Programming Resources | Python | Javascript | Artificial Intelligence Updates | Computer Science Courses | AI Books
54.3K subscribers
880 photos
1 video
4 files
334 links
Everything about programming for beginners
* Python programming
* Java programming
* App development
* Machine Learning
* Data Science

Managed by: @love_data
Download Telegram
Essential Programming Languages to Learn Data Science ๐Ÿ‘‡๐Ÿ‘‡

1. Python: Python is one of the most popular programming languages for data science due to its simplicity, versatility, and extensive library support (such as NumPy, Pandas, and Scikit-learn).

2. R: R is another popular language for data science, particularly in academia and research settings. It has powerful statistical analysis capabilities and a wide range of packages for data manipulation and visualization.

3. SQL: SQL (Structured Query Language) is essential for working with databases, which are a critical component of data science projects. Knowledge of SQL is necessary for querying and manipulating data stored in relational databases.

4. Java: Java is a versatile language that is widely used in enterprise applications and big data processing frameworks like Apache Hadoop and Apache Spark. Knowledge of Java can be beneficial for working with large-scale data processing systems.

5. Scala: Scala is a functional programming language that is often used in conjunction with Apache Spark for distributed data processing. Knowledge of Scala can be valuable for building high-performance data processing applications.

6. Julia: Julia is a high-performance language specifically designed for scientific computing and data analysis. It is gaining popularity in the data science community due to its speed and ease of use for numerical computations.

7. MATLAB: MATLAB is a proprietary programming language commonly used in engineering and scientific research for data analysis, visualization, and modeling. It is particularly useful for signal processing and image analysis tasks.

Free Resources to master data analytics concepts ๐Ÿ‘‡๐Ÿ‘‡

Data Analysis with R

Intro to Data Science

Practical Python Programming

SQL for Data Analysis

Java Essential Concepts

Machine Learning with Python

Data Science Project Ideas

Learning SQL FREE Book

Join @free4unow_backup for more free resources.

ENJOY LEARNING๐Ÿ‘๐Ÿ‘
โค1
### Learn GitHub Easily ๐Ÿคฉ

Here's all you need to get started ๐Ÿ™Œ

1. Introduction to GitHub
- What is GitHub?
- Differences between Git and GitHub
- Creating a GitHub account

2. Creating a Repository
- Setting up a new repository
- Understanding repository settings (public vs. private)
- Adding a README file

3. Cloning a Repository
- Cloning repositories to your local machine
- Understanding SSH vs. HTTPS cloning

4. Managing Repositories
- Navigating the GitHub interface
- Viewing and editing files
- Understanding branches in GitHub

5. Committing Changes
- Making changes locally and pushing to GitHub
- Committing changes with meaningful messages
- Synchronizing changes with git pull and git push

6. Branching and Merging
- Creating branches on GitHub
- Comparing branches
- Merging branches through pull requests

7. Pull Requests (PRs)
- Creating a pull request
- Reviewing pull requests
- Merging pull requests and resolving conflicts

8. Issues and Project Management
- Creating and managing issues
- Using labels, milestones, and assignees
- Introduction to GitHub Projects for task management

9. Collaboration Features
- Using GitHub Discussions
- Code reviews and comments
- Mentioning team members and using notifications

10. GitHub Actions
- Introduction to CI/CD with GitHub Actions
- Creating simple workflows
- Using actions from the GitHub Marketplace

11. GitHub Pages
- Setting up GitHub Pages for static sites
- Using Jekyll for site generation

12. Managing Releases
- Creating and managing releases
- Understanding versioning (tags)

13. Security Features
- Setting up branch protections
- Enabling two-factor authentication (2FA)
- Managing collaborator permissions

14. Exploring GitHub API
- Overview of GitHub API
- Making API requests for repositories and issues

15. GitHub CLI
- Introduction to GitHub Command Line Interface
- Common commands and usage

16. Best Practices
- Writing effective commit messages
- Structuring your repositories
- Managing large projects and dependencies

17. Resources for Continued Learning
- GitHub documentation and guides
- Online tutorials and courses
- Community forums and events
โค8
15 Best Project Ideas for Backend Development : ๐Ÿ› ๏ธ๐ŸŒ

๐Ÿš€ Beginner Level :

1. ๐Ÿ“ฆ RESTful API for a To-Do App
2. ๐Ÿ“ Contact Form Backend
3. ๐Ÿ—‚๏ธ File Upload Service
4. ๐Ÿ“ฌ Email Subscription Service
5. ๐Ÿงพ Notes App Backend

๐ŸŒŸ Intermediate Level :
6. ๐Ÿ›’ E-commerce Backend with Cart & Orders
7. ๐Ÿ” Authentication System (JWT/OAuth)
8. ๐Ÿง‘โ€๐Ÿคโ€๐Ÿง‘ User Management API
9. ๐Ÿงพ Invoice Generator API
10. ๐Ÿง  Blog CMS Backend

๐ŸŒŒ Advanced Level :
11. ๐Ÿง  AI Chatbot Backend Integration
12. ๐Ÿ“ˆ Real-Time Stock Tracker using WebSockets
13. ๐ŸŽง Music Streaming Server
14. ๐Ÿ’ฌ Real-Time Chat Server
15. โš™๏ธ Microservices Architecture for Large Apps

Here you can find more Coding Project Ideas: https://whatsapp.com/channel/0029VazkxJ62UPB7OQhBE502

Web Development Jobs: https://whatsapp.com/channel/0029Vb1raTiDjiOias5ARu2p

JavaScript Resources: https://whatsapp.com/channel/0029VavR9OxLtOjJTXrZNi32

ENJOY LEARNING ๐Ÿ‘๐Ÿ‘
โค4
How to master Python from scratch๐Ÿš€

1. Setup and Basics ๐Ÿ
   - Install Python ๐Ÿ–ฅ๏ธ: Download Python and set it up.
   - Hello, World! ๐ŸŒ: Write your first Hello World program.

2. Basic Syntax ๐Ÿ“œ
   - Variables and Data Types ๐Ÿ“Š: Learn about strings, integers, floats, and booleans.
   - Control Structures ๐Ÿ”„: Understand if-else statements, for loops, and while loops.
   - Functions ๐Ÿ› ๏ธ: Write reusable blocks of code.

3. Data Structures ๐Ÿ“‚
   - Lists ๐Ÿ“‹: Manage collections of items.
   - Dictionaries ๐Ÿ“–: Store key-value pairs.
   - Tuples ๐Ÿ“ฆ: Work with immutable sequences.
   - Sets ๐Ÿ”ข: Handle collections of unique items.

4. Modules and Packages ๐Ÿ“ฆ
   - Standard Library ๐Ÿ“š: Explore built-in modules.
   - Third-Party Packages ๐ŸŒ: Install and use packages with pip.

5. File Handling ๐Ÿ“
   - Read and Write Files ๐Ÿ“
   - CSV and JSON ๐Ÿ“‘

6. Object-Oriented Programming ๐Ÿงฉ
   - Classes and Objects ๐Ÿ›๏ธ
   - Inheritance and Polymorphism ๐Ÿ‘จโ€๐Ÿ‘ฉโ€๐Ÿ‘ง

7. Web Development ๐ŸŒ
   - Flask ๐Ÿผ: Start with a micro web framework.
   - Django ๐Ÿฆ„: Dive into a full-fledged web framework.

8. Data Science and Machine Learning ๐Ÿง 
   - NumPy ๐Ÿ“Š: Numerical operations.
   - Pandas ๐Ÿผ: Data manipulation and analysis.
   - Matplotlib ๐Ÿ“ˆ and Seaborn ๐Ÿ“Š: Data visualization.
   - Scikit-learn ๐Ÿค–: Machine learning.

9. Automation and Scripting ๐Ÿค–
   - Automate Tasks ๐Ÿ› ๏ธ: Use Python to automate repetitive tasks.
   - APIs ๐ŸŒ: Interact with web services.

10. Testing and Debugging ๐Ÿž
    - Unit Testing ๐Ÿงช: Write tests for your code.
    - Debugging ๐Ÿ”: Learn to debug efficiently.

11. Advanced Topics ๐Ÿš€
    - Concurrency and Parallelism ๐Ÿ•’
    - Decorators ๐ŸŒ€ and Generators โš™๏ธ
    - Web Scraping ๐Ÿ•ธ๏ธ: Extract data from websites using BeautifulSoup and Scrapy.

12. Practice Projects ๐Ÿ’ก
    - Calculator ๐Ÿงฎ
    - To-Do List App ๐Ÿ“‹
    - Weather App โ˜€๏ธ
    - Personal Blog ๐Ÿ“

13. Community and Collaboration ๐Ÿค
    - Contribute to Open Source ๐ŸŒ
    - Join Coding Communities ๐Ÿ’ฌ
    - Participate in Hackathons ๐Ÿ†

14. Keep Learning and Improving ๐Ÿ“ˆ
    - Read Books ๐Ÿ“–: Like "Automate the Boring Stuff with Python".
    - Watch Tutorials ๐ŸŽฅ: Follow video courses and tutorials.
    - Solve Challenges ๐Ÿงฉ: On platforms like LeetCode, HackerRank, and CodeWars.

15. Teach and Share Knowledge ๐Ÿ“ข
    - Write Blogs โœ๏ธ
    - Create Video Tutorials ๐Ÿ“น
    - Mentor Others ๐Ÿ‘จโ€๐Ÿซ

I have curated the best interview resources to crack Python Interviews ๐Ÿ‘‡๐Ÿ‘‡
https://topmate.io/coding/898340

Hope you'll like it

Like this post if you need more resources like this ๐Ÿ‘โค๏ธ
โค2
Tools & Tech Every Developer Should Know โš’๏ธ๐Ÿ‘จ๐Ÿปโ€๐Ÿ’ป

โฏ VS Code โžŸ Lightweight, Powerful Code Editor
โฏ Postman โžŸ API Testing, Debugging
โฏ Docker โžŸ App Containerization
โฏ Kubernetes โžŸ Scaling & Orchestrating Containers
โฏ Git โžŸ Version Control, Team Collaboration
โฏ GitHub/GitLab โžŸ Hosting Code Repos, CI/CD
โฏ Figma โžŸ UI/UX Design, Prototyping
โฏ Jira โžŸ Agile Project Management
โฏ Slack/Discord โžŸ Team Communication
โฏ Notion โžŸ Docs, Notes, Knowledge Base
โฏ Trello โžŸ Task Management
โฏ Zsh + Oh My Zsh โžŸ Advanced Terminal Experience
โฏ Linux Terminal โžŸ DevOps, Shell Scripting
โฏ Homebrew (macOS) โžŸ Package Manager
โฏ Anaconda โžŸ Python & Data Science Environments
โฏ Pandas โžŸ Data Manipulation in Python
โฏ NumPy โžŸ Numerical Computation
โฏ Jupyter Notebooks โžŸ Interactive Python Coding
โฏ Chrome DevTools โžŸ Web Debugging
โฏ Firebase โžŸ Backend as a Service
โฏ Heroku โžŸ Easy App Deployment
โฏ Netlify โžŸ Deploy Frontend Sites
โฏ Vercel โžŸ Full-Stack Deployment for Next.js
โฏ Nginx โžŸ Web Server, Load Balancer
โฏ MongoDB โžŸ NoSQL Database
โฏ PostgreSQL โžŸ Advanced Relational Database
โฏ Redis โžŸ Caching & Fast Storage
โฏ Elasticsearch โžŸ Search & Analytics Engine
โฏ Sentry โžŸ Error Monitoring
โฏ Jenkins โžŸ Automate CI/CD Pipelines
โฏ AWS/GCP/Azure โžŸ Cloud Services & Deployment
โฏ Swagger โžŸ API Documentation
โฏ SASS/SCSS โžŸ CSS Preprocessors
โฏ Tailwind CSS โžŸ Utility-First CSS Framework

React โค๏ธ if you found this helpful

Coding Jobs: https://whatsapp.com/channel/0029VatL9a22kNFtPtLApJ2L
โค7