Melayu Tech on LinkedIn: 💾 RAM - Random Access Memory 📀 ROM - Read-Only Memory 💿 SSD -… (2024)

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Complete JavaScript Road Map🔥A-Z JavaScript1.Variables ↳ var ↳ let↳ const2. Data Types↳ number ↳ string↳ boolean ↳ null↳ undefined↳ symbol3.Declaring variables↳ var↳ let↳ const4.Expressions Primary expressions↳ this↳ Literals↳ []↳ {}↳ function↳ class↳ function*↳ async function↳ async function*↳ /ab+c/i↳ string↳ ( )Left-hand-side expressions↳ Property accessors↳ ?.↳ new↳ new .target↳ import.meta↳ super↳ import()5.operators↳ Arithmetic Operators: +, -, *, /, %↳ Comparison Operators: ==, ===, !=, !==, <, >, <=, >=↳ Logical Operators: &&, ||, !6.Control Structures↳ if↳ else if ↳ else↳ switch↳ case↳ default7.Iterations/Loop↳ do...while↳ for↳ for...in↳ for...of↳ for await...of↳ while8.Functions↳ Arrow Functions↳ Default parameters↳ Rest parameters↳ arguments↳ Method definitions↳ getter↳ setter9.Objects and Arrays↳ Object Literal: { key: value }↳ Array Literal: [element1, element2, ...]↳ Object Methods and Properties↳ Array Methods: push(), pop(), shift(), unshift(), splice(), slice(), forEach(), map(), filter()10.Classes and Prototypes↳ Class Declaration↳ Constructor Functions↳ Prototypal Inheritance↳ extends keyword↳ super keyword↳ Private class features↳ Public class fields↳ static↳ Static initialization blocks11.Error Handling↳ try, ↳ catch, ↳ finally (exception handling)ADVANCED CONCEPTS--------------------------12.Closures↳ Lexical Scope↳ Function Scope↳ Closure Use Cases13.Asynchronous JavaScript↳ Callback Functions↳ Promises↳ async/await Syntax↳ Fetch API↳ XMLHttpRequest14.Modules↳ import and export Statements (ES6 Modules)↳ CommonJS Modules (require, module.exports)15.Event Handling↳ Event Listeners↳ Event Object↳ Bubbling and Capturing16.DOM Manipulation↳ Selecting DOM Elements↳ Modifying Element Properties↳ Creating and Appending Elements17.Regular Expressions↳ Pattern Matching↳ RegExp Methods: test(), exec(), match(), replace()18.Browser APIs↳ localStorage and sessionStorage↳ navigator Object↳ Geolocation API↳ Canvas API19.Web APIs↳ setTimeout(), setInterval()↳ XMLHttpRequest↳ Fetch API↳ WebSockets20.Functional Programming↳ Higher-Order Functions↳ map(), reduce(), filter()↳ Pure Functions and Immutability21.Promises and Asynchronous Patterns↳ Promise Chaining↳ Error Handling with Promises↳ Async/Await22.ES6+ Features↳ Template Literals↳ Destructuring Assignment↳ Rest and Spread Operators↳ Arrow Functions↳ Classes and Inheritance↳ Default Parameters↳ let, const Block Scoping23.Browser Object Model (BOM)↳ window Object↳ history Object↳ location Object↳ navigator Object24.Node.js Specific Concepts↳ require()↳ Node.js Modules (module.exports)↳ File System Module (fs)↳ npm (Node Package Manager)25.Testing Frameworks↳ Jasmine ↳ Mocha↳ Jest

Python is a popular programming language in the field of data analysis due to its versatility, ease of use, and extensive libraries for data manipulation, visualization, and analysis. Here are some key Python skills that are important for data analysts:1. Basic Python Programming: Understanding basic Python syntax, data types, control structures, functions, and object-oriented programming concepts is essential for data analysis in Python.2. NumPy: NumPy is a fundamental package for scientific computing in Python. It provides support for large multidimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays.3. Pandas: Pandas is a powerful library for data manipulation and analysis in Python. It provides data structures like DataFrames and Series that make it easy to work with structured data and perform tasks such as filtering, grouping, joining, and reshaping data.4. Matplotlib and Seaborn: Matplotlib is a versatile library for creating static, interactive, and animated visualizations in Python. Seaborn is built on top of Matplotlib and provides a higher-level interface for creating attractive statistical graphics.5. Scikit-learn: Scikit-learn is a popular machine learning library in Python that provides tools for building predictive models, performing clustering and classification tasks, and evaluating model performance.6. Jupyter Notebooks: Jupyter Notebooks are an interactive computing environment that allows you to create and share documents containing live code, equations, visualizations, and narrative text. They are commonly used by data analysts for exploratory data analysis and sharing insights.7. SQLAlchemy: SQLAlchemy is a Python SQL toolkit and Object-Relational Mapping (ORM) library that provides a high-level interface for interacting with relational databases using Python.8. Regular Expressions: Regular expressions (regex) are powerful tools for pattern matching and text processing in Python. They are useful for extracting specific information from text data or performing data cleaning tasks.9. Data Visualization Libraries: In addition to Matplotlib and Seaborn, data analysts may also use other visualization libraries like Plotly, Bokeh, or Altair to create interactive visualizations in Python.10. Web Scraping: Knowledge of web scraping techniques using libraries like BeautifulSoup or Scrapy can be useful for collecting data from websites for analysis.By mastering these Python skills and applying them to real-world data analysis projects, you can enhance your proficiency as a data analyst and unlock new opportunities in the field.#python #pythonprogramming #Programming #programmer #languagelearning

🚀 Top 10 YouTube Channels to Explore AIWe’ve carefully selected the best YouTube channels for diving deep into AI:1. Sentdex: Python and machine learning tutorials.https://lnkd.in/eik68-zv2. Two Minute Papers: Quick AI research summaries.https://lnkd.in/gxjT3dV3. Siraj Raval: Accessible AI education.https://lnkd.in/dvW9ZEgx4. TensorFlow: Tutorials and demos on this open-source library.https://lnkd.in/eCTwWYbW5. Lex Fridman: Interviews with AI experts.https://lnkd.in/e7duvZa6. Matt Wolfe: AI news, reviews, and tutorials.https://lnkd.in/gxsYQccv7. AI Explained: Simplifying complex AI concepts.https://lnkd.in/gwmewMhv8. DeepLearning.AI: Courses by Andrew Ng.https://lnkd.in/emSdwk99. Yannic Kilcher: Practical AI tutorials.https://lnkd.in/eJGtQ3_d10. Data School: Data science and machine learning tutorials.https://lnkd.in/eu3utjvTo Get More Freebies, Join Our Telegram Group 👇👇👇✅Group: t.me/MelayuTechPG➡️➡️➡️➡️➡️➡️➡️➡️➡️➡️➡️➡️➡️Contact:✅DM: t.me/MelayuTech✅Group: t.me/MelayuTechPG✅WA: https://lnkd.in/eVxHEPCj✅FB:https://lnkd.in/eWRV8jfa✅LKDN:https://lnkd.in/embpsm_C🌏 React, Comment and Share to get more updates #AI #artificialintelligence

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𝗛𝗼𝘄 𝘁𝗼 𝗰𝗼𝗱𝗲 𝘄𝗶𝘁𝗵 𝗚𝗶𝘁𝗛𝘂𝗯 𝗖𝗼𝗽𝗶𝗹𝗼𝘁?A recent study by GitHub and Microsoft discovered that AI now authors 46% of new code. They also found that overall developer productivity surged by 55%, leading to more efficient coding processes. When we talk about AI-powered coding, we mainly talk about GitHub Copilot.But 𝗵𝗼𝘄 𝗚𝗶𝘁𝗛𝘂𝗯 𝗖𝗼𝗽𝗶𝗹𝗼𝘁 𝘄𝗼𝗿𝗸𝘀?The process goes in the following steps:𝟭. 𝗦𝗲𝗰𝘂𝗿𝗲 𝗽𝗿𝗼𝗺𝗽𝘁 𝘁𝗿𝗮𝗻𝘀𝗺𝗶𝘀𝘀𝗶𝗼𝗻: Your prompts are securely sent to Copilot, ensuring data privacy.𝟮. 𝗖𝗼𝗻𝘁𝗲𝘅𝘁𝘂𝗮𝗹 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱𝗶𝗻𝗴: Copilot analyzes the code around your cursor, the file type, and other open files to offer relevant suggestions.𝟯. 𝗖𝗼𝗻𝘁𝗲𝗻𝘁 𝗳𝗶𝗹𝘁𝗲𝗿𝗶𝗻𝗴: It filters out personal data and inappropriate content, focusing solely on generating helpful code.𝟰. 𝗖𝗼𝗱𝗲 𝗴𝗲𝗻𝗲𝗿𝗮𝘁𝗶𝗼𝗻: Based on the intent identified in your prompts, Copilot crafts code suggestions that align with your coding style and project standards.𝟱. 𝗨𝘀𝗲𝗿 𝗶𝗻𝘁𝗲𝗿𝗮𝗰𝘁𝗶𝗼𝗻: Here, we can decide whether to use, tweak, or reject Copilot's suggestions.𝟲. 𝗙𝗲𝗲𝗱𝗯𝗮𝗰𝗸 𝗹𝗼𝗼𝗽: Copilot learns from your interactions, improving its suggestions. Every time you tweak or reject its ideas, he knows from it. It employs techniques like zero-shot (asking without examples), one-shot (asking with an example), and few-shot learning (providing multiple examples) to adapt to our instructions, whether you provide examples or not.𝟳. 𝗣𝗿𝗼𝗺𝗽𝘁 𝗵𝗶𝘀𝘁𝗼𝗿𝘆 𝗿𝗲𝘁𝗲𝗻𝘁𝗶𝗼𝗻: It remembers past prompts and interactions, making future suggestions more accurate.To Get More Freebies, Join Our Telegram Group 👇👇👇✅Group: t.me/MelayuTechPG➡️➡️➡️➡️➡️➡️➡️➡️➡️➡️➡️➡️➡️Contact:✅DM: t.me/MelayuTech✅Group: t.me/MelayuTechPG✅WA: https://lnkd.in/eVxHEPCj✅FB:https://lnkd.in/eWRV8jfa✅LKDN:https://lnkd.in/embpsm_C🌏 React, Comment and Share to get more updates #coding #github #GitHubCopilot #technology #AI

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