Understanding SQL vs Programming Languages: A Simple Guide

Let me suggest we move beyond the fuzzy categories of “strong” and “weak” type systems. Instead we can speak of something well-defined like sound and unsound. For example, the languages pictured here all have unsound type systems. Whereas Typescript is… also unsound. However in strict mode, it continues to be unsound.

👉🏻👉🏻Have you ever wondered C is statically & weakly typed programming language, and what does that mean? To understand that we need to see all types of languages first. Below image might help you with which language is static or dynamic and strong or weak. So let's start 👇🏻👇🏻 1.Statically typed languages: • The languages in which we have to declare the variable data type is called Statically typed language. • Computer checks the data types on compile-time. example: C, C++, Java. 2.Dynamically typed languages: • The languages in which we don’t have to declare the variable data type, just use the variable directly and computer figures its data type itself is called Dynamically typed data. • Computer checks the data type on run-time (execution). example: Python, Javascript, Ruby. 3.Strongly typed languages: • In these languages, compiler can’t type-cast the data so we have to do it manually — doesn’t matter if it’s implicit or explicit type-casting. • These languages are strict — they won’t let you mix types unless you do it clearly. example: Python, JAVA Python won’t let you add "2" + 2 unless you convert one type to the other. 4.Weakly typed languages: • In these languages, compiler can type-cast data, implicit and explicit both, so no need to do things manually. • These languages are more flexible — they’ll quietly convert types to make things work. example: Javascript, C, C++ In JavaScript, "2" + 2 becomes "4" automatically! I would like to thank my sir for delivering this wonderful knowledge to me. I hope you learned something new.🌟🌟

What Programming Language to Learn in 2025 and WHY In 2025, several programming languages are highly recommended for learning, each with its own strengths and ideal applications. Here's a breakdown of some of the top contenders: 1️⃣ Python: Python has often been mentioned as the best language to learn in the year 2025. It is applauded to be flexible, easy to write and understand and as such, can be used by beginners and even by advanced software programmers. Python is an important skill in most sectors due to it being extensively utilized in the fields of data science, machine learning, artificial intelligence, web development, and automation. 2️⃣ JavaScript: JavaScript is also necessary in web development. It is applied to develop interactive web pages and to show dynamically the content. JavaScript is capable of front and back development. 3️⃣ Java: Java is a stable language which is robust especially in large businesses. It is applied in the development of web and mobile application and business software. Java is popular in the enterprise level applications because of its stability and capability to support massive projects. Question for you – what do you think is the programming language that will present the greatest career opportunities over the next 5 years, and why? 🤔 Let's discuss the future of coding! 🚀 –– Check out here - https://lnkd.in/gAquT3ZX #Programming #Coding #SoftwareDevelopment #TechCareers #AI #Python #JavaScript #Java #CSharp #GoLang #TypeScript #TechTrends #CareerAdvice #Askpromptai

💻✨ Complete Programming Setup Guide (C, SQL, Web, Python) — Beginner to Advanced . Are you starting your coding journey but confused about which software to install? 🤔 Here’s your complete setup guide — all in one place 🚀 💠 C Language Setup :- ➢ Options :- 🟢 Code::Blocks ➜ Best overall (built-in compiler) 🟣 Dev C++ ➜ Lightweight & simple 🔵 Turbo C++ ➜ Old school (basic C only) ➢ Recommended :- Code::Blocks or Dev C++ 🛠️ Installation :- • Download 👉 https://lnkd.in/dBSTGEFk ( Code::Blocks ) • OR 👉 https://lnkd.in/dxd66bJD ( Dev C++ ) • Install with MinGW compiler • Open → New Project → Console Application → Language: C • Save file as .c → Run (F9) 🧩 Logo :- Code::Blocks | Dev C++ 💡Why Dev C++? ➜ Lightweight, fast & beginner friendly ! 💠SQL / Database Setup :- ➢ Options :- 🐬 MySQL Workbench ➜ Professional database GUI 🟢 XAMPP / WAMP ➜ All-in-one local server (PHP + MySQL + Apache) 🟣 SQLite Browser ➜ Lightweight database tool ➢ Recommended :- ✅ WAMP (Windows) or XAMPP for local database + PHP ✅ MySQL Workbench for writing SQL queries 🛠️ Installation :- • Download 👉 https://lnkd.in/dqP7n-Fg (_XAMPP ) • OR 👉 https://lnkd.in/dWaRsNx5 ( WAMP ) • Install → Start Apache + MySQL server • Open phpMyAdmin → Create database → Write SQL queries 🧩 Logo :- XAMPP | WAMP | MySQL 💡 Why MySQL? ➜ Most used for backend databases, easy GUI, used in real-world projects. 💠Web Development Setup (HTML, CSS, JavaScript, Bootstrap) ⁃ Best Software: Visual Studio Code ( VS Code ) ⁃ Why: Supports all web languages + live preview + great extensions 🛠️ Installation :- • Download 👉 https://lnkd.in/dw7Zv9VZ ( VS Code ) • Install these extensions :- 🌈 Live Server — Auto browser refresh 🎨 Prettier — Auto formatting 💻 HTML CSS Support 📁 Create files :- index.html , style.css , script.js • Click “Go Live” to preview instantly 🌐 💡 Tip :- Use Bootstrap CDN 👉 https://getbootstrap.com/ 🧩 Logo :- VS Code | Bootstrap 💡 Why VS Code? ➜ Free, smart suggestions, extensions, and supports almost every language. 💠 Python Language Setup :- ➢ Options :- 🟢 PyCharm (JetBrains) — Full IDE for Python 🔵 VS Code — Multi-language & beginner-friendly ⚪ IDLE — Basic editor (comes with Python) ➢ Recommended :- VS Code (multi-language) or PyCharm (advanced users) 🛠️ Installation :- • Download 👉 https://lnkd.in/dV_Xmn_4 ✅ Check “Add Python to PATH” • Install VS Code or PyCharm 👉 https://lnkd.in/dD2EgPdC • In VS Code → install Python (Microsoft) extension • Run your first program :- print("Hello, World!") 🧩 Logo :- Python | PyCharm | VS Code 💡 Why Python? ➜ Simple syntax, beginner-friendly, and used in AI, ML, Web Dev & Automation. #CodingSetup #BeginnersGuide #CProgramming #SQL #WebDevelopment #Python #VSCode #LearningJourney #ProgrammingLife #LinkedInLearning

Typing Systems in Programming Languages Every programming language follows a specific system that defines how it handles data types — when they’re checked and how strictly they’re enforced. These systems help determine how safe, flexible, or predictable a language is. Typing systems are generally classified into four main categories: 1. Statically Typed Languages Variable types are declared and checked before execution (compile-time). Errors are caught early, improving reliability and performance. Examples: C, C++, Java 2. Dynamically Typed Languages Types are determined during execution (run-time). Offers more flexibility but can lead to runtime type errors. Examples: Python, JavaScript, Ruby 3. Strongly Typed Languages Enforces strict rules for mixing different data types. Type conversions must be explicit, preventing unintended behavior. Examples: Python, Java 4. Weakly Typed Languages Allows implicit type conversions between incompatible types. Provides flexibility but can cause unpredictable results. Examples: C, JavaScript Summary: > Static vs Dynamic → When types are checked Strong vs Weak → How strictly types are enforced Thanks for giving us such information about languages in depth (Syed Zabi Ulla)

I've seen this same diagram almost twenty times over the last 48 hours, repeated in about seven different ways but always the same graph. C'mon, people, quit it. Repeating what other people post and claiming it as your own just makes you look bad. If you found something interesting (or want to disagree with it!), repost it and add your commentary. Always give credit to the original source via citation, not just name. Always link directly to the source if you can. Ironically, I disagree with the C++ classified as "weak"; yes, it does automated type conversions, but then again so does Haskell or most other functional languages, and those hardly qualify as "weak"ly typed, particularly since their results are very well known ahead of time.

🧠 Understanding Encapsulation in Java (Human Brain Analogy) Object-Oriented Programming (OOP) is built on six key pillars — Class, Object, Encapsulation, Inheritance, Polymorphism, and Abstraction. 💡 What is Encapsulation? Encapsulation is the process of providing security and controlled access to the most important component of an object — its data members. It helps protect data from unauthorized access and modification. 🧍♂️ Real-Life Analogy — Human Brain Think of the human brain as the most critical part of our body. We cannot access it directly — it’s protected by the skull 🧱 (like the private keyword in Java). 👀👂 Eyes & Ears → Setters(methods) → to set data to the brain 👄 Mouth → Getter(methods) → to get data from the brain 🧠 Skull → Private Access Modifier → to secure the brain This perfectly represents Encapsulation — the brain (data) is secure and accessed only through controlled channels. 💻 Example class Bank { private int accountNumber; private int pin; public void setPin(int pin) { this.pin = pin; // 'this' used to solve shadowing problem } public int getPin() { return this.pin; } } public class Demo{ public static void main(String[] args){ Bank b = new Bank(); b.setPin(1234); System.out.print(b.getPin()); } } ✨ Encapsulation = Data Security + Controlled Access #Java #OOP #Encapsulation #Programming #LearningJourney #SoftwareDevelopment #CodingInJava

💻 Day 24 of #100DaysLearningChallenge by Saurabh Shukla Sir 📚 Learning Topic: Working with XML in Python Coding 🧠 What I Learned: Today, I explored how to handle XML files using Python — one of the most common tasks in backend development and data exchange. XML files are used everywhere — from APIs to configuration files — and learning to parse them efficiently is a must for any Python developer! 💡 Concepts Covered: 👉 What XML files are and why they’re used in software systems 👉 Reading and loading XML data in Python 👉 Parsing XML using the built-in xml.etree.ElementTree module 👉 Navigating through XML elements and attributes 👉 Extracting specific data and converting XML into Python data structures ⚙️ Key Takeaways: ✅ Learned how to open and read XML files using Python ✅ Understood the role of the ElementTree library in XML parsing ✅ Practiced extracting values and iterating through nested XML tags ✅ Gained insights on using XML in real-world applications like configuration files and data exchange 💡 Concept Insight: XML + Python = a powerful combo for automation and data interoperability. Whether it’s reading API responses, storing configurations, or exchanging structured data, XML parsing makes your programs more robust and scalable. 🚀 🔗 GitHub: https://lnkd.in/eTkRFyv2 #100DaysLearningChallenge #XML #Python #WebDevelopment #DataExchange #LearningEveryday #Developers #ProgrammingJourney #TechLearning #CodeEveryday

Java is a comprehensive and powerful language that has proven its worth in the world of programming for more than two decades. And despite the emergence of new languages such as Python and Kotlin, Java still holds a distinguished position thanks to its continuous evolution and adaptability to modern technologies. Nested Loops (programming construct where one loop is placed inside another. This structure allows for repeated execution of a block of code within another loop, enabling the handling of multi-dimensional data or complex repetitive tasks) public class NestedLoopExample {   public static void main (String [] args) {     for (int i = 1; i <= 3; i++) {           for (int j = 1; j <= 4; j++) {            System.out.println("i = " + i + ", j = " + j);       }       System.out.println("-----------------");     }   } }

Which Programming Language Delivers the Fastest API Performance? Rust is 7.5x faster than Node.js. I agree, Rust is not the simplest programming language. C# is 6.3x faster than Node.js, and .NET is very accessible. I ran all these tests after seeing so many job offers claiming "building a top team to deliver super-performant APIs with Python or Node.js". Please stop that. This is not true. Saying that is like running a decathlon with stones in your shoes. I also spent some time comparing different JVMs, both in Docker and without Docker. Comparing Java Native vs Java in Docker => You lose a lot of performance using Docker. Be smart, choose the right stack, and please stop using Python and Node.js for APIs! You will save time, achieve better performance, and keep your team happy. If you need Python for AI, then go for https://aspire.dev. C# backbone with Python workers. Machine at https://ovhcloud.com, VPS-2, 6 vCore, 12 Gb RAM for 7€/month tax included. Full source code and setup scrips https://lnkd.in/euVVsKT9 See comment for updated graph for node.js now 2546 req/s