People who went to school in the late 1990s and learned about computers as a new subject remember LOGO and Basic as the simplest languages. Following the Web boom, there was a rise in demand for programmers. A popular choice was HTML. The importance of cyber security increased the demand for programmers to write more computer instructions. Codes in C, C++, Java, PHP were long and complicated. People started looking for simpler and more efficient options.
Things changed in 2020! Python is now the most popular & secure programming language for developing software applications development.
Before we dig deep into Python and its qualities, let's look at what secure coding is.
Coding is a process by which instructions are given to the computer to perform specific tasks. The flaws in a program can allow intruders to access your machine and data and allow them to manipulate your systems and even take control of them.
There is no guarantee that a given language will be the most secure, even if the code writing is easier. Security has also become a critical concern due to open source codes.
Statistically speaking, a language with more users may also have a higher number of vulnerabilities. This is especially true for older versions of the language. Your programming practices determine how secure your code will be.
There's more buzz about cyber security today than ever before. Considering the high-profile data breach of 2020 and the huge IT and cyber skills demand forecast for the next decade, cyber security is a bigger topic today than it ever has been. Back then, few of us would have not even heard of the phrase, let alone understood what it meant.
The popularity of cyber security is unsurprising, considering all the headlines.
As developers, we all have our own preferences when it comes to our favorite coding language. As a matter of fact, there are no such official terms as 'most secure language'. Recent surveys, however, identified several critical security aspects of various programming languages.
A multitude of factors must be considered when analyzing vulnerabilities in any language, such as the Buffer Flow vulnerability, the Common Weakness Enumeration (CWE), the Heartbleed bug, and others. We collected information from various databases, such as security advisories, GitHub issue trackers, and the national vulnerability database. During the survey, we also gathered information from various sources.
There can be several reasons why a programming language is more popular than the rest - involved with the commercially important software, compatible with multiple platforms, supported, and easy to use.
A language becomes more vulnerable the more often it is used. It is better for languages whose continuous support/updates are available for a longer period of time. In most cases, it is not the language that has weaknesses, but the coder who fails to follow security guidelines and fails to patch his programs as needed.
You become better at your job as a result. The ability to develop analytical skills in cyber security helps cyber security experts examine software and detect security vulnerabilities, detect malicious codes, and execute cyber security tasks requiring programming knowledge.
The choice of which programming language to learn, however, is not so straightforward.
If you are concentrating on computer forensics, security for web applications, information security, malware analysis, or application security, you may have to learn a specific language. For cyber security experts, experience with a programming language offers a competitive edge over others, regardless of the language they use.
While it isn't always necessary to have a programming background, it is an asset to have at mid-level and higher levels of cyber security positions. Cyber security experts who have a good understanding of programming languages stay on top of cyber criminals. A good understanding of system architecture makes it easier to defend the system.
Currently, there are more than 250 major computer programming languages in widespread use, with 700 of them being used worldwide. But the number of such languages in the cyber world is much lower.
Python has, however, been the language of choice for cyber security for several years now. This is a server-side scripting language, which means you don't need to compile the resulting script. Typically speaking, it's a general-purpose language that is commonly used in cybersecurity-related situations.
Compared to other programming languages, Python is considered less vulnerable. Small programs are generated using Python by security professionals. For beginners, Python is another popular language. Python is open-source and has many modules. Python has been used to develop many popular open-source programs.
Python provides the ability to automate tasks and perform malware analysis. In addition, an extensive library of third-party scripts is readily available, meaning help is always just a click away. The readability of the code, clear syntax and a large number of libraries are just some of the reasons it is so popular among developers and in the software development industry.
Programming in Python can detect malware, perform penetration testing, perform scanning, and analyze cyber threats, which is why it is a valuable programming language for cyber security experts.
What Should Be My First Step in Learning Cyber Security?
Python is a good place to start. Its syntax is simple, and you can find many libraries that make coding easier.
Cyber security professionals often use Python to analyze malware and scan websites for malicious code. The programming language is a good starting point for more complex languages. The system provides high levels of web readability and is used by tech giants, such as Google, Reddit, and NASA. A good place to start learning high-level programming languages is Python.
The popularity of Python has surpassed that of Java for the first time
Python is the leader of the pack for the first time in more than 20 years. The long-standing hegemony of Java and C has ended.
It's true that Python is now the most popular programming language, but why? What makes Python so popular? How does Python differ from other languages?
Programming Python isn't an isolated experience. Python is an open-source language that is used for software application development by a lot of Python developers. The popularity and community of Python go together.
Since the creation of Python more than 30 years ago, the Python community has grown a great deal. Think about tens of thousands of software engineers working with Python at the same time that you are. Probability is that someone else has already solved a problem that you have, and you will easily find a solution if you search the internet for it.
Therefore, people can ask developers of any level for assistance if they have a problem with Python.
The simplicity of Python's syntax makes it easy to read and understand even by amateur developers. The simplicity of Python is an important reason for its popularity. It is a relatively straightforward language when compared to other programming languages. One of the reasons why developers like working on it are because of its simplicity.
Python is the closest programming language to English you can find if you are just getting started. Beginners and newcomers find Python to be extremely easy to learn and to use. Since Python is an interpreted language, it also makes it easy to modify its codebase quickly, which adds to its popularity among developers, making it the most popular programming language.
Libraries refer to modules that come with prewritten code that makes it possible for the user to perform multiple actions using the same functionality. As a result, libraries make the development process much easier since you do not have to write every line of code from scratch.
A programming language can have a wider range of use-cases the more libraries and packages it has at its disposal. You can use Python's excellent libraries to save time and effort when you are developing your code for the first time. The following are some of Python's most popular libraries:
Django is a framework for developing web applications.
The TensorFlow toolkit is used for building machine learning applications of high quality.
Engineering applications with SciPy.
NumPy is a powerful library for machine learning.
Pandas is a data analysis and manipulation library.
Plotly is a visualization library.
Flask is a microframework for web applications.
Using SQLAlchemy, one can create Object Models that can interact with traditional relational databases like MySQL and Postgres.
Many people are grateful for Python's simplicity to learn and work with, but what they really should be grateful for is the extensive libraries that are being created as a result of Python's simplicity.
Computer science trends include cloud computing, machine learning, and big data. Artificial Intelligence (AI) is a branch of Machine Learning that focuses on making systems perform certain tasks and take decisions without human supervision.
Data analysts and other professionals can use Python to carry out complex statistical calculations, create data visualizations, build machine learning algorithms, manipulate and analyze data, and complete other data-related tasks.
TensorFlow for neural networks and OpenCV for computer vision are just two of the many Python libraries used every day in machine learning projects. With 57% of data scientists and machine learning developers using Python, Python is the most popular language for Machine Learning.
Python supports a wide range of operating systems, including Android, iOS, and Windows. The Python code can be run and used on a variety of tools and platforms using Python interpreters. Also, with Python, similar code can be run on multiple platforms, and modified app code can be compiled without having to recompile. Additionally, Python can be used if you want to check the impact of code changes, even if it's instantaneous.
Prototyping software applications is easier with Python. It supports prototyping and enables you to create computer programs, websites, web apps, and web services. Python is a very popular language for creating open-source software that provides enhanced capabilities for process control.
Python-based applications you can build:
Technology has swept the market off its feet with blockchain, one of the hottest trends of this decade. Developing Blockchains was not easy, but Python made it look easy. It is easier to build blockchain applications with Python because it is an understandable language.
Developing audio and video applications with Python is a great way to create music. You can use Python to analyze audio and video content on the internet since it is full of audio and video content. You can successfully develop an app using Python libraries like PyDub and OpenCV.
Python and Pygame are used to test the prototypes in real-time, allowing developers to make a rapid game prototype. You can also build game design tools with Python, such as dialog trees and level designers, that help you in the development process.
System administration apps can be developed using Python thanks to the 'os' module, which allows developers to communicate with the operating system. Developers can use this module to interact with the OS that Python runs under.
Python allows for practical flexibility in app development and can create a variety of applications. This is why Python can also be used to develop e-Commerce apps and ERP systems. Python makes the development of such apps effortless, so it is the language of choice for app development companies.
Over the past few years, Python has experienced an increase in demand, popularity, and support communities. Because of its many pre-built libraries, a built-in framework for unit testing, and OS independence, Python has gained a reputation as one of the best tools when it comes to optimizing development resources.
Are you interested in hiring Python developers? We have highly dedicated and experienced Python developers available for hire.
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