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๐—ง๐—ต๐—ผ๐˜€๐—ฒ ๐˜„๐—ต๐—ผ ๐˜„๐—ฎ๐—ป๐˜ ๐—ฟ๐—ฒ๐—ณ๐—ฒ๐—ฟ๐—ฟ๐—ฎ๐—น๐˜€ ๐—ฎ๐—ป๐—ฑ ๐—๐—ผ๐—ฏ๐˜€ ๐—ฎ๐—ป๐—ฑ ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐—ป๐˜€๐—ต๐—ถ๐—ฝ๐˜€ ๐—ผ๐—ฝ๐—ฝ๐—ผ๐—ฟ๐˜๐˜‚๐—ป๐—ถ๐˜๐—ถ๐—ฒ๐˜€ ๐—ณ๐—ฟ๐—ผ๐—บ ๐—ง๐—ผ๐—ฝ ๐—ฃ๐—ฟ๐—ผ๐—ฑ๐˜‚๐—ฐ๐˜ ๐—•๐—ฎ๐˜€๐—ฒ๐—ฑ, ๐—ฆ๐—ฒ๐—ฟ๐˜ƒ๐—ถ๐—ฐ๐—ฒ ๐—•๐—ฎ๐˜€๐—ฒ๐—ฑ ๐—ฎ๐—ป๐—ฑ ๐—ฆ๐˜๐—ฎ๐—ฟ๐˜ ๐˜‚๐—ฝ  ๐—–๐—ผ๐—บ๐—ฝ๐—ฎ๐—ป๐—ถ๐—ฒ๐˜€ ๐—น๐—ถ๐—ธ๐—ฒ ๐—”๐—บ๐—ฎ๐˜‡๐—ผ๐—ป, ๐—š๐—ผ๐—ผ๐—ด๐—น๐—ฒ,  ๐—”๐—ฝ๐—ฝ๐—น๐—ฒ, ๐— ๐—ถ๐—ฐ๐—ฟ๐—ผ๐˜€๐—ผ๐—ณ๐˜, ๐—œ๐—•๐— , ๐—ง๐—–๐—ฆ, ๐—–๐—ผ๐—ด๐—ป๐—ถ๐˜‡๐—ฎ๐—ป๐˜, ๐—ช๐—ถ๐—ฝ๐—ฟ๐—ผ, ๐—–๐—ง๐—ฆ, ๐—š๐—ผ๐—น๐—ฑ๐—บ๐—ฎ๐—ป ๐—ฆ๐—ฎ๐—ฐ๐—ต๐˜€, ๐—ข๐—น๐—ฎ, ๐—จ๐—ฏ๐—ฒ๐—ฟ, ๐—ญ๐—ผ๐—บ๐—ฎ๐˜๐—ผ, ๐—ฆ๐˜„๐—ถ๐—ด๐—ด๐˜†, ๐˜‚๐—ฝ๐—š๐—ฟ๐—ฎ๐—ฑ, ๐—–๐˜‚๐—ฟ๐—ฒ ๐—™๐—ถ๐˜, ๐—›๐—ฎ๐—ฐ๐—ธ๐—ฒ๐—ฟ๐—ฟ๐—ฎ๐—ป๐—ธ, ๐—š๐—ฒ๐—ฒ๐—ธ๐˜€๐—ณ๐—ผ๐—ฟ๐—ด๐—ฒ๐—ฒ๐—ธ๐˜€ ๐—ฎ๐—ป๐—ฑ ๐—บ๐—ฎ๐—ป๐˜† ๐—บ๐—ผ๐—ฟ๐—ฒ, ๐—ฐ๐—ฎ๐—ป ๐—ท๐—ผ๐—ถ๐—ป ๐˜๐—ต๐—ฒ ๐—ฏ๐—ฒ๐—น๐—ผ๐˜„ network.

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๐Ÿš€ Python Roadmap for Data Analytics ๐Ÿ๐Ÿ“Š๐Ÿ”ฅ

๐Ÿง  STEP 1: Learn Python Basics
โœ” Variables & Data Types
โœ” Loops & Functions
โœ” Lists, Tuples & Dictionaries
โœ” File Handling
โœ” Exception Handling

๐Ÿ›  Tools to Learn:
โœ” Jupyter Notebook
โœ” Visual Studio Code

๐Ÿ“Š STEP 2: Learn Data Handling
โœ” Reading CSV & Excel Files
โœ” Data Cleaning
โœ” Handling Missing Values
โœ” Data Transformation

๐Ÿ›  Libraries to Learn:
โœ” Pandas
โœ” NumPy

๐Ÿ“ˆ STEP 3: Learn Data Visualization
โœ” Line Charts
โœ” Bar Charts
โœ” Pie Charts
โœ” Heatmaps
โœ” Interactive Dashboards

๐Ÿ›  Visualization Libraries:
โœ” Matplotlib
โœ” Seaborn
โœ” Plotly

๐Ÿง  STEP 4: Learn Statistics Basics
โœ” Mean, Median & Mode
โœ” Probability
โœ” Correlation
โœ” Hypothesis Testing
โœ” A/B Testing

โšก STEP 5: Learn SQL with Python
โœ” Database Connections
โœ” SQL Queries
โœ” Fetching Data
โœ” Data Integration

๐Ÿ›  Libraries to Learn:
โœ” sqlite3
โœ” SQLAlchemy
โœ” PyMySQL

๐Ÿค– STEP 6: Learn Basic Machine Learning
โœ” Regression
โœ” Classification
โœ” Clustering
โœ” Model Evaluation

๐Ÿ›  Frameworks to Learn:
โœ” Scikit-learn
โœ” XGBoost

๐Ÿ“‚ STEP 7: Learn Automation & Reporting
โœ” Automating Reports
โœ” Excel Automation
โœ” API Data Collection
โœ” Scheduling Tasks

๐Ÿ›  Libraries to Learn:
โœ” openpyxl
โœ” requests
โœ” schedule

๐Ÿ”ฅ STEP 8: Build Real Projects
โœ” Sales Data Analysis
โœ” HR Analytics Dashboard
โœ” Customer Churn Analysis
โœ” Financial Analytics
โœ” Netflix Dataset Analysis


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1๏ธโƒฃ Jobs and Internships Updates
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25 Github Repositories Every Python Developer Should Know

1. Python
The official repository of Python's source code. Dive into it to explore Python's internals or contribute to the language's development.

2. Awesome Python
A curated list of awesome Python frameworks, libraries, software, and resources. A perfect starting point for any Python developer.

3. Requests
Simplifies HTTP requests in Python. A must-have library for working with APIs and web scraping.

4. Flask
A lightweight web framework that is simple to use yet highly flexible, ideal for small to medium-sized applications.

5. Django
A high-level web framework that encourages rapid development and clean, pragmatic design for building robust web applications.

6. FastAPI
A modern web framework for building APIs with Python. Known for its speed and automatic OpenAPI documentation.

7. Pandas
Provides powerful tools for data manipulation and analysis, including support for data frames.

8. NumPy
The go-to library for numerical computations. Itโ€™s the backbone of Pythonโ€™s scientific computing stack.

9. Matplotlib
A plotting library for creating static, animated, and interactive visualizations in Python.

10. Seaborn
Builds on Matplotlib and simplifies creating beautiful and informative statistical graphics.

11. Scikit-learn
A machine learning library featuring various classification, regression, and clustering algorithms.

12. TensorFlow
A powerful framework for machine learning and deep learning, supported by Google.

13. PyTorch
Another leading machine learning framework, known for its flexibility and dynamic computation graph.

14. BeautifulSoup
Simplifies web scraping by parsing HTML and XML documents.

15. Scrapy
An advanced web scraping and web crawling framework.

16. Streamlit
Makes it easy to build and share data apps using pure Python. Great for data scientists.

17. Celery
A distributed task queue library for running asynchronous jobs.

18. SQLAlchemy
A powerful ORM (Object-Relational Mapping) tool for managing database operations in Python.

19. Pytest
A robust testing framework for writing simple and scalable test cases.

20. Black
An uncompromising code formatter for Python. Makes your code consistent and clean.

21. Bokeh
For creating interactive visualizations in modern web browsers.

22. Plotly
Another library for creating interactive visualizations but with more customization options.

23. OpenCV
The go-to library for computer vision tasks like image processing and object detection.

24. Pillow
A friendly fork of PIL (Python Imaging Library), used for image processing tasks.

25. Rich
A Python library for beautiful terminal outputs with rich text, progress bars, and more.

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https://t.me/jobsandinternshipsindia

Data Structures and Algorithms:

https://t.me/datastructuresandalgoofficial

Web Development and Web Design:

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Big Data:

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Machine Learning:

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Cloud Computing:

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Deep Learning:

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Android Development:

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App Development:

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Ethical Hacking

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Digital Marketing

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