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In this article, we will see if Is Python good for developing games? If yes, what are the primary reasons for it, and if no, what are the reasons?

Python as a game development language has become popular in the entertainment industry. It’s not just because Python is popular in other domains of technology or because it’s free and open source (though those are both great reasons).

Python is utilized in game development because it is a robust and versatile programming language. It automates many of the most typical activities related to game creation, and there are numerous resources to help you learn how to use it efficiently.

Is Python good for Developing Games?

Python is a fantastic programming language for game development. Python game development has shown to be a perfect solution for developers for speedy prototyping and execution of video games as the gaming industry has grown.

Python’s simple and clear syntactic structure differentiates it from other programming languages for game development.

Almost every developer will agree that Python code is simpler to grasp than Java or C code. Python is a popular coding choice among game developers due to its low learning curve.

Python is object-oriented, contains built-in high-level data structures, and enables dynamic type and dynamic binding. Python does allow game creation, although it is not as popular as C++ with DirectX and OpenGL.

PyGame is a developer-friendly and simple-to-use library for creating games. Python is a simple language to learn, so creating games with Python is not difficult.

Reasons why Python is an excellent choice for Game Development. Simple and clear Syntax

Python’s clear syntax is one of the main reasons it is so popular. As a result, the code is simple to read and understand, making it an excellent choice for game creation. Not only is the code simple to read, but it is also simple to write, saving time and effort when developing a game.

Concise syntax aid in the smooth execution of an idea or logic, debugging becomes less difficult, and the flexibility to easily add features makes it a good choice for game development.

Flexible Object Orientation

Flexible object orientation works by assigning attributes/features/properties to a class or type of object. These features are then inherited by other categories derived from them. So, if a programmer wishes to develop an animal-type class, they would first set up some basic features such as eat(), sleep(), and so on, and then any other kinds derived from animals might have the same attributes.

Python is quite versatile in terms of object orientation, which is one of its many strengths. As a result, programmers may simply build new objects and alter old ones without having to write a lot of code. Python is an excellent choice for game development since developers frequently need to generate and update objects on the fly.

Gaming-based Libraries and Frameworks

Python for game creation supports 2D visuals in addition to 3D graphics, making it one of the most popular languages for programming games in general. It has various libraries and frameworks that make game development simple.

Pygame, Pyglet, Kivy, PySDL2, Pymunk, PyOpenGL, PyODE, and many other libraries are widely used in game development. Additionally, a variety of tutorials assist newcomers in their initial stages.

Dynamic Typing

For example, if an idea comes to you in the middle of the night, you can try it out right away because Python does not require type declarations. Python programmers never need to cast variables before using them since all data types are dynamically defined at runtime.

Large Community Support

A supporting community is one of the most crucial things you can have when developing a game. Python has one of the largest and most active programming language communities. If you’re stuck on a problem, chances are more someone in the community has faced and solved it before.

Additionally, because of the large community support, you can simply recruit Python engineers at a minimal cost. Furthermore, these tools include a wealth of open-source tutorials and other instructional materials, so even if you don’t intend to hire a developer, you’ll be able to get started on your project right away.

Artificial Intelligence(AI) Integration Compatibility

The importance of Artificial Intelligence(AI) integration in the gaming business has grown. What if you want to maximize your gaming by using the power of deep learning and other forms of machine learning? Python is an effective AI development tool. Many libraries, such as TensorFlow, Keras, Theano, and others, are specifically created for developers who want to incorporate AI capabilities into their games.

Also, Python may be used on a variety of platforms. As a result, your game can be created on one platform and readily deployed to another. Python is also free and open source, which saves costs even further.

Robust Performance

Importing modules, which allow developers to obtain data from other sources and reuse it in their projects, is a common and influential feature in Python. The module importation function also saves space because a single project does not need to contain all of the data required to work; only the modules needed are imported from other sources and combined with original files.

Python is well-known for its robustness. As a result, your game will be able to handle a large amount of action without slowing down. It comes in handy for creating action-based games since it allows them to process information at the same time without freezing or lagging. The popular Nintendo game Mario Kart 8 Deluxe is a nice example. The game has sold over 45 million copies globally, demonstrating the power of the programming language.

Python Game Development Frameworks Pyglet

Pyglet is an open-source and cross-platform Python windowing and graphics library. It is a powerful Python toolkit for creating games and other visually appealing programs for Windows, Mac OS X, and Linux.

Pyglet provides windowing, user interface event management, joysticks, OpenGL graphics, image and video import, and audio playback. Pyglet works with Python 3.5+ and other Python interpreters like PyPy.


Pyglet does not require any external sources or setup.

It includes a built-in guide for images and audio.

It handles local windowing that is flexible.

Pyglet was written in the natural Python language.


Pygame is a Python framework for developing multimedia applications such as video games that is free and open source. It is based on the well-known SDL library. C, Python, Native, and OpenGL are all used in this module.

Pygame allows users to utilize Python programming to create full-featured games and multimedia packages. It is incredibly portable and compatible with nearly every platform and operating system.


CPUs are easily accessible.

Pygame makes the best use of C and Assembles code for centre functions.

It is simple to use and portable.

Only a minimum lines of code is needed.


PyOpenGL is a popular Python cross-platform binding to OpenGL and related APIs. PyOpenGL’s binding is built with the standard ctypes library.

PyOpenGL works with a variety of Python external GUI libraries, including Pygame, PyQt, Raw XLib, and many others.


PyKyra is a robust Python game development framework based on Software and Documentation Localization(SDL) and the Kyra engine.

Along with the core capabilities of the framework, it also supports MPEG video, sound formats, direct image reading, and a lot more.


Kivy is a cross-platform, open-source Python toolkit for fast application development that incorporates innovative user interfaces such as multi-touch apps. Kivy is available for Linux, Windows, OS X, Android, iOS, and Raspberry Pi.

It supports the majority of inputs, protocols, and devices. The library is GPU speeded up and includes over 20 highly extensible widgets.


Panda3D is an open-source and free-to-use engine written in Python and C++ for dynamic 3D games, visualizations, simulations, and presentations, among other things. Panda3D has command-line tools for analyzing and optimizing source assets, allowing users to manage and code the content development workflow.


Python-Ogre, sometimes known as PyOgre, is a Python binding of the OGRE 3D engine’s C++ library. PyOgre is a cross-platform programming language with a focus on scalability and efficiency. Ogre is a 3D rendering engine written entirely in C++ that has a strong feature set and has been used to create aesthetically spectacular games.


Cocos2d is an open-source framework built in Python, C++, and Objective C that can be used to create cross-platform GUI-based interactive products such as games.


Renowned games such as Frets on Fire have been created with Python and Pygame. Pygame is once again one of the most popular Python libraries for game development. It has been used to create games by both amateur and professional programmers.

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Why Is Python So Much Of A Pain For Enterprise Projects?

In this article, we will learn why python is not the most suitable for larger projects.

Python is preferred as a programming language by millions of developers, making it one of the most popular open-source programming languages with a large developer community. However, some engineers regard Python as a software development anomaly. They consider Python to be primarily a “glue” scripting language, more suited for routine system automation or integrating two applications. That is not correct; Python as a programming language is capable of producing industrial−strength software.

Under the hood, Python contains every feature that makes it ideal for a wide range of software development projects. It is open−source and uses an object-oriented approach. Additionally, hundreds of thousands of modules and libraries expand the scope of what Python can do.

However, Python continues to fail to penetrate one computing segment−enterprise development. Python is still considered primarily just as a scripting language for enterprise−level software development.

Absence of a Graphical User Interface (GUI) and teamwork tools

The fundamental reason Python may not be suitable for full−stack development at the enterprise level is that creating graphical user interfaces in Python is overly complicated.

Python comes with Tkinter, which is commonly used by Python developers to construct interfaces but is not powerful enough to suit the needs of large-scale applications. Modern software development is highly graphic, and Python suffers from a lack of a decent GUI tool.

Absence of teamwork tools

The other reason Python is not commonly used for large−scale full−stack development is a lack of tools for team collaboration. This shortcoming is unacceptable in corporate software development and is seen as a potentially fatal flaw. Because they provide dedicated capabilities to assist teams to cooperate while working on large-scale projects, programming languages like Java and C++ are chosen for enterprise−level full−stack development.

Working with several programmers and using a language with no such assistance tool can be problematic. While Python handles this issue by using modules and namespace resolution to assure that there are no conflicts in a multi−programmer environment, it is still not convincing enough for teams to use it in large-scale projects.

Limitations of the database access layer

Large−scale software development projects need a large amount of data. Terabytes of data are produced or consumed by modern applications, and such data management necessitates the use of well-established technologies such as ODBC and JDBC. Unfortunately, Python’s database access layer lacks the robustness of these solutions. Python’s database layer may be described as primitive and underdeveloped. This is yet another key problem that renders Python unsuitable for usage in enterprise−level applications by any full−stack developer.

Modern software development is highly agile, and corporations seek programming languages that can interact quickly and smoothly with complex legacy data, as well as SQL database access that is robust and fast. Python does not meet these requirements, and because there are no words on it shortly, it is not suited for large−scale development stacks.

Python is Slow at the Runtime

Another major reason to avoid Python in your stack is that it runs slower than other programming languages such as PHP, JavaScript, C++, or Java. While this may not be an issue with a few hundred lines of code, it becomes a major one when a full− stack developer is required to design massive applications with hundreds of thousands of lines of code.

As a high−level programming language, Python is not highly sensitive to hardware resources. Instead of a compiler, Python code requires an interpreter. Because an interpreter compiles the code line by line, it’s easy to see how this can be an issue when working with a large code base. Python is also slow at runtime because it is a dynamically typed language. When we utilize a variable in a dynamically typed language, the variable data type is not defined. The data type is determined at runtime. As a result, every time a variable is read, written, or referenced, the interpreter checks the data type to allocate memory appropriately. It slows down Python code during execution.

Python Provides Inadequate Documentation

Assume you’ve worked on large−scale projects before. In that scenario, you understand how significant it is to have detailed documents available technology before integrating it into your development stack. Python suffers greatly from a lack of adequate documentation as compared to competing programming languages such as Java, Perl, and PHP.

Moreover, there aren’t as many Python books available. In comparison, PHP has twice as many titles on the market as Python. While the online Python documentation is well organized and a nice place to start, it only serves as a sparse(limited) reference source. If your team lacks experienced coders, you may be stranded for a long time without enough learning resources for Python developers to assist you in troubleshooting.

Using Other Languages is Difficult

Many programmers dislike writing code in languages other than their own/native language. This is because they assume that learning other languages is far more difficult. They may be accustomed to programming with Python, the world’s most user-friendly programming language. If you are a Python expert, you may be experiencing this problem. Pythonistas admire the language because it is easy to learn, widely utilized, and extremely powerful. One of the key reasons we like Python is that it is simple.


While Python is a robust high-level programming language, it has limits when it comes to large−scale full−stack development. Although the extremely active Python community and devoted development teams around the world are attempting to improve Python’s restrictions so that it can be used for enterprise-level development, it is currently best suited for small−scale software development projects.

What Is Tls? And Why Transport Encryption Is Not Sufficient?

Privacy and security are the two most valuable terms in this rapidly-expanding era of technology. From regular individuals to hotshot corporations, everyone is wary of the privacy of the security of the information they are exchanging.

In the aftermath of the COVID-19 pandemic, almost all corporations have turned to video conferencing applications to keep their operations afloat. Now, with so much sensitive information flying about, the need for top-notch privacy measures isn’t unwarranted.

Today, in an attempt to ease your mind, we’ll take a look at the most popular encryption protocol that companies are using — Transport Layer Security (TLS) — and tell you whether it’s capable of making your conferences tamper-proof.

What is TLS?

Transport Layer Security (TLS) is a widely-used security protocol that facilitates privacy and security for information exchanged over the internet. TLS is a worthy successor of the Secure Socket Layer (SSL) protocol, which was first introduced by Netscape in 1996.

TLS 1.3 is the current industry-standard. It succeeded TLS 1.2 in 2023.

How does TLS protect your data?

Uses same pre-defined keys for encryption and decryption

Public Key Cryptography is used to authenticate

Undetected loss is prevented with the use of integrity checks

Before two parties start exchanging data over a TLS connection, a sequence, called TLS Handshake, is carried out. Through the handshake, the parties agree upon the encryption keys that would be used throughout the session. TLS uses Public Key Cryptography to set the encryption/session keys over an encrypted channel. The handshake once again makes use of the Public Keys to carry out the authentication process.

After the authentication and encryption are confirmed, the data packets are signed off with a unique Message Authentication Code (MAC). This allows the clients to verify the integrity of the data packages. Once all prerequisites are met, clients can exchange data over a secure TLS connection.

A real-world example of TLS

Now that you’re familiar with the basics of TLS, let’s take a look at how it works in the real world.

For example, imagine you are texting with your friend over a TLS connection — which is standard for almost all video conferencing apps and websites. Now, every text or media file you send is first encrypted and sent directly to the server. The server decrypts the package, verifies, encrypts it again, and sends it over to the intended recipient. Finally, the message is again decrypted at your friend’s end, allowing them to read and respond accordingly.

Is TLS enough?

As mentioned, COVID-19 has directed a lot of traffic to video conferencing platforms. The likes of Zoom, Microsoft Teams, and Google Meet have benefitted greatly from the lockdown measures, but they haven’t particularly done enough to ensure our security.

Related: Zoom vs Google Meet

Almost all leading video conferencing platforms use Transport Layer Security or Transport Encryption to safeguard our data. And while that seems secure enough for most occasions, it’s hardly the gold standard of security and privacy.

Unlike End-to-End Encryption, TSL allows your server to decrypt the data you are transmitting. So, unless you’re sharing public / non-sensitive info, you could deem it unnecessary and exploitation of your privacy. It also makes your messages vulnerable to government intrusion, meaning they could take harsh actions if they intended.

Additionally, in a TSL connection, the server and client computer are free to pick the form of encryption their session would have. So, they could pick a standard that isn’t as robust as you’d like, leaving you vulnerable to cyber-attacks.

Although this issue is usually prevalent when communicating with a “secure” website, it’s still something worth pondering over.

Why Is Java Important For Big Data?

Big data refers to extremely large and complex data sets that traditional data processing software and tools are not capable of handling. These data sets may come from a variety of sources, such as social media, sensors, and transactional systems, and can include structured, semi-structured, and unstructured data.

The three key characteristics of big data are volume, velocity, and variety. Volume refers to a large amount of data, velocity refers to the speed at which the data is generated and processed, and variety refers to the different types and formats of data. The goal of big data is to extract meaningful insights and knowledge from these data sets that can be used for a variety of purposes, such as business intelligence, scientific research, and fraud detection.

Why is Java needed for Big Data?

Java and Big Data have a fairly close relationship and data scientists along with programmers are investing in learning Java due to its high adeptness in Big Data.

Java is a widely-used programming language that has a large ecosystem of libraries and frameworks that can be used for big data processing. Additionally, Java is known for its performance and scalability, which makes it well-suited for handling large amounts of data. Furthermore, many big data tools such as Apache Hadoop, Apache Spark, and Apache Kafka are written in Java and have Java APIs, making it easy for developers to integrate these tools into their Java-based big data pipelines.

Here are some key points we should investigate where Java’s importance can be mentioned cut-shortly;

Performance and Scalability

Java is known for its performance and scalability, which makes it well-suited for handling large amounts of data.

Java APIs

Many big data tools such as Apache Hadoop, Apache Spark, and Apache Kafka are written in Java and have Java APIs, making it easy for developers to integrate these tools into their Java-based big data pipelines.


Java is platform-independent, meaning that the same Java code can run on different operating systems and hardware architectures without modification.

Support and Community

Java has a large and active community of developers, which means that there is a wealth of resources, documentation, and support available for working with the language.

Prime Reasons Why Data Scientists Should Know Java

Java is a popular language for big data scientists because it is highly scalable and can handle large amounts of data with ease. Data science has heavy requirements, and being the top 3 listed programming languages Java can meet the requirements easily. With active Java Virtual Machines around the globe and the capability to scale Machine Learning applications, Java offers scalability to Data science development.

Widely-used big Data Frameworks Large Developer Community

Java has a large developer community, which means that there is a wealth of resources available online for learning and troubleshooting. This makes it easy for big data scientists to find answers to questions and learn new skills, which can help them quickly and effectively solve problems that arise during data science development.


Java is platform-independent and can run on a variety of operating systems and architectures, which makes it a great choice for big data scientists who may need to develop applications that run on different platforms.


In short, Java is a powerful and versatile language that is well-suited for big data development, thanks to its scalability, wide use of big data frameworks, large developer community, portability, and familiarity in the industry. It is a language that big data scientists should consider learning to excel in the field.


In conclusion, Java is a powerful and versatile language that is well-suited for big data development. Its scalability, ability to handle multithreading and efficient memory management makes it an excellent choice for handling large amounts of data.

Additionally, Java is the primary language for many popular big data frameworks, such as Hadoop and Spark, which provide pre-built functionality for common big data tasks. The large developer community also means that there is a wealth of resources available online for learning and troubleshooting. Furthermore, Java is platform-independent, which makes it a great choice for big data scientists who may need to develop applications that run on different platforms.

Why Pay Extra For Expensive Earbuds When Cheap Earbuds Sound Good Enough?

Lily Katz / Android Authority

With so many of us tightening our budgets, expensive earbuds may seem frivolous. After all, cheap earbuds have improved over the past few years. Many earbuds that cost $50 or less boast pretty good sound quality and even offer features like water resistance and wireless charging. With all that cheap earbuds have to offer, is it even worth it to pay more for Sony, Samsung, or Apple’s flagships?

With concrete examples, we’ll break down the differences between cheap and expensive true wireless earbuds. In the end, you may find the price is just right between those two extremes.

Mid-tier noise-cancelling performance is all over the place. ANC on the Beats Studio Buds and Nothing Ear 1 does little to cancel out background noise. Then there are the Samsung Galaxy Buds 2 and Sony LinkBuds S that dampen background noise quite a bit. Sony’s own LinkBuds S actually has better ANC than the flagship WF-1000XM4 from 75-350Hz, so wind, engine rumbles, and A/C whirs will all sound a smidge quieter with the LinkBuds S than with the XM4. No matter what, though, the WF-1000XM4 block out more high frequencies like chatter, keyboard sounds, and clinking dishware.

Performance is consistently great when you get to high-end earbuds. Look at how the Google Pixel Buds Pro, Samsung Galaxy Buds 2 Pro, Apple AirPods Pro 2, and Sony WF-1000XM4 compare above. The Galaxy Buds 2 Pro cancel out more super-low frequencies than any of the other earbuds, but the effect may not sound as pleasing as with the AirPods Pro (2nd generation) or the WF-1000XM4.

If you are on the lookout for great noise-cancelling earbuds, don’t cut corners. We recommend you put off your purchase and save for a few more weeks or months to experience great ANC. Your ears will thank you.

Custom EQ module and EQ presets: You can change the earbuds’ frequency response to your liking, or choose from a handful of premade profiles.

Personalized EQ with hearing test: Some apps prompt you to take a hearing test to optimize the frequency response to your hearing abilities.

Control customization: You can change the earbud commands to your liking.

Personalized spatial audio with head tracking: Listeners can personalize the spatial audio effect for a more immersive experience. Enabling head tracking sets your phone as an anchor point, and the audio changes as you move your head relative to your phone.

Ambient sound or transparency mode: These listening modes let you filter background noise through the earbuds.

Location-based sound modes: When you enter or leave a certain location, the sound changes to a predetermined setting that you created.

Automatic ear detection: When you remove the buds, your media pauses. Inserting the earbuds resumes playback.

Ear tip fit test: The app will run a test emitting noises through the earbuds to determine if you’re using the best size ear tips for your ears.

Bluetooth codec options: You can toggle between the earbuds’ Bluetooth codecs.

Bluetooth multipoint: The earbuds can connect to two devices simultaneously. This is good for productivity when you want to watch a YouTube video but keep an ear on your phone for incoming calls.

Ongoing firmware updates: All apps offer access to firmware updates to keep the earbuds competitive and to fix bugs.

While none of these features are necessary for connecting your earbuds to your Android phone or iPhone and listening to music, they sure enhance the experience. You’ll have a hard time finding any earbuds with an original retail price of $50 or less that come with an app at all.

Instead, the appeal of cheap earbuds is that what you buy is what you get. However, simplicity has its downsides. App-less earbuds can’t receive updates to improve the product throughout its lifetime. Most earbud manufacturers start offering apps at around the $100 mark, which is a reasonable price for access to a few or all of these features.

What to watch out for before buying expensive earbuds

Austin Kwok / Android Authority

While expensive earbuds have plenty to offer, wireless earbuds don’t last very long. With regular use, you’re lucky to get more than a couple of years out of them. That’s right, you could spend well over $200 on earbuds that can’t hold a charge after two or three years. How does this battery degradation happen? Simply put, we subject earbuds to an unending charge/recharge cycle. This puts a lot of stress on the tiny lithium-ion batteries, hastening battery breakdown.

Most wireless earbuds won’t survive more than two years of daily use.

If you don’t want to buy a pair of earbuds every two years, we recommend buying wired earbuds. The KZ ZSN Pro X ($23 at Amazon) and Tin Audio T2 HiFi ($49 at Amazon) fit the bill as great wired earbuds. Alternatively, wired headphone batteries last much longer than those in earbuds because they’re bigger and aren’t always recharging. The Anker Soundcore Life Q35 don’t cost too much ($99 at Amazon). Listeners who are ready to go big can grab the Sony WH-1000XM5 headphones for $398 at Amazon.

Those who insist on wireless earbuds may find purchasing mid-tier earphones is an easier financial pill to swallow. These usually fall between $75 and $160. Spending this much opens the door for app support, with a feature set near or identical to a brand’s flagship option.

Why Is Nist Cybersecurity Framework Important For Tech

blog / Cybersecurity Why is NIST Cybersecurity Framework Important for Tech-Era Businesses?

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According to Cybercrime magazine, cyber crimes will cost the world $10.6 trillion annually by 2025. Hence, an understanding of cybersecurity has become essential for both individuals and companies as the tools to fight attacks. One way of developing this awareness is through a cybersecurity framework. This provides a common language and set of norms that enable organizations to minimize the risks of attacks. That’s why multiple cybersecurity frameworks have been implemented to safeguard information. One such is the NIST Cybersecurity Framework. We’ll understand how it works and who should use it but, first, we’ll answer the question ‘what is NIST Cybersecurity Framework?’

What is NIST Cybersecurity Framework?

Created by the National Institute of Standards and Technology (NIST), which operates under the US Department of Commerce, this framework helps set up and enhance cybersecurity strategy. It offers a set of standards and suggestions that help companies become better equipped to recognize and stop cyberattacks. Also, companies respond to, prevent, and recover from cyber disasters using NIST. Therefore, it is considered the benchmark standard in building a cybersecurity program. This framework has five core functionalities. 


Using this function, the company may build vital infrastructure for understanding and managing cybersecurity risks, threats, and vulnerabilities to system data and other resources.


This function describes necessary safeguards to guarantee the delivery of critical infrastructure services. Moreover, it also supports the capacity to restrict or control the consequences of a possible cybersecurity event. 


It is vital to detect possible cybersecurity issues, and this function outlines the required activities to note the development of a cybersecurity event in a timely way.


This function aids in mitigating the effects of a potential cyber disaster. It focuses on suitable activities in the event of a detected cybersecurity incident and helps limit the impact of a possible cybersecurity problem.


A prompt return to normal activities is required to mitigate the consequences of a cybersecurity event. Therefore, this function creates and carries out the necessary operations for resilience and restores any skills or services that were harmed due to a cybersecurity event. 

Who Should Use the NIST Cybersecurity Framework?

Initially created in 2014 under President Barack Obama’s executive orders, this framework is used across US government offices. These guidelines benefit the government and help other businesses and non-governmental organizations. As a result, anybody dealing with or being accountable for their organization’s cybersecurity should be familiar with the NIST Cybersecurity Framework. Simply put, any company that wants to strengthen its cybersecurity can use the NIST Cybersecurity Framework. 

ALSO READ: What is Cybersecurity and Why is it a Great Career Choice for You

How to Get Started with the NIST Cybersecurity Framework?

As mentioned earlier, the framework is divided into five significant functions: Identify, Protect, Detect, Respond, and Recover. When these five often-used phrases are viewed together, they give a complete picture of the lifecycle of managing cybersecurity over a period of time. As a result, the indicated functions are an appropriate place to start using this framework for your organization. 

What does NIST Stand for?

NIST in NIST Cybersecurity Framework stands for the National Institute of Standards and Technology, which comes under the US Department of Commerce. It is a non-regulatory government organization established to foster innovation and economic competitiveness in science, engineering, and technology. Therefore, NIST’s primary job is to develop the best standards for companies and government agencies. 

The downside of the digital world is the need for constant vigilance against cybercrimes, and the only way forward is knowing the tools to help deal with this reality. Learning about the NIST cybersecurity framework is a step in the right direction. Now, if you are keen on fortifying your knowledge further and becoming part of the solution, then explore Emeritus’ courses on cybersecurity to enhance your cybersecurity career. 

By Siddhesh Shinde

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