Here are the top 5 PHP frameworks that every developer should learn:

1. Laravel

Laravel is a powerful, full-stack PHP framework with a great community and a focus on elegance and simplicity. It is a popular choice for web development projects of all sizes, from small blogs to large enterprise applications. Laravel is known for its expressive syntax, powerful features, and large ecosystem of libraries and packages.

2. Symfony

Symfony is another popular PHP framework with a strong focus on modularity and flexibility. It is the foundation for many other PHP frameworks, including Drupal and Magento. Symfony is a good choice for developers who need a framework that is highly customizable and can be adapted to a wide range of projects.

3. Yii

Yii is a high-performance PHP framework that is known for its speed and scalability. It is a good choice for developers who need to build web applications that can handle a lot of traffic. Yii is also a good choice for developers who are looking for a framework with a large and active community.

4. CodeIgniter

CodeIgniter is a lightweight PHP framework that is easy to learn and use. It is a good choice for developers who are just starting out with PHP or who need to build a simple web application quickly. CodeIgniter is also a good choice for developers who are looking for a framework with a small footprint.

5. Slim

Slim is a micro-framework that is designed to be as small and simple as possible. It is a good choice for developers who need to build small, RESTful APIs or web applications. Slim is also a good choice for developers who are looking for a framework that is easy to learn and use.

These are just a few of the many great PHP frameworks that are available. The best framework for you will depend on your specific needs and preferences.

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Analytics Insight has selected the best laptops for Python programming.

Laptops for Python programming require a better battery life, speed, bigger screen size, powerful hard drive, a good keyboard and a bigger VRAM. Python is a vastly popular programming language and is widely used throughout the world. It’s easy to see why python is among the most used programs of today. It can understand the English language, which allows it to carry out simple commands with minimal fuss and effort. Python is used across a variety of platforms and systems worldwide, as well as with software applications and not every laptop is suitable for that. The following are some of the most suitable laptops for python programming.

MacBook Pro (2020)

The MacBook Pro, offers an all-round powerful processor, tons RAM, and a speed not comparable to most laptops on the market. Coming in at 16-inches, the computer still feels light and portable.This laptop is known for its top-notch display, powerful speakers, and speedy keyboard. This is a great laptop for all kinds of uses, including programming, design, art, and student life. Its CPU is 9th-generation Intel Core i7 – i9 and has AMD Radeon Pro 5300M – Radeon Pro 5500M for graphics along with a RAM of 16GB – 64GB. Its screen is a 16-inch Retina display with a storage of 512GB – 8TB SSD.

HP Spectre x360

The HP Spectre x360 2-in-1 laptop, is a great option for programmers looking for a new laptop. This version comes with 10th generation Intel Core processors and powerful Intel Iris Plus graphics. This model of the HP Spectre line takes the HP computer to the next level, especially in terms of battery life and sleek design. Its CPU is 10th-generation Intel Core i5 – i7 and has Intel Iris Plus for graphics, with a RAM of 8GB – 16GB. Its screen is 13.3 inches FHD, 1920 x 1080 resolution with a storage of 256GB – 2TB SSD.

Dell XPS 15

The Dell XPS 15 9500, is another great Dell product with a quality build. It offers a 10th generation i7 processor for extra speed. The trackpad and keyboard are flouted for its fast trackpad and intuitive navigation. On top of that, this robust laptop can be charged for more than 12 hours of battery life. It has a NVIDIA GTX 1650 Ti for graphics with a screen of Ultrasharp 4K Ultra HD display (3840 x 2160 resolution). It has 6GBs of DDR4 RAM.

Lenovo ThinkPad X1 Extreme

Lenovo’s ThinkPad X1, Extreme is one of the best laptops for programming, especially for those who like Lenovo. One perk of this laptop is that it’s far more configurable than most. It’s a solid laptop with a robust carbon-fiber and aluminum package. It has a CPU of 8th-generation Intel Core with Nvidia GeForce GTX 1050Ti with Max-Q for graphics. It has a RAM of 64GB along with a screen of 15.6” FHD (1920 x 1080 resolution). Its storage is 1TB SSD

Asus Chromebook Flip C436F/C436FA

Chromebooks are known for being a middle ground between a traditional laptop and a lightweight tablet. The Asus Chromebook Flip C436F is the best one out there, combining tons of power, premium build, and a cheaper price. It’s an ideal laptop for remote web developers who are on the move. Its CPU is 10th-generation Intel Core i3 – i5 and has Intel UHD for graphics. Its RAM is 8GB – 16GB along with 14” LED-backlit Full HD (1920 x 1080 resolution) for screen. It’s storage is 512GB, 256GB, or 128GB.

Microsoft Surface Laptop 3

The Microsoft Surface 3, is a great budget laptop for the 13.5-inch size. The powerful processor is capable of compiling complex programming languages. This laptop offers an overall great experience for programmers who like the Microsoft layout. Its CPU is 10th Gen Intel Core i3 along with Intel’s Iris Plus for graphics. Its RAM is 4GB – 16GB and a screen of 12.3” PixelSense Display. Its storage is 128GB – 1TB.

Acer Aspire E15 E5-576G-5762

Acer is a big name within the computing industry and this laptop offers great value for money. It’s ideal for python programming and comes with a reasonable budget too. The CPU is Intel Core i5 and offers speeds of 1.6 GHz. Acer Aspire E15 is able to handle programming software easily and comes with 8GB of RAM and 256GB of SSD memory. It also has a NVIDIA GEFORCE MX150 GPU. Since it has a NVIDIA GEFORCE GPU, it’s a strong contender for being one of the best laptops. Also, it has a backlit keyboard and a 15.6-inch HD display.

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According to a recent study, almost 27% of the advertised jobs require Python as a core skill, up from 18.5% at the beginning of the year.

Python is one of the TIOBE Index programming languages of the year. It has become the go-to language for developers working on data science and machine learning for a few years now. 

What sets Python apart is its intuitive features like libraries, high productivity, ease of learning etc. According to a recent study, almost 27% of the advertised jobs require Python as a core skill — up from 18.5% at the beginning of the year.

Last October, Python released version 3.9. The updates included improvements in Python internals, performance boosts, dictionary union operators, handy new string functions, new type operations, consistent and stable internal APIs and more.

What To Expect From 3.10

Python started work on the upcoming version, Python 3.10 pre-alpha, in May 2020. Python version 3.10 is set to be released on 4 October 2021. 

However, with the pre-alpha, the company has unveiled an early developer preview of Python 3.10.

The current alpha version of 3.10 is 3.10.0a6, which is the sixth of seven planned alpha releases. During the alpha phase, features may be added up until the start of the beta phase and, if necessary, may be modified or deleted until the release candidate phase. 

The significant new features of the Python 3.10 version are mentioned below:

Structural Pattern Matching

The pattern matching feature has been added in the form of a match statement and case statements of patterns with associated actions. Patterns usually consist of sequences, primitive data types mappings, as well as class instances. Pattern matching will help developers extract information from various complex data types, branch on the structure of data, and apply specific actions based on different data forms.

Better Error Messages In The Parser

When parsing code that contains unclosed parentheses or brackets the interpreter now includes the location of the unclosed bracket of parentheses instead of displaying SyntaxError: unexpected EOF while parsing or pointing to some incorrect location. Previous versions of the interpreter reported confusing places like the location of the syntax error but in Python3.10 a more informative error is emitted.

Parameter Specification Variables

Parameter Specification Variables feature supports forwarding the parameter types of one callable over to another callable, making it difficult to annotate function decorators. This feature proposes typing.ParamSpec and typing.Concatenate that help in specifying the type of a callable. 

Precise Line Numbers for Debugging and Other Tools

According to its developers, Python should guarantee that when tracing is turned on, “line” the tracing events are generated for all lines of code executed and only for lines of code that are executed. A side effect of ensuring correct line numbers is that some bytecodes will need to be marked as artificial and to assist such tools, a new co_lines attribute will be added that describes the mapping from bytecode to source.

Deprecate Distutils Module

Distutils is an undocumented and unmaintained collection of utilities for packaging and distributing Python packages, including the compilation of native extension modules. This feature defines a configuration format that describes a Python distribution and provides the tools to convert a directory of source code into a source distribution and binary distribution forms.

In both the version of Python 3.10 and 3.11, distutils will be formally marked as deprecated. Meaning, all the known issues will be closed at this time, and import distutils will raise a deprecation warning. During Python 3.10 and 3.11, uses of distutils within the standard library may change to use alternative APIs. However, in Python 3.12, distutils will no longer be installed by make install or any of the first-party distributions. 

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• If you’re interested in learning to code, Python is a great language to start with.
• This training is designed for beginners and covers all the fundamental techniques and more advanced concepts all in one convenient course bundle.
• You can sign up for the 2021 Premium Python Bootcamp Bundle for only $35 for a limited time.

If you already have a strong interest in technology, why not do something more productive with that passion? A way to stay ahead of the game is by learning the popular programming language Python.

It’s actually considered one of the easier languages to learn, but it never hurts to have some help. Sign up for the 2021 Premium Python Certification Bootcamp Bundle, which is on-sale for a limited time, to learn the ropes.

In this bootcamp that’s packed with 13 courses totaling 41 hours, you’ll receive the ultimate guide to coding with Python. From the beginner stage to an advanced level, becoming a master is the end goal of this training.

Of course, it’ll start off with a course for absolute beginners and gradually move into more complex instruction. Some of the classes are courtesy of Zenva Academy, a world-class training platform featuring in-demand programming courses, alongside experts including Minerva Singh, Dr. Chris Mall, and more.

You’ll get to dabble with web automation, artificial intelligence, and debugging code you made yourself. You’ll even have the ability to create your own Python applications in minutes at the end of your studies.

Who knows what career change, creative project, or whatever else learning Python can provide. Sign up for the 2021 Premium Python Certification Bootcamp Bundle and you can find out. Valued at $2,500, you can sign up for only $34.99. This affordable price tag won’t be around forever, so go for it while you can.

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Software architecture, Java and machine learning are amongst the top programming skills ranked by software engineers as important to their role in 2021, according to new research from Degreed. 

Degreed global user data from February 2020 – February 2021 uncovered the skills ranked as most important in a role, for software engineers, product managers, and business analysts. The data highlights the transferability of skills in certain technology and product management roles both internally and externally. 

Data was analysed from millions of active users in the U.S. UK, LATAM, Germany, France, the Netherlands, India, APAC, and Australia, and New Zealand – in companies that include Capgemini, Visa, Ford, Mastercard, and Unilever.

For software engineers, the top 10 skills they rank as vital to doing their work well are Java, Python, Programming, Software Architecture, Machine Learning, Software Engineering, Artificial Intelligence, Software Testing, SQL, and Linux.

Programming languages are most popular across all software engineering roles regardless of sector. The order of the top 10 changes in different sectors but the skills listed remain the same. In finance, for example, more focus is placed on Java, while in media/telecoms the priority is Python.

The top 10 skills most frequently cited as important by product managers were:

• Product Management
• Product Strategy
• Leadership
• Product Planning
• New Product Development
• Product Lifecycle Management
• Project Management
• Product Marketing
• Strategic Partnerships
• Go-to-market Strategy

Across multiple industries, the top 10 list remains consistent (particularly within finance, manufacturing, and media/telecom).

The top 10 skills ranked by business analysts are:

• Business Analysis
• Microsoft Excel
• Data Analytics/Analysis
• Project Management
• Business Intelligence
• Agile
• Project Planning
• Agile Development
• Reporting
• Software Testing

“What’s compelling here is that the top 10 lists for each role do not change significantly as we look at what skills workers find crucial to their work, no matter their sector,” says David Kuntz, head of data science at Degreed.

“A product manager in finance will broadly require the same skill set as a product manager in manufacturing. This means that the market for talented product managers, software engineers, and business analysts is not limited to an employers own sector or to a single department,” he says. 

“Employers should look further afield when struggling to find the right talent as many of the skills within roles are transferable across industries and business functions,” Kuntz says.

“They can look broadly for candidates, both internally and externally. This is particularly relevant for in-demand roles such as software engineering, where there is a well-known talent shortage.”

Kuntz says it is vital to regularly look at the skills people say that they need to effectively do their work, as this ties into their productivity and quality of work. 

“Especially as organisations look towards recovery post-pandemic,” he says.

The research found more than four in ten workers say that a lack of confidence in their skills means tasks take longer to complete and 22% say that their work is of a lower quality. 

“Spotting skills gaps early will help employers proactively upskill their workers to prevent this,” Kuntz adds.

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Which programming languages do employers want? That’s arguably one of the most vital questions that software developers and engineers need answered.

Coding Dojo, which conducts coding bootcamps, runs an annual breakdown of the most-demanded programming languages, using data from Indeed. Python topped the latest edition of its list, followed by SQL, Java, and R, with Visual Basic rounding out the top five. 

“Over the last few years, businesses have been asking for more and more Python developers,” stated Coding Dojo’s accompanying blog post. “Since the beginning of 2018, the number of jobs has skyrocketed. This is great news for Python programmers, or those looking to start their career. As one of the simplest languages to learn, it makes it much easier to get into software development.”

The posting called out two other languages, Kotlin and TypeScript, as worth keeping an eye on: “Among the other languages we check, these two saw a significant rise. Both of them had thousands more jobs than last year, despite the pandemic.” 

While there’s some debate over whether TypeScript is a discrete language (since technically it’s a superset of JavaScript), it’s undeniable that its popularity has increased substantially in recent years. In the 2020 edition of the Stack Overflow Developer Survey, some 86.1 percent of surveyed developers said that Rust was a language they loved, followed by TypeScript (67.1 percent), Python (66.7 percent), Kotlin (62.9 percent), and Go (62.3 percent). TypeScript’s version 4.0introduces even more features that make it powerful and easy to use. 

Meanwhile, Kotlin benefits from Google naming it a key language for Android development. While some tech pundits have predicted that the language may eventually evolve into a robust Java replacement, such an event may take quite some time, despite all the buzz around Kotlin’s usefulness. For those interested in experimenting with Kotlin (particularly in a mobile context), Google offers a number of courses,  including Kotlin Bootcamp for Programmers, Android Kotlin Fundamentals, and Advanced Android in Kotlin.

According to Burning Glass, which collects and analyzes millions of job postings from across the country, the top programming languages of 2020 (by job postings) included AQL, Java, Python, and JavaScript:

These different lists of top programming languages present technologists with uniformly good news: Some of the most in-demand languages among employers are also some of the most ubiquitous, meaning there are lots of documentation and educational resources out there if you want to learn one (or more!) of them. 

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The Python programming language, which has never been more popular, arguably thanks to the rise of data science and AI projects in the enterprise, officially turns 30 years old tomorrow.

One of the five members of the 2021 Python Steering Council within the Python Software Foundation is Pablo Galindo, a software engineer at Bloomberg, who spoke with VentureBeat about the inherent challenges of enabling a language to grow and evolve without sacrificing backward compatibility.

VentureBeat: How did you first get involved with Python?

Pablo Galindo: I was doing my first year of [my] PhD when I was in Granada. My background is in physics. I used to simulate black holes. The code that normally goes into simulations is compiled in C and C++. Python was a fantastic language to kind of wrap simulation code. I very quickly fell in love with the syntax and the power that it has.

VentureBeat: Has the popularity of Python surprised you?

Galindo: I will say for sure. It surprises me [that] this is still one of the most used languages in the world.

VentureBeat: What are some of the most common use cases for Python within Bloomberg?

Galindo: We actually use a lot of Python. There are 2,000 developers using Python. The use cases are quite broad. We use Python for things like machine learning models or service-oriented architecture. We have also used Python for a lot of internal user experience tools and developer tools. We also use Python for data transformation.

VentureBeat: What’s your current assessment of Python?

Galindo: Python is a very mature language, and it has evolved. It also has a bunch of things that it carries over. Python has some baggage that nowadays feels a bit old, but the community and the ecosystem has to be preserved. It’s similar to how C and C++ are evolving right now. When you make changes to the language, it’s quite dangerous [because you can] break things. That’s what people are scared of the most.

But even though Python is quite old, there are big changes. The Python 3.1 release for this October will include pattern matching, which is one of the biggest syntax changes that Python has seen in a long time. We can learn from other languages. I think we’re happy to say that we are still evolving and adapting. We have a good experience with respecting the importance of backwards compatibility.

VentureBeat: If you could be Python king for a day, what would you change?

Galindo: I would be a horrible King for a day. The first order of business would be to fix all these things that we have acquired over the years in the language. That would require breaking a bunch of things. Obviously, I will not do that, but I think one of the things I really would like to see in the future is for Python to become faster than it is. I think Python still has a lot of potential to become faster. I’m thinking this will be impossible. But one can dream.

VentureBeat: What do you know now about Python today that you wish you knew when you first began using it?

Galindo: I think the most important thing I learned is how many different uses there are for Python. It’s important to listen to all these sorts of users when considering the evolution of the language. It’s quite surprising and quite revealing to consider how changes or improvements will conflict or will interact with other users of the language.

That’s something that when I started I didn’t even consider. It would be good if people could be empathetic to us changing the language when we have to balance these things.

VentureBeat: What’s your best advice to the leaders of organizations that have adopted Python?

Galindo: It’s important to explain in a compelling way to the decision makers of the organization what are the advantages of Python compared to something different. They need to specifically understand how the ecosystem and the languages will transform the organization.

The key here is making developers more proactive. Productivity not only means producing more; it is pretty difficult to compete with the speed to market of something that can iterate as fast as Python.

The second important thing is the happiness of the developer. Writing code for a developer is a way of expressing themselves. A developer can express themselves in a compiled language. It is kind of more difficult, because the interaction cycles are longer and less interactive. In Python, the syntax doesn’t get in the way. You can say what you want very easily, and experiment.

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Master the world’s most popular programming languages with this training bundle. You’ll also learn about Raspberry Pi and artificial intelligence.

It’s no secret that learning how to code is one of the most important things you can do if you want to land a high-paying tech job. Whether you’re primarily interested in creating best-selling apps or pro-level websites, you need to be well versed in the programming languages and platforms that are used by today’s most competitive developers.

But if you want to stand out from the crowd and get a great tech job, you need to know more than just one or two programming languages. The Premium Learn to Code 2021 Certification Bundle will get you up to speed with the world’s most popular and powerful programming languages and scripts so you can have a successful career as a developer. The entire training bundle is available for over 95% off at just $59.99 right now.

With 27 courses and 26 hours of instruction led by industry pros, this best-selling bundle is your one-stop resource for mastering Java, MySQL, Swift, and more. There’s also plenty of instruction that walks you through the basics and more advanced elements of Python. In addition, other courses include: How to create apps by utilizing JavaScript’s advanced elements and protocols, integrate comprehensive UIs into your websites using powerful scripting protocols, and work with Ruby to create a wide range of applications for desktop and mobile devices.

Get the skills and tools you need in order to become an in-demand developer this year with help from the Premium Learn to Code 2021 Certification Bundle while it’s available for just $59.99—over 95% off its usual price for a limited time.

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Emerging technologies like AI, data science and machine learning are all about working with intelligent models that need good algorithms to run. For instance, logistic regression or support vector machines.To understand these algorithms and how they work, one must be adept at programming languages.

Here, we discuss 11 crucial programming languages for data scientists.

1| C/C++

C/C++ are usually the first languages one learns when entering the world of programming. These languages give learners insights into the basics of programming and how it works. In machine learning and data science, libraries and frameworks are essential to tackle complex computational tasks. Popular languages like C and C++ have a number of interesting libraries that makes it easy and accessible for data scientists to work on complex computational projects. 

2| Julia

Introduced in 2015, Julia is one of the popular languages ideal for data science, scientific computing, parallel computing, data mining, machine learning, among others. This language uses multiple dispatches as a paradigm and can be used as a general-purpose programming language where you can easily code and write software in the application domains. 

3| Java

Java is one of the oldest languages used for various enterprise development purposes. As one of the oldest languages, it comes with a great number of libraries and tools for ML and data science. Java has a number of libraries and tools — the popular ones being Weka, Java-ML, Deeplearning4j — which are leveraged to solve most of the cutting edge machine learning problems. Also, Java is 25 times faster than Python.

4| JavaScript

JavaScript is a lightweight and interpreted programming language used to create web sites and applications. Using JavaScript in data science and machine learning has several advantages. For instance, the language provides maximum security compared to popular languages like Python. It is a cross-platform programming language to quickly develop and deploy applications in any operating system.

5| Lisp

Programming language like Common Lisp helps in creating flexible computational models. Programs that analyse the sequence data, graph knowledge, and tabular data can be written easily, and can be made to work together naturally in Lisp. The language allows the computer program to examine, introspect, as well as modify its own structure and behavior at runtime, making it ideal for artificial intelligence and machine learning applications. This language is suitable for bioinformatics and computational biology research.

6| MATLAB

Developed by MathWorks, MATLAB is a multi-paradigm programming language and numeric computing environment for complex computations. As data science is all about large swathes of data and numbers, MATLAB is ideal to gain insights from the data and visualise them. MATLAB code can be integrated with other languages, enabling developers to deploy algorithms as well as applications within web, enterprise, and production systems.

7| Python

Python is a general-purpose coding language. The main reason why this language is so popular among the developers is its plethora of libraries and frameworks, which help in performing complex computational tasks. The language has interfaces to many system calls and libraries, as well as to various window systems, and is extensible in C or C++.

8| R

R is a popular statistical language that has gained much traction over the last few years when it comes to processes of data analytics and visualisations. With better data visualisation techniques, this programming language offers an essential role in statistical methods. The language can be used for effective data analysis and gain meaningful insights from it.

9| SQL

SQL or Structured Query Language has become one of the go-to languages for the aspiring data scientists. Most organisations use this language for analysing the tons of raw and unstructured data. The language is used for both data management and data analysis. Due to the speed advantages of SQL, recruiters expect developers to be proficient in it.

10| Scala

Scala or SCAlable LAnguage is a Java-like programming language designed to express common programming patterns in a concise, elegant, and type-safe way. The language is excellent for large-scale projects. It provides a lightweight syntax for defining anonymous functions and supports higher-order functions as well as allows functions to be nested apart from supporting multiple parameter lists. 

11| SAS

SAS, previously known as the Statistical Analysis System is a popular programming language that provides users with a host of product components, including asset performance analytics, analytics for IoT, decision making, and more. One of the best features of this language is allowing data in any format, ranging from SAS tables to Excel worksheets. SAS can also manage and manipulate data to obtain important information. 

The post Top Programming Languages For Data Scientists In 2021 appeared first on Artificial Intelligence.

By Hari Krishnan Nair

In 2020, the sudden global rise of digital-first business solutions led to a spike in demand for programming talent that can build and customise these solutions as per the need of organisations. According to industry estimates, there are close to 85,000 vacant roles for coders across companies. Looking at this scenario, it is the best time for students as well as professionals to gain fluency in key programming languages to build a successful career in 2021.

How to choose the right programming language?

For freshers and graduates, picking the right programming language is a stepping stone to ensure steady growth in their professional life. Each programming language is designed for a specific purpose and has its own merits and demerits. Here are three parameters that are essential in decision-making towards a language of choice.

1. Job opportunities available in the chosen language: Professionals equipped with such skills should be able to command great career opportunities in leading roles like software application engineer, computer system engineers, web developers, data analyst, business intelligence analyst, amongst others, with sizable remunerations.

2. Uptick in popularity of chosen languages: The choice of the programming language should be mainstream and firmly established in the software development industry.

3. Career goals: Gaining fluency in these programming skills becomes important if the learner is looking to carve out a career in digital competencies like data science, analytics, artificial intelligence and machine learning.

Here is the list of top programming languages that will be in demand amongst employers in 2021.

Python: It is one of the most rapidly growing and versatile languages as it contains a plethora of libraries that cater to different roles. It is considered easy to use as is interpreter-based and has high levels of readability. Indeed, a job search portal ranked it as the third-most profitable programming language in the world. The average salary of a Python developer in India with 2-4 years of experience is around Rs 5 lakh.

Java: The existence of Java dates way before Python. It continues to be a popular choice in large organisations like Infosys, TCS, Wipro, IBM, HCL and Cognizant. The language is known to be extremely stable and is extensively used for enterprise-scale web, mobile and desktop application development as well as big data applications. Learners can command popular roles like junior developer, architect, Java web developer, Java Android developer and Java EE developer after equipping themselves with such skills, with a median salary ranging from Rs 3-11 lakh per annum.

R: It is one of the best programming languages to learn as it is a comprehensive statistical analysis language that encourages developers to initiate new ideas. The flexibility of usage allows R to be used on massive datasets at various organisational levels. It offers an excellent framework in the fields of data science and analytics, machine learning, statistical computing and graphics. The average base salary of a data analyst with R skills in India is around Rs 5 lakh per annum. The maximum salary that professionals can earn in this profile is around 9.7 lakh per annum. KPMG, Amazon, EY, Honeywell, Dell and Wells Fargo are some companies that are using this language to code.

PHP: Created in 1990, PHP is an open-source programming language that is considered essential amongst web developers. This language is used to build more than 80% of websites on the internet. Some of the most popular websites like Yahoo and Facebook are built on PHP. The basic average salary of a PHP developer in India can be Rs 6 lakh per year.

Swift: It’s an open-source, easy-to-learn and general-purpose programming language developed by Apple in July 2010. It requires fewer coding skills compared with other programming languages, and can be used with IBM Swift Sandbox and IBM Bluemix. With a few years of experience, Swift developers can earn nearly Rs 11 lakh per annum.

Kotlin: It’s a statically-typed, general-purpose programming language introduced by JetBrains in 2011. It is sponsored by Google and was announced as one of the official languages for Android development in 2017. The language is majorly used in developing Android apps, web applications, desktop applications, and server-side application development. Developers with relevant skills and some experience can command salaries of Rs 8-10 lakh per annum. Almost every company working in mobile app development prefers hiring developers who can write apps in Kotlin for Android.

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