Python Roadmap: 🗺
📂 Basics
∟📂 Data Types & Variables
∟📂 Operators & Expressions
∟📂 Control Flow (if, loops)
∟📂 Functions & Modules
∟📂 File Handling
∟📂 OOP (Classes & Objects)
∟📂 Exception Handling
∟📂 Advanced Topics (Decorators, Generators)
∟📂 Libraries (NumPy, Pandas, Matplotlib)
∟📂 Web Scraping / API Integration
∟📂 Frameworks (Flask/Django)
∟📂 Automation & Scripting
∟📂 Projects
∟ ✅ Apply For Job
Like if you need a detailed explanation step-by-step ❤️
📂 Basics
∟📂 Data Types & Variables
∟📂 Operators & Expressions
∟📂 Control Flow (if, loops)
∟📂 Functions & Modules
∟📂 File Handling
∟📂 OOP (Classes & Objects)
∟📂 Exception Handling
∟📂 Advanced Topics (Decorators, Generators)
∟📂 Libraries (NumPy, Pandas, Matplotlib)
∟📂 Web Scraping / API Integration
∟📂 Frameworks (Flask/Django)
∟📂 Automation & Scripting
∟📂 Projects
∟ ✅ Apply For Job
Like if you need a detailed explanation step-by-step ❤️
👍15
_Operating System RoadMap
|
|-- Kernel
| |-- Memory Management
| | |-- Paging
| | |-- Segmentation
| | |-- Virtual Memory
| |
| |-- Process Management
| | |-- Process Scheduling
| | |-- Inter-Process Communication (IPC)
| | |-- Threads
| |
| |-- File System
| | |-- File I/O
| | |-- Directory Structure
| | |-- File Permissions
| |
| |-- Device Drivers
| | |-- Communication with Hardware
| | |-- Input/Output (I/O)
| |
| |-- System Calls
| |-- Interface to Kernel Functionality
| |-- Examples: open(), read(), write(), etc.
|
|-- Memory Management
| |-- RAM
| | |-- Stack
| | |-- Heap
| | |-- Data Segment
| | |-- Code Segment
| |
| |-- Cache
| | |-- L1, L2, L3 Caches
| |
| |-- Virtual Memory
| |-- Page Table
| |-- Page Replacement Algorithms
| |-- Swapping
|
|-- File System
| |-- File Organization
| |-- File Allocation Table (FAT)
| |-- Inodes
| |-- File Access Methods
|
|-- Networking
| |-- TCP/IP
| |-- Protocols
| |-- Network Stack
| |-- Routing
| |-- Firewalls
|
|-- Security
| |-- Authentication
| |-- Authorization
| |-- Encryption
| |-- Access Control Lists (ACL)
|
|-- Process Management
| |-- PCB (Process Control Block)
| |-- Context Switching
| |-- Deadlocks
| |-- Synchronization
| |-- Mutual Exclusion
|
|-- Device Management
| |-- I/O Buffering
| |-- Device Controllers
| |-- Interrupt Handling
| |-- DMA (Direct Memory Access)
|
|-- User Interface
| |-- Graphical User Interface (GUI)
| |-- Command Line Interface (CLI)
| |-- Windowing Systems
|
|-- Shell
| |-- Command Interpreter
| |-- Scripting
| |-- Job Control
|
|-- System Utilities
| |-- Task Manager
| |-- Disk Cleanup
| |-- System Monitor
| |-- Backup and Restore
|
|-- Boot Process
| |-- BIOS/UEFI
| |-- Boot Loader
| |-- Kernel Initialization
| |-- Init Process
|
|-- System Libraries
| |-- Standard C Library
| |-- POSIX Library
| |-- WinAPI (for Windows)
|
|-- System Calls
| |-- File System Calls
| |-- Process Control Calls
| |-- Memory Management Calls
| |-- Communication Calls
|
|-- Error Handling
| |-- Error Codes
| |-- Logging
| |-- Recovery Strategies
|
|-- Distributed Systems
| |-- Clustering
| |-- Load Balancing
| |-- Distributed File Systems
|
|-- Cloud Computing
| |-- Virtualization
| |-- Infrastructure as a Service (IaaS)
| |-- Platform as a Service (PaaS)
| |-- Software as a Service (SaaS)
|
└-- Comments
|-- // Single-line comment
└-- /* Multi-line comment */
Join for more: https://t.iss.one/programming_guide
|
|-- Kernel
| |-- Memory Management
| | |-- Paging
| | |-- Segmentation
| | |-- Virtual Memory
| |
| |-- Process Management
| | |-- Process Scheduling
| | |-- Inter-Process Communication (IPC)
| | |-- Threads
| |
| |-- File System
| | |-- File I/O
| | |-- Directory Structure
| | |-- File Permissions
| |
| |-- Device Drivers
| | |-- Communication with Hardware
| | |-- Input/Output (I/O)
| |
| |-- System Calls
| |-- Interface to Kernel Functionality
| |-- Examples: open(), read(), write(), etc.
|
|-- Memory Management
| |-- RAM
| | |-- Stack
| | |-- Heap
| | |-- Data Segment
| | |-- Code Segment
| |
| |-- Cache
| | |-- L1, L2, L3 Caches
| |
| |-- Virtual Memory
| |-- Page Table
| |-- Page Replacement Algorithms
| |-- Swapping
|
|-- File System
| |-- File Organization
| |-- File Allocation Table (FAT)
| |-- Inodes
| |-- File Access Methods
|
|-- Networking
| |-- TCP/IP
| |-- Protocols
| |-- Network Stack
| |-- Routing
| |-- Firewalls
|
|-- Security
| |-- Authentication
| |-- Authorization
| |-- Encryption
| |-- Access Control Lists (ACL)
|
|-- Process Management
| |-- PCB (Process Control Block)
| |-- Context Switching
| |-- Deadlocks
| |-- Synchronization
| |-- Mutual Exclusion
|
|-- Device Management
| |-- I/O Buffering
| |-- Device Controllers
| |-- Interrupt Handling
| |-- DMA (Direct Memory Access)
|
|-- User Interface
| |-- Graphical User Interface (GUI)
| |-- Command Line Interface (CLI)
| |-- Windowing Systems
|
|-- Shell
| |-- Command Interpreter
| |-- Scripting
| |-- Job Control
|
|-- System Utilities
| |-- Task Manager
| |-- Disk Cleanup
| |-- System Monitor
| |-- Backup and Restore
|
|-- Boot Process
| |-- BIOS/UEFI
| |-- Boot Loader
| |-- Kernel Initialization
| |-- Init Process
|
|-- System Libraries
| |-- Standard C Library
| |-- POSIX Library
| |-- WinAPI (for Windows)
|
|-- System Calls
| |-- File System Calls
| |-- Process Control Calls
| |-- Memory Management Calls
| |-- Communication Calls
|
|-- Error Handling
| |-- Error Codes
| |-- Logging
| |-- Recovery Strategies
|
|-- Distributed Systems
| |-- Clustering
| |-- Load Balancing
| |-- Distributed File Systems
|
|-- Cloud Computing
| |-- Virtualization
| |-- Infrastructure as a Service (IaaS)
| |-- Platform as a Service (PaaS)
| |-- Software as a Service (SaaS)
|
└-- Comments
|-- // Single-line comment
└-- /* Multi-line comment */
Join for more: https://t.iss.one/programming_guide
👍7❤2👌1
Frontend web development:
https://www.w3schools.com/html
https://www.w3schools.com/css
https://www.jschallenger.com
https://javascript30.com
https://t.iss.one/webdevcoursefree/110
https://t.iss.one/Programming_experts/107
Backend development:
https://learnpython.org/
https://t.iss.one/pythondevelopersindia/314
https://www.geeksforgeeks.org/java/
https://introcs.cs.princeton.edu/java/11cheatsheet/
https://docs.microsoft.com/en-us/shows/beginners-series-to-nodejs/?languages=nodejs
Database:
https://mode.com/sql-tutorial/introduction-to-sql
https://www.sqltutorial.org/wp-content/uploads/2016/04/SQL-cheat-sheet.pdf
https://books.goalkicker.com/MySQLBook/MySQLNotesForProfessionals.pdf
https://docs.oracle.com/cd/B19306_01/server.102/b14200.pdf
https://leetcode.com/problemset/database/
Cloud Computing:
https://bit.ly/3aoxt1N
https://t.iss.one/free4unow_backup/366
UI/UX:
https://www.freecodecamp.org/learn/responsive-web-design/
https://bit.ly/3r6F9xE
ENJOY LEARNING 👍👍
https://www.w3schools.com/html
https://www.w3schools.com/css
https://www.jschallenger.com
https://javascript30.com
https://t.iss.one/webdevcoursefree/110
https://t.iss.one/Programming_experts/107
Backend development:
https://learnpython.org/
https://t.iss.one/pythondevelopersindia/314
https://www.geeksforgeeks.org/java/
https://introcs.cs.princeton.edu/java/11cheatsheet/
https://docs.microsoft.com/en-us/shows/beginners-series-to-nodejs/?languages=nodejs
Database:
https://mode.com/sql-tutorial/introduction-to-sql
https://www.sqltutorial.org/wp-content/uploads/2016/04/SQL-cheat-sheet.pdf
https://books.goalkicker.com/MySQLBook/MySQLNotesForProfessionals.pdf
https://docs.oracle.com/cd/B19306_01/server.102/b14200.pdf
https://leetcode.com/problemset/database/
Cloud Computing:
https://bit.ly/3aoxt1N
https://t.iss.one/free4unow_backup/366
UI/UX:
https://www.freecodecamp.org/learn/responsive-web-design/
https://bit.ly/3r6F9xE
ENJOY LEARNING 👍👍
👍7
Types of API ✅
👍2
Python libraries for data science and Machine Learning 👇👇
1. 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.
2. Pandas: Pandas is a powerful data manipulation and analysis library that provides data structures like DataFrames and Series, making it easy to work with structured data.
3. Matplotlib: Matplotlib is a plotting library that enables the creation of various types of visualizations, such as line plots, bar charts, histograms, scatter plots, etc., to explore and communicate data effectively.
4. Scikit-learn: Scikit-learn is a machine learning library that offers a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and more. It also provides tools for model selection and evaluation.
5. TensorFlow: TensorFlow is an open-source machine learning framework developed by Google that is widely used for building deep learning models. It provides a comprehensive ecosystem of tools and libraries for developing and deploying machine learning applications.
6. Keras: Keras is a high-level neural networks API that runs on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit. It simplifies the process of building and training deep learning models by providing a user-friendly interface.
7. SciPy: SciPy is a scientific computing library that builds on top of NumPy and provides additional functionality for optimization, integration, interpolation, linear algebra, signal processing, and more.
8. Seaborn: Seaborn is a data visualization library based on Matplotlib that provides a higher-level interface for creating attractive and informative statistical graphics.
Channel credits: https://t.iss.one/datasciencefun
ENJOY LEARNING 👍👍
1. 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.
2. Pandas: Pandas is a powerful data manipulation and analysis library that provides data structures like DataFrames and Series, making it easy to work with structured data.
3. Matplotlib: Matplotlib is a plotting library that enables the creation of various types of visualizations, such as line plots, bar charts, histograms, scatter plots, etc., to explore and communicate data effectively.
4. Scikit-learn: Scikit-learn is a machine learning library that offers a wide range of algorithms for classification, regression, clustering, dimensionality reduction, and more. It also provides tools for model selection and evaluation.
5. TensorFlow: TensorFlow is an open-source machine learning framework developed by Google that is widely used for building deep learning models. It provides a comprehensive ecosystem of tools and libraries for developing and deploying machine learning applications.
6. Keras: Keras is a high-level neural networks API that runs on top of TensorFlow, Theano, or Microsoft Cognitive Toolkit. It simplifies the process of building and training deep learning models by providing a user-friendly interface.
7. SciPy: SciPy is a scientific computing library that builds on top of NumPy and provides additional functionality for optimization, integration, interpolation, linear algebra, signal processing, and more.
8. Seaborn: Seaborn is a data visualization library based on Matplotlib that provides a higher-level interface for creating attractive and informative statistical graphics.
Channel credits: https://t.iss.one/datasciencefun
ENJOY LEARNING 👍👍
👍4❤1
Here are 20 essential VS Code shortcuts for beginners:
1. Ctrl + P: Open any file quickly 📂
2. Ctrl + /: Toggle line comment 📝
3. Alt + Up/Down: Move a line up or down ↕️
4. Ctrl + Shift + K: Delete the current line ❌
5. Ctrl + B: Show/hide the sidebar 📚
6. Ctrl + Space: Trigger IntelliSense for code suggestions 💡
7. Ctrl + Shift + F: Search across files 🔍
8. Ctrl + D: Select the next occurrence of the selected text 📑
9. Ctrl + Shift + L: Select all occurrences of the current selection 🔗
10. Ctrl + Shift + P: Open the Command Palette 📜
11. Ctrl + F2: Rename all occurrences of a variable ✏️
12. Ctrl + J: Show/hide the integrated terminal 💻
13. Ctrl + `: Open a new terminal 🔧
14. Ctrl + Shift + N: Open a new window 🖼️
15. Ctrl + W: Close the current editor tab 🗂️
16. Ctrl + Shift + E: Focus on the file explorer 🗃️
17. Ctrl + Shift + G: Open the Git view 🔄
18. Ctrl + Shift + M: Open the Problems panel 🚨
19. Alt + Shift + Up/Down: Copy the line up or down 📋
20. Ctrl + Alt + Arrow keys: Split the editor window ✂️
Master these and level up your coding speed! 🚀
1. Ctrl + P: Open any file quickly 📂
2. Ctrl + /: Toggle line comment 📝
3. Alt + Up/Down: Move a line up or down ↕️
4. Ctrl + Shift + K: Delete the current line ❌
5. Ctrl + B: Show/hide the sidebar 📚
6. Ctrl + Space: Trigger IntelliSense for code suggestions 💡
7. Ctrl + Shift + F: Search across files 🔍
8. Ctrl + D: Select the next occurrence of the selected text 📑
9. Ctrl + Shift + L: Select all occurrences of the current selection 🔗
10. Ctrl + Shift + P: Open the Command Palette 📜
11. Ctrl + F2: Rename all occurrences of a variable ✏️
12. Ctrl + J: Show/hide the integrated terminal 💻
13. Ctrl + `: Open a new terminal 🔧
14. Ctrl + Shift + N: Open a new window 🖼️
15. Ctrl + W: Close the current editor tab 🗂️
16. Ctrl + Shift + E: Focus on the file explorer 🗃️
17. Ctrl + Shift + G: Open the Git view 🔄
18. Ctrl + Shift + M: Open the Problems panel 🚨
19. Alt + Shift + Up/Down: Copy the line up or down 📋
20. Ctrl + Alt + Arrow keys: Split the editor window ✂️
Master these and level up your coding speed! 🚀
👍5