets to enable the user to experience a real-life gaming environment. If you are interested in reading more about the applications of VR, here is an interesting article on VR applications for businesses.    Future Trends in Software Development  The trends in software development keep changing. Emerging technologies replace leading technologies with time.  But, these three technologies, Blockchain, AI, VR, and AR, will continue to top the list of software development trends in 2020.  So, this was about software technology trends! I will post a separate reply for the second part of the question, i.e., the programming language trends.
Trends in Software Development Technology  The latest trend in software development is the use of cutting-edge technologies such as AI, Blockchain, and AR/VR.  So, here is a summary of the emerging technologies that the software developers cannot ignore.    #1. Artificial Intelligence  Artificial intelligence is gradually becoming a part of both mobile and web applications. AI offers the application capability to ‘learn’ from user interactions.  Machine learning, and deep learning, combined with AI, help the app understand the user behavior and needs, and respond accordingly.  AI is helping businesses segment customers based on their needs and preferences. Thus, AI is becoming a crucial part of app localization. Businesses are leveraging AI in developing personalized advertising campaigns.  AI-empowered chatbots and virtual assistants are replacing humans in real-time customer support functions. It is making customer interactions faster, unbiased, and reliable.    #2. Blockchain  Blockchain makes business transactions secure, speedy, and transparent. With the use of blockchain, the contracts and financial transactions are becoming hassle-free by eliminating the need for third-party intermediaries.  Blockchain is particularly helpful for businesses that are going global. The technology can help cross-country businesses store and retrieve data securely.  Internationally operating businesses can benefit the most from the blockchain. This technology helps them maintain legal compliance rules digitally, and safeguard themselves against statutory and legal hassles of the different countries that they are operating in.  Last but not least, blockchain is empowering businesses with its applications in digital marketing and advertising.    #3. Augmented Reality & Virtual Reality  The terms AR and VR, often used as substitutes, refer to the most sought after technology trends in modern apps. AR & VR are a boon for the growth of industrial sectors such as interior design, architecture, fashion, etc.  As the businesses are increasingly becoming customer-centric, they have realized the need for showcasing their products or services directly at the customers’ location. AR helps users get a feeling of using the product while the product could actually be miles away.  AR creates an overlay of the product such as furniture, jewelry, etc., on the environment of the user. For this, the customer just needs simple equipment like a mobile phone, and an AR enabled-app.  On the contrary, virtual reality is most widely used in the gaming industry. It requires special headsets to enable the user to experience a real-life gaming environment. If you are interested in reading more about the applications of VR, here is an interesting article on VR applications for businesses.    Future Trends in Software Development  The trends in software development keep changing. Emerging technologies replace leading technologies with time.  But, these three technologies, Blockchain, AI, VR, and AR, will continue to top the list of software development trends in 2020.  So, this was about software technology trends! I will post a separate reply for the second part of the question, i.e., the programming language trends.

Trends in Software Development Technology 

The latest trend in software development is the use of cutting-edge technologies such as AI, Blockchain, and AR/VR. 

So, here is a summary of the emerging technologies that the software developers cannot ignore. 

 

#1. Artificial Intelligence 

Artificial intelligence is gradually becoming a part of both mobile and web applications. AI offers the application capability to ‘learn’ from user interactions. 

Machine learning, and deep learning, combined with AI, help the app understand the user behavior and needs, and respond accordingly. 

AI is helping businesses segment customers based on their needs and preferences. Thus, AI is becoming a crucial part of app localization. Businesses are leveraging AI in developing personalized advertising campaigns. 

AI-empowered chatbots and virtual assistants are replacing humans in real-time customer support functions. It is making customer interactions faster, unbiased, and reliable. 

 

#2. Blockchain 

Blockchain makes business transactions secure, speedy, and transparent. With the use of blockchain, the contracts and financial transactions are becoming hassle-free by eliminating the need for third-party intermediaries. 

Blockchain is particularly helpful for businesses that are going global. The technology can help cross-country businesses store and retrieve data securely. 

Internationally operating businesses can benefit the most from the blockchain. This technology helps them maintain legal compliance rules digitally, and safeguard themselves against statutory and legal hassles of the different countries that they are operating in. 

Last but not least, blockchain is empowering businesses with its applications in digital marketing and advertising. 

 

#3. Augmented Reality & Virtual Reality 

The terms AR and VR, often used as substitutes, refer to the most sought after technology trends in modern apps. AR & VR are a boon for the growth of industrial sectors such as interior design, architecture, fashion, etc. 

As the businesses are increasingly becoming customer-centric, they have realized the need for showcasing their products or services directly at the customers’ location. AR helps users get a feeling of using the product while the product could actually be miles away. 

AR creates an overlay of the product such as furniture, jewelry, etc., on the environment of the user. For this, the customer just needs simple equipment like a mobile phone, and an AR enabled-app. 

On the contrary, virtual reality is most widely used in the gaming industry. It requires special headsets to enable the user to experience a real-life gaming environment. If you are interested in reading more about the applications of VR, here is an interesting article on VR applications for businesses

 

Future Trends in Software Development 

The trends in software development keep changing. Emerging technologies replace leading technologies with time. 

But, these three technologies, Blockchain, AI, VR, and AR, will continue to top the list of software development trends in 2020. 

So, this was about software technology trends! I will post a separate reply for the second part of the question, i.e., the programming language trends.

Being a software developer, these are the languages I consider to be the best for development: 1. Python: Python is quite easy to learn, it provides a broad base of support libraries, helps to produce optimized code, prototyping in Python is quick, moreover, it can be treated in a procedural way, and an object- orientated way or a functional way. 2. Java: Java is an OOP (Object Oriented Programming) language. The strong presence of java in development is because of its "write once, run anywhere” philosophy, that means one can write Java software on any device, compile it into low-level machine code, and then execute it on any platform that’s equipped with a JVM (Java Virtual Machine). This means Java is highly cross-platform compatible. Java is also the basis for the Android operating system. 3. JavaScript: JavaScript is a powerful and flexible programming language, used for both front-end and back-end of web development. It provides interactivity of web pages. It provides various frameworks and libraries that enable one to create complex applications with low overhead. It helps one to build multi-functional website. 4. C++: C++ is a high level OOP language. It is portable and is often used for multi-device, multi-platform app development. Also it gives user control over memory management practices. It is mostly used for Virtual Reality, gaming, computer graphics developments. 5. Swift: Swift is a user-friendly, intuitive, general-purpose programming language developed by Apple Inc for macOS, iOS, watchOS, and tvOS and beyond. It enables easy release of applications. Other languages like C,R, SQL, PHP, Scala, Ruby, Go etc. are also used extensively in software development.
Being a software developer, these are the languages I consider to be the best for development: 1. Python: Python is quite easy to learn, it provides a broad base of support libraries, helps to produce optimized code, prototyping in Python is quick, moreover, it can be treated in a procedural way, and an object- orientated way or a functional way. 2. Java: Java is an OOP (Object Oriented Programming) language. The strong presence of java in development is because of its "write once, run anywhere” philosophy, that means one can write Java software on any device, compile it into low-level machine code, and then execute it on any platform that’s equipped with a JVM (Java Virtual Machine). This means Java is highly cross-platform compatible. Java is also the basis for the Android operating system. 3. JavaScript: JavaScript is a powerful and flexible programming language, used for both front-end and back-end of web development. It provides interactivity of web pages. It provides various frameworks and libraries that enable one to create complex applications with low overhead. It helps one to build multi-functional website. 4. C++: C++ is a high level OOP language. It is portable and is often used for multi-device, multi-platform app development. Also it gives user control over memory management practices. It is mostly used for Virtual Reality, gaming, computer graphics developments. 5. Swift: Swift is a user-friendly, intuitive, general-purpose programming language developed by Apple Inc for macOS, iOS, watchOS, and tvOS and beyond. It enables easy release of applications. Other languages like C,R, SQL, PHP, Scala, Ruby, Go etc. are also used extensively in software development.

Being a software developer, these are the languages I consider to be the best for development:

1. Python:

Python is quite easy to learn, it provides a broad base of support libraries, helps to produce optimized code, prototyping in Python is quick, moreover, it can be treated in a procedural way, and an object- orientated way or a functional way.

2. Java:

Java is an OOP (Object Oriented Programming) language. The strong presence of java in development is because of its "write once, run anywhere” philosophy, that means one can write Java software on any device, compile it into low-level machine code, and then execute it on any platform that’s equipped with a JVM (Java Virtual Machine). This means Java is highly cross-platform compatible. Java is also the basis for the Android operating system.

3. JavaScript:

JavaScript is a powerful and flexible programming language, used for both front-end and back-end of web development. It provides interactivity of web pages. It provides various frameworks and libraries that enable one to create complex applications with low overhead. It helps one to build multi-functional website.

4. C++:

C++ is a high level OOP language. It is portable and is often used for multi-device, multi-platform app development. Also it gives user control over memory management practices. It is mostly used for Virtual Reality, gaming, computer graphics developments.

5. Swift:

Swift is a user-friendly, intuitive, general-purpose programming language developed by Apple Inc for macOS, iOS, watchOS, and tvOS and beyond. It enables easy release of applications.

Other languages like C,R, SQL, PHP, Scala, Ruby, Go etc. are also used extensively in software development.

Artificial Intelligence or AI is no longer a ‘sci-fi thing’ as it is already adopted by various industries. It is a set of technologies that makes machines smarter over time. It basically makes the computers think intelligently, especially when you are working on a new artificial intelligence project that needs a critical branch of engineering to accomplish. Surprising is to see different kinds of languages, tools and methods used for developing classy AI programs and interesting it is to know about them, indeed. So, let’s have a glance over them to know how they contribute to the exceptional AI programs and what their role is to the world of artificial intelligence. 1. Python:Python contains simple syntaxes and easy to implement AI. It does not take much of your development time in comparison to C++, Ruby, or Java. The best thing of using Python is that it supports functional, object-oriented, and procedure-oriented styles of programming. It facilitates plenty of libraries to make our tasks more manageable. 2. Java: One of the right choices for AI development, Java has to do a lot with artificial neural networks, algorithms, and genetic programming. It offers many benefits like easy usage, package services, debugging ease, simplified work with large-scale projects, user interaction, and better graphical representation of data. It also incorporates Swing and SWT (the Standard Widget Toolkit) to make graphics and interfaces look appealing and sophisticated. 3. R: R offers the most effective production to analyze and manipulate data for statistical usages. It lets you create a well-designed and quality-based publication inclusive of formulas and mathematical symbols. Other than its general usage, R language also offers numerous packages like Gmodels, RODBC, Class, and Tm that are basically the part of machine learning. 4. Lisp: Lisp is regarded as the most suitable language for developing AI-based programs like DART, CYC, and Macsyma. It processes the symbolic information effectively and is also known best for the easy dynamic creation of the new objects and excellent prototyping capabilities with automatic garbage collection. The excellent features this language contains make it most useful for the AI programs. It contains the macro system that enables the developers to develop a domain-specific level of abstraction. 5. Prolog: Alongside Lisp, Prolog is another most used language to build AI projects, especially medical-related projects. Automatic backtracking, tree-based data structuring, and efficient pattern-matching are some of the important features of Prolog that makes the AI programming framework extremely powerful and flexible. Conclusion: If you go into details, you will get to know that all languages have different features to create diversified programs. As per my personal opinion, the cost of Lisp-based development is higher and so the demand for Lisp experts is limited in the market. But you cannot ignore Python as well when you are developing the Machine Learning projects, as it is extensively used in the ML sphere. Similarly, Java is better than Python to develop games, and desktop & mobile applications. So, all languages have their own importance and usage depending upon the type of AI project wherein they are going to be used.
Artificial Intelligence or AI is no longer a ‘sci-fi thing’ as it is already adopted by various industries. It is a set of technologies that makes machines smarter over time. It basically makes the computers think intelligently, especially when you are working on a new artificial intelligence project that needs a critical branch of engineering to accomplish. Surprising is to see different kinds of languages, tools and methods used for developing classy AI programs and interesting it is to know about them, indeed. So, let’s have a glance over them to know how they contribute to the exceptional AI programs and what their role is to the world of artificial intelligence. 1. Python:Python contains simple syntaxes and easy to implement AI. It does not take much of your development time in comparison to C++, Ruby, or Java. The best thing of using Python is that it supports functional, object-oriented, and procedure-oriented styles of programming. It facilitates plenty of libraries to make our tasks more manageable. 2. Java: One of the right choices for AI development, Java has to do a lot with artificial neural networks, algorithms, and genetic programming. It offers many benefits like easy usage, package services, debugging ease, simplified work with large-scale projects, user interaction, and better graphical representation of data. It also incorporates Swing and SWT (the Standard Widget Toolkit) to make graphics and interfaces look appealing and sophisticated. 3. R: R offers the most effective production to analyze and manipulate data for statistical usages. It lets you create a well-designed and quality-based publication inclusive of formulas and mathematical symbols. Other than its general usage, R language also offers numerous packages like Gmodels, RODBC, Class, and Tm that are basically the part of machine learning. 4. Lisp: Lisp is regarded as the most suitable language for developing AI-based programs like DART, CYC, and Macsyma. It processes the symbolic information effectively and is also known best for the easy dynamic creation of the new objects and excellent prototyping capabilities with automatic garbage collection. The excellent features this language contains make it most useful for the AI programs. It contains the macro system that enables the developers to develop a domain-specific level of abstraction. 5. Prolog: Alongside Lisp, Prolog is another most used language to build AI projects, especially medical-related projects. Automatic backtracking, tree-based data structuring, and efficient pattern-matching are some of the important features of Prolog that makes the AI programming framework extremely powerful and flexible. Conclusion: If you go into details, you will get to know that all languages have different features to create diversified programs. As per my personal opinion, the cost of Lisp-based development is higher and so the demand for Lisp experts is limited in the market. But you cannot ignore Python as well when you are developing the Machine Learning projects, as it is extensively used in the ML sphere. Similarly, Java is better than Python to develop games, and desktop & mobile applications. So, all languages have their own importance and usage depending upon the type of AI project wherein they are going to be used.

Artificial Intelligence or AI is no longer a ‘sci-fi thing’ as it is already adopted by various industries. It is a set of technologies that makes machines smarter over time. It basically makes the computers think intelligently, especially when you are working on a new artificial intelligence project that needs a critical branch of engineering to accomplish.

Surprising is to see different kinds of languages, tools and methods used for developing classy AI programs and interesting it is to know about them, indeed. So, let’s have a glance over them to know how they contribute to the exceptional AI programs and what their role is to the world of artificial intelligence.

1. Python:Python contains simple syntaxes and easy to implement AI. It does not take much of your development time in comparison to C++, Ruby, or Java. The best thing of using Python is that it supports functional, object-oriented, and procedure-oriented styles of programming. It facilitates plenty of libraries to make our tasks more manageable.

2. Java: One of the right choices for AI development, Java has to do a lot with artificial neural networks, algorithms, and genetic programming. It offers many benefits like easy usage, package services, debugging ease, simplified work with large-scale projects, user interaction, and better graphical representation of data. It also incorporates Swing and SWT (the Standard Widget Toolkit) to make graphics and interfaces look appealing and sophisticated.

3. R: R offers the most effective production to analyze and manipulate data for statistical usages. It lets you create a well-designed and quality-based publication inclusive of formulas and mathematical symbols. Other than its general usage, R language also offers numerous packages like Gmodels, RODBC, Class, and Tm that are basically the part of machine learning.

4. Lisp: Lisp is regarded as the most suitable language for developing AI-based programs like DART, CYC, and Macsyma. It processes the symbolic information effectively and is also known best for the easy dynamic creation of the new objects and excellent prototyping capabilities with automatic garbage collection. The excellent features this language contains make it most useful for the AI programs. It contains the macro system that enables the developers to develop a domain-specific level of abstraction.

5. Prolog: Alongside Lisp, Prolog is another most used language to build AI projects, especially medical-related projects. Automatic backtracking, tree-based data structuring, and efficient pattern-matching are some of the important features of Prolog that makes the AI programming framework extremely powerful and flexible.

Conclusion:

If you go into details, you will get to know that all languages have different features to create diversified programs. As per my personal opinion, the cost of Lisp-based development is higher and so the demand for Lisp experts is limited in the market. But you cannot ignore Python as well when you are developing the Machine Learning projects, as it is extensively used in the ML sphere. Similarly, Java is better than Python to develop games, and desktop & mobile applications.

So, all languages have their own importance and usage depending upon the type of AI project wherein they are going to be used.

Advanced humanoid robots are capable of simulating humans in all respects. Like darwin’s theory of evolution, they achieved this milestone through technological evolution. This was possible because we are getting better in communicating with electronics through high-level programming languages like Python.   A humanoid robot is just one of the instances; the magic of programming Python spans even to Galaxy. NASA uses Python to program its space equipment.   Python is extremely easy to handle. It enables programmers to write fewer lines of code and make it more readable. Even non-programmers can learn the Python language with ease. But what it is there that makes Python the best programming language for Big Data.     ( Image source: MVHS)      Big Data     (Image Source: geekmusthave)   The general misconception of Big data is that it is about the volume/size of data. But Big data is more than the volume or size. It is referred to the large amounts of data which is pouring in from various data sources and has different formats.       Usually, you gather data in these formats.     Unstructured data: Audio, video files etc.   Semi-Structured data: XML, JSON   Structured data: RDBMS    Later, this data is made more meaningful with data cleansing technique and used for various purposes like business process enhancement, customer acquisition, improving user experience, etc. Take the example of Netflix, which uses Big Data analytics to make shows and movie recommendations to its users.    There are few other sectors that uses Big Data involves Banking, Transportation, Health care units, Government Organization, and so on.      Big data is also described with its 5V’s- Volume- huge amount of data, Variety- different formats of data, Value- extract useful information from data, Velocity- accumulating data with speed, and Veracity- analysing uncertainty and inconsistency in data.     ( Image source: edureka)      Reasons why Python is best for Big Data    Python does not need to be compiled as it is an interpreted language . Interpreter actually parses the program code for generating the output.   In Python, variable types are defined automatically.   It supports an advanced library to implement machine learning algorithms. This is an advantage for the science community that deals with the scientific data.       NumPy: You can call this a science-geek library. It supplies an extensive library of high-level mathematical and numerical functions   matplotlib: It is a multi-platform data visualization library and feeds huge amounts of data in an easily digestible visuals   Scikit-learn: Scikit-learn provides a range of supervised and unsupervised learning algorithms.   Pandas: It allows various data manipulation operations such as groupby, join, merge, melt, concatenation as well as data cleaning   Tensorflow : Developed by Google’s team this Machine Learning library is used for research in deep neural networks   PyBrain: It contains algorithms for neural networks.   Scipy:It supports linear algebra, interpolation, FFT, ODE solvers, signal & image processing that is essential for scientific and technical computing        4. Hadoop is a popular open-source big data platform. Its inherent compatibility with Python makes it a preferred language for Big data   5. Scalable applications can be created with python programming. Python also has the ability to integrate itself with web applications very easily.   6. It is more preferable when data-analytics is required.  
Advanced humanoid robots are capable of simulating humans in all respects. Like darwin’s theory of evolution, they achieved this milestone through technological evolution. This was possible because we are getting better in communicating with electronics through high-level programming languages like Python.   A humanoid robot is just one of the instances; the magic of programming Python spans even to Galaxy. NASA uses Python to program its space equipment.   Python is extremely easy to handle. It enables programmers to write fewer lines of code and make it more readable. Even non-programmers can learn the Python language with ease. But what it is there that makes Python the best programming language for Big Data.     ( Image source: MVHS)      Big Data     (Image Source: geekmusthave)   The general misconception of Big data is that it is about the volume/size of data. But Big data is more than the volume or size. It is referred to the large amounts of data which is pouring in from various data sources and has different formats.       Usually, you gather data in these formats.     Unstructured data: Audio, video files etc.   Semi-Structured data: XML, JSON   Structured data: RDBMS    Later, this data is made more meaningful with data cleansing technique and used for various purposes like business process enhancement, customer acquisition, improving user experience, etc. Take the example of Netflix, which uses Big Data analytics to make shows and movie recommendations to its users.    There are few other sectors that uses Big Data involves Banking, Transportation, Health care units, Government Organization, and so on.      Big data is also described with its 5V’s- Volume- huge amount of data, Variety- different formats of data, Value- extract useful information from data, Velocity- accumulating data with speed, and Veracity- analysing uncertainty and inconsistency in data.     ( Image source: edureka)      Reasons why Python is best for Big Data    Python does not need to be compiled as it is an interpreted language . Interpreter actually parses the program code for generating the output.   In Python, variable types are defined automatically.   It supports an advanced library to implement machine learning algorithms. This is an advantage for the science community that deals with the scientific data.       NumPy: You can call this a science-geek library. It supplies an extensive library of high-level mathematical and numerical functions   matplotlib: It is a multi-platform data visualization library and feeds huge amounts of data in an easily digestible visuals   Scikit-learn: Scikit-learn provides a range of supervised and unsupervised learning algorithms.   Pandas: It allows various data manipulation operations such as groupby, join, merge, melt, concatenation as well as data cleaning   Tensorflow : Developed by Google’s team this Machine Learning library is used for research in deep neural networks   PyBrain: It contains algorithms for neural networks.   Scipy:It supports linear algebra, interpolation, FFT, ODE solvers, signal & image processing that is essential for scientific and technical computing        4. Hadoop is a popular open-source big data platform. Its inherent compatibility with Python makes it a preferred language for Big data   5. Scalable applications can be created with python programming. Python also has the ability to integrate itself with web applications very easily.   6. It is more preferable when data-analytics is required.  

Advanced humanoid robots are capable of simulating humans in all respects. Like darwin’s theory of evolution, they achieved this milestone through technological evolution. This was possible because we are getting better in communicating with electronics through high-level programming languages like Python.  

A humanoid robot is just one of the instances; the magic of programming Python spans even to Galaxy. NASA uses Python to program its space equipment.  

Python is extremely easy to handle. It enables programmers to write fewer lines of code and make it more readable. Even non-programmers can learn the Python language with ease. But what it is there that makes Python the best programming language for Big Data.  

 

( Image source: MVHS)  

  

Big Data  

 

(Image Source: geekmusthave)  

The general misconception of Big data is that it is about the volume/size of data. But Big data is more than the volume or size. It is referred to the large amounts of data which is pouring in from various data sources and has different formats.   

  

Usually, you gather data in these formats.    

  • Unstructured data: Audio, video files etc.  
  • Semi-Structured data: XML, JSON  
  • Structured data: RDBMS
       

Later, this data is made more meaningful with data cleansing technique and used for various purposes like business process enhancement, customer acquisition, improving user experience, etc. Take the example of Netflix, which uses Big Data analytics to make shows and movie recommendations to its users.   

There are few other sectors that uses Big Data involves Banking, Transportation, Health care units, Government Organization, and so on.  

  

Big data is also described with its 5V’s- Volume- huge amount of data, Variety- different formats of data, Value- extract useful information from data, Velocity- accumulating data with speed, and Veracity- analysing uncertainty and inconsistency in data.  

 

( Image source: edureka)  

  

Reasons why Python is best for Big Data   

  1. Python does not need to be compiled as it is an interpreted language . Interpreter actually parses the program code for generating the output.
      
  2. In Python, variable types are defined automatically.
      
  3. It supports an advanced library to implement machine learning algorithms. This is an advantage for the science community that deals with the scientific data.      
  • NumPy: You can call this a science-geek library. It supplies an extensive library of high-level mathematical and numerical functions  
  • matplotlib: It is a multi-platform data visualization library and feeds huge amounts of data in an easily digestible visuals  
  • Scikit-learn: Scikit-learn provides a range of supervised and unsupervised learning algorithms.  
  • Pandas: It allows various data manipulation operations such as groupby, join, merge, melt, concatenation as well as data cleaning  
  • Tensorflow : Developed by Google’s team this Machine Learning library is used for research in deep neural networks  
  • PyBrain: It contains algorithms for neural networks.  
  • Scipy:It supports linear algebra, interpolation, FFT, ODE solvers, signal & image processing that is essential for scientific and technical computing    

  

4. Hadoop is a popular open-source big data platform. Its inherent compatibility with Python makes it a preferred language for Big data
  

5. Scalable applications can be created with python programming. Python also has the ability to integrate itself with web applications very easily.
  

6. It is more preferable when data-analytics is required.

  

Python has a rich library suitable for AI and data science- NumPy, SciPy, Matplotlib. Seaborn, Scikit Learn, TensorFlow, Keras  It is object-oriented and easy to program. It can be also used as frontend language  It is also used in soft computing, machine learning, NLP programming and also used as web scripting or in Ethical hacking 
Python has a rich library suitable for AI and data science- NumPy, SciPy, Matplotlib. Seaborn, Scikit Learn, TensorFlow, Keras  It is object-oriented and easy to program. It can be also used as frontend language  It is also used in soft computing, machine learning, NLP programming and also used as web scripting or in Ethical hacking 
  • Python has a rich library suitable for AI and data science- NumPy, SciPy, Matplotlib. Seaborn, Scikit Learn, TensorFlow, Keras 
  • It is object-oriented and easy to program. It can be also used as frontend language 
  • It is also used in soft computing, machine learning, NLP programming and also used as web scripting or in Ethical hacking 
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