Future Tech Skills’ Post

𝐏𝐘𝐓𝐇𝐎𝐍 𝐟𝐨𝐫 𝐄𝐯𝐞𝐫𝐲𝐨𝐧𝐞 (𝐃𝐚𝐲-20): Start your learning today with Yogesh Tyagi. (Share and comment for better reach) 𝗠𝗮𝘀𝘁𝗲𝗿𝗶𝗻𝗴 𝗠𝗼𝗱𝘂𝗹𝗲𝘀 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻 𝗪𝗵𝗮𝘁 𝗔𝗿𝗲 𝗠𝗼𝗱𝘂𝗹𝗲𝘀 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻? Modules are Python files containing reusable code like functions, classes, or variables. They simplify development by promoting code reuse, organization, and modularity. 𝗧𝘆𝗽𝗲𝘀 𝗼𝗳 𝗠𝗼𝗱𝘂𝗹𝗲𝘀: Python offers built-in modules (e.g., math, os), external modules installed via pip (e.g., numpy, pandas), and custom modules tailored to specific project needs. 𝗕𝗲𝗻𝗲𝗳𝗶𝘁𝘀 𝗼𝗳 𝗨𝘀𝗶𝗻𝗴 𝗠𝗼𝗱𝘂𝗹𝗲𝘀: Modules streamline code management, enhance maintainability, and provide access to pre-built libraries, reducing development effort and time. 𝗪𝗼𝗿𝗸𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗠𝗼𝗱𝘂𝗹𝗲𝘀: Import modules using import, from ... import, or aliases. Explore their content with dir() and detailed documentation using help(). 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: Understand the __name__ and __main__ variables for module execution contexts. Reload modules dynamically with importlib. 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻𝘀 𝗼𝗳 𝗠𝗼𝗱𝘂𝗹𝗲𝘀: From web development to data analysis and automation, modules power a variety of Python use cases, ensuring efficiency and scalability. Follow Yogesh Tyagi for more such information and don’t forget to save this post for later #python #pythonlearning #interviewpreparation #interview #jobs #development #testing #datanalyst #datascience #growth #etlautomationTesting #bigdatatesting #cloudtesting #automationtesting

Python topics need to cover in python...... To cover few topics attached notes that is having with examples all the scenarios to learn. Good notes to learn python. ✅Python Basics, ✅ Data Structures ✅Object-Oriented Programming (OOP) ✅ Error Handling and Exceptions ✅ Modules and Packages ✅ File Handling ✅ Functional Programming ✅Iterators and Generators ✅Regular Expressions (Regex) ✅Data Handling and Processing ✅Date and Time Handling 🎯Advanced Topics, ✅Database Interaction, ✅Web Development, ✅ Web Scraping, ✅Data Science and Machine Learning, ✅APIs and HTTP Requests, ✅Testing and Debugging ✅ Virtual Environments and Package Management, ✅ Deployment and Production, ✅Best Practices and Optimization. ✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅✅ Follow Harikesh Tyagi🎯 for more content. #DataEngineering #SoftwareEngineering #DataPipeline #BigData #ETL #DataWarehouse #DataOps #MachineLearning #CloudComputing #DataAnalytics #Python #SQL #APIs #DevOps #DataIntegration #DataModeling #Scalability #Automation #DataScience #Coding #Programming #Database

🌟 Final Project Reveal: "Using Python to Interact with the Operating System" 🌟 As promised, I'm excited to finally share the final project from the "Using Python to Interact with the Operating System" course by Google. This project has been a great learning experience, and I can't wait for you all to check it out! 🔍 Project Overview: The project consists of two Python scripts that automate log analysis and generate useful insights. ▶ code.py: Reads a log file and uses advanced regex to extract and separate info and error messages by username. Generates two CSV files: One listing error names with their occurrence counts. Another showing user information alongside the number of info and error messages they encountered. ▶ csv_to_html.py: Converts the CSV files into HTML content, which can be visualized in a web browser. This allows for a clear and interactive presentation of log data and statistics. 🔧 Key Skills Highlighted: 🔸Mastering the OS and sys modules in Python. 🔸Utilizing regex for parsing complex data patterns. 🔸File management and data streaming to automate CSV report generation. 🔸Implementing automated unit testing and exception handling. 📊 The result? A user-friendly way to visualize log data and track errors—ideal for system admins and developers looking to keep an eye on system performance. Stay tuned for more updates, and feel free to check out the project in the link below! 🔗 https://lnkd.in/dTrzK5rQ #Python #Programming #Automation #LogAnalysis #BashScripting #DataVisualization #WebDevelopment #SystemAdmin #Coding #Tech #GoogleCourse

Here is a structured and easy-to-read LinkedIn post based on the Python roadmap image you provided: --- 🚀 **Python Roadmap for Aspiring Developers** 🚀 If you're looking to master Python, this comprehensive roadmap by Gina Acosta Gutiérrez is a fantastic guide to get you started and advance your skills. Here’s a breakdown of what to focus on: ### Basics - Basic Syntax - Variables - Data Types - Conditionals - Typecasting - Exceptions - Functions - Lists, Tuples, Sets - Dictionaries ### Advanced - List Comprehensions - Generators - Expressions - Paradigms - Regex - Decorators - Iterators - Lambdas ### Data Structures and Algorithms (DSA) - Arrays and Linked Lists - Heaps, Stacks, Queues - Hash Tables - Binary Search Trees - Recursion - Sorting Algorithms ### Object-Oriented Programming (OOP) - Classes - Inheritance - Methods (Including Dunder Methods) ### Data Science - NumPy - Pandas - Matplotlib - Seaborn - Scikit-Learn - TensorFlow - PyTorch ### Package Managers - PyPI - pip - conda ### Web Frameworks - Django - Flask - FastAPI - Tornado ### Automation - File Manipulation (os, shutil, pathlib) - Web Scraping (BeautifulSoup, Scrapy) - GUI Automation (PyAutoGUI) - Network Automation ### Testing - Unit Testing (unittest, pytest) - Integration Testing - End-to-End Testing (Selenium, PyAutoGUI) - Test-Driven Development (TDD) This roadmap is a valuable resource for anyone on their Python journey. Follow these steps, and you'll be well on your way to becoming a proficient Python developer. Thank you, Gina Acosta Gutiérrez, for this insightful roadmap! 🌟 #Python #DataScience #Programming #WebDevelopment #Automation #Testing #Learning

Python roadmap💫

Learning how to leverage Dask has paid off tenfold over the past few years. Highly recommend to anyone working with large datasets. I’d also add NVIDIA RAPIDs (cupy, cudf, cuml, Dask-CUDA) to leverage GPU compute.

Do you know the level of your Python skills and what to learn next? Here is a short guide to help you judge where you stand: 1. 𝗕𝗮𝘀𝗶𝗰 𝗟𝗲𝘃𝗲𝗹 • Can you write simple scripts and understand basic syntax? • Are you comfortable with the basic data types (lists, dictionaries, sets, tuples)? • Can you read/write files and handle exceptions? • Do you know common data libraries like pandas and NumPy? • Can you use basic control flow (if statements, for/while loops)?   2. 𝗜𝗻𝘁𝗲𝗿𝗺𝗲𝗱𝗶𝗮𝘁𝗲 𝗟𝗲𝘃𝗲𝗹 • Can you use list comprehensions and lambda functions? • Are you proficient with pandas for data manipulation and Matplotlib/Seaborn for visualization? • Do you understand object-oriented programming (OOP) concepts? • Can you write robust functions and work with modules/packages? • Can you use virtual environments and write automated tests? 3. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗟𝗲𝘃𝗲𝗹 • Can you optimize code for performance and use decorators? • Do you understand concurrency and parallelism (threads, asyncio, multiprocessing)? • Can you handle big data with tools like Dask or Spark? • Can you write and maintain complex, large-scale applications? • Are you familiar with best practices for testing, debugging, and deploying code? Knowing your level helps you set the right goals and focus your learning efforts. Where do you see yourself on this scale, and what’s your next step to level up? ---------------- ♻️ Share if you find this post useful ➕ Follow for more daily insights on how to grow your career in the data field #dataanalytics #datascience #python #skilldevelopment #careergrowth  

MY DATA SCIENCE JOURNEY (DAY 5) #100dayswithdatascience Today let look at python problem solving Using Python language. *What is Python?* Python is a high-level, interpreted programming language that is widely used for various purposes such as web development, data science, automation, game development, and more. *Key Features of Python:* 1. Easy to learn and use 2. High-level syntax 3. Dynamic typing 4. Object-oriented programming 5. Large standard library 6. Cross-platform compatibility 7. Open-source *Real-World Applications of Python:* 1. Web Development (Django, Flask) 2. Data Science and Analytics (NumPy, pandas, Matplotlib) 3. Automation (PyAutoGUI, Robot Framework) 4. Game Development (Pygame, Pyglet) 5. Network Security (Scapy, Nmap) 6. Education *Problem-Solving with Python:* 1. Data Analysis: Use pandas and NumPy to analyze and visualize complex data. 2. Automation: Automate repetitive tasks using PyAutoGUI and Robot Framework. 3. Web Scraping: Extract data from websites using BeautifulSoup and Scrapy. 4. Machine Learning: Build predictive models using Scikit-learn and TensorFlow. 5. Network Security: Identify vulnerabilities using Scapy and Nmap. *Benefits of Using Python:* 1. Rapid development 2. Flexible and adaptable 3. Large community 4. Extensive libraries 5. Easy integration with other languages *Join the Conversation:* Share your experiences with Python! What problems have you solved using Python? What resources do you recommend #100dayswithdatascience #20dayslinkedinchallange #Python #Programming #ProblemSolving #DataScience #Automation

𝗣𝘆𝘁𝗵𝗼𝗻 𝗠𝗮𝘀𝘁𝗲𝗿𝘆 𝗥𝗼𝗮𝗱𝗺𝗮𝗽: 𝗙𝗿𝗼𝗺 𝗕𝗮𝘀𝗶𝗰𝘀 𝘁𝗼 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 Unlock the power of Python with this concise roadmap, catering to both beginners and seasoned developers. 𝗕𝗮𝘀𝗶𝗰𝘀: 𝟭. 𝗦𝘆𝗻𝘁𝗮𝘅 𝗮𝗻𝗱 𝗩𝗮𝗿𝗶𝗮𝗯𝗹𝗲𝘀: - Master Python's basic syntax and variable handling. 𝟮. 𝗗𝗮𝘁𝗮 𝗧𝘆𝗽𝗲𝘀 𝗮𝗻𝗱 𝗖𝗼𝗻𝗱𝗶𝘁𝗶𝗼𝗻𝘀: - Explore data types, type conversion, and conditional statements. 𝟯. 𝗟𝗼𝗼𝗽𝘀 𝗮𝗻𝗱 𝗘𝘅𝗰𝗲𝗽𝘁𝗶𝗼𝗻𝘀: - Efficiently use 'for' and 'while' loops, and handle exceptions. 𝟰. 𝗙𝘂𝗻𝗰𝘁𝗶𝗼𝗻𝘀: - Grasp the concept of functions for modular coding. 𝗗𝗮𝘁𝗮 𝗦𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲𝘀: 𝟱. 𝗟𝗶𝘀𝘁𝘀, 𝗧𝘂𝗽𝗹𝗲𝘀, 𝗦𝗲𝘁𝘀, 𝗗𝗶𝗰𝘁𝗶𝗼𝗻𝗮𝗿𝗶𝗲𝘀: - Understand Python's versatile data structures. 𝗔𝗱𝘃𝗮𝗻𝗰𝗲𝗱 𝗖𝗼𝗻𝗰𝗲𝗽𝘁𝘀: 𝟲. 𝗢𝗢𝗣, 𝗢𝗦 𝗖𝗼𝗺𝗽𝗮𝘁𝗶𝗯𝗶𝗹𝗶𝘁𝘆, 𝗮𝗻𝗱 𝗘𝗱𝗶𝘁𝗼𝗿𝘀: - Delve into OOP, Python's compatibility with various OS, and popular code editors. 𝟳. 𝗦𝗰𝗿𝗶𝗽𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: - Learn Python scripting for task automation. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝗮𝗻𝗱 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀: 𝟴. 𝗗𝗮𝘁𝗮 𝗪𝗿𝗮𝗻𝗴𝗹𝗶𝗻𝗴, 𝗗𝗦𝗔, 𝗮𝗻𝗱 𝗩𝗲𝗿𝘀𝗶𝗼𝗻 𝗖𝗼𝗻𝘁𝗿𝗼𝗹: - Prepare and analyze data, delve into DSA, and use Git for version control. 𝟵. 𝗪𝗲𝗯 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀 𝗮𝗻𝗱 𝗜𝗗𝗘𝘀: - Familiarize yourself with web frameworks, and explore IDEs like PyCharm and Jupyter. 𝟭𝟬. 𝗧𝗲𝘀𝘁𝗶𝗻𝗴 𝗮𝗻𝗱 𝗣𝗮𝗰𝗸𝗮𝗴𝗲 𝗠𝗮𝗻𝗮𝗴𝗲𝗿𝘀: - Discover testing frameworks (Beautiful Soup, Scrapy, URLLIB) and package managers (pip, conda). #python #engineering #softwareengineering #devops #computerscience

𝗪𝗵𝘆 𝗣𝘆𝘁𝗵𝗼𝗻 𝗶𝘀 𝗮 𝗚𝗮𝗺𝗲-𝗖𝗵𝗮𝗻𝗴𝗲𝗿 𝗶𝗻 𝗧𝗲𝗰𝗵 🚀 Python has become one of the most versatile and powerful programming languages in the tech world. Whether you're developing web applications, diving into data science, or automating tasks, Python has got you covered. 🔹 𝗪𝗵𝘆 𝗣𝘆𝘁𝗵𝗼𝗻? 𝗘𝗮𝘀𝘆 𝘁𝗼 𝗟𝗲𝗮𝗿𝗻: Python's simple syntax makes it accessible for beginners and efficient for experienced developers. 𝗩𝗲𝗿𝘀𝗮𝘁𝗶𝗹𝗲: Used in web development, data analysis, machine learning, automation, and more. 𝗦𝘁𝗿𝗼𝗻𝗴 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆 𝗦𝘂𝗽𝗽𝗼𝗿𝘁: A vast community of developers and extensive libraries to help you solve any problem. 🔹 𝗞𝗲𝘆 𝗙𝗲𝗮𝘁𝘂𝗿𝗲𝘀: 𝗥𝗶𝗰𝗵 𝗟𝗶𝗯𝗿𝗮𝗿𝗶𝗲𝘀 𝗮𝗻𝗱 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸𝘀: Leverage tools like Django for web development, Pandas for data analysis, and TensorFlow for machine learning. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗶𝗼𝗻: Simplify repetitive tasks and boost productivity with Python scripts. 𝗗𝗮𝘁𝗮 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻: Create stunning visualizations with libraries like Matplotlib and Seaborn. 🔹 𝗚𝗲𝘁𝘁𝗶𝗻𝗴 𝗦𝘁𝗮𝗿𝘁𝗲𝗱: 𝗟𝗲𝗮𝗿𝗻 𝘁𝗵𝗲 𝗕𝗮𝘀𝗶𝗰𝘀: Focus on understanding Python syntax and core programming concepts. 𝗕𝘂𝗶𝗹𝗱 𝗣𝗿𝗼𝗷𝗲𝗰𝘁𝘀: Apply your knowledge by working on real-world projects. 𝗝𝗼𝗶𝗻 𝘁𝗵𝗲 𝗖𝗼𝗺𝗺𝘂𝗻𝗶𝘁𝘆: Engage with other Python enthusiasts on forums and social media. Python is not just a language; it's a gateway to endless possibilities in the tech world. Let's embrace the future of programming with Python! 🌟 #Python #Programming #WebDevelopment #DataScience #Automation #MachineLearning