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Sharing NumPy Notes Today! React to this post to let me know you're interested! βœ¨πŸ˜‰
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Anonymous Poll
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Yes ✌️
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NumPy Notes.zip
5.2 MB
NumPy Notes
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Free Notes.zip
278.6 MB
πŸ“Œ List of ResourcesπŸ‘‡:
1. DATA SCIENCE INTERVIEW PREPARATION - PANDAS
2. DATA SCIENCE INTERVIEW PREPARATION - PYTHON
3. 100 Questions to Master DSA ✨
4. Data Structure and Algorithms Handwritten Notes
5. Data Structures and Algorithms
6. Data Structures
7. DSA_Book
8. Java_Complete_Notes
9. ML Cheatsheet
10. Python-and-ML-MCQ
11. SQL Handwritten Notes
12. AI Handwritten Notes
13. ML Handwritten Notes
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Browser Project Source Code.zip
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30-Day Python Learning Roadmap for Data Analysis πŸ˜„πŸ‘‡

Days 1-5: Get Started with Python
Day 1: Install Python and a code editor (try Anaconda or Jupyter Notebook).
Days 2-5: Learn the basics of Python:
🟒 Understand variables and data types.
πŸ”΅ Practice basic operations (addition, subtraction, etc.).

Days 6-10: Control Flow and Functions
Days 6-8: Learn about control flow:
βšͺ️ Use if statements to make decisions.
🟠 Understand loops (for and while) to repeat tasks.
Days 9-10: Discover functions:
🟑 Learn how to create and use functions.
🟣 Understand modules and how to import them.

Days 11-15: Data Structures
Days 11-12: Explore these data structures:
🟀 Lists: ordered collections.
πŸ”΄ Tuples: immutable ordered collections.
🟑 Dictionaries: key-value pairs.
Days 13-15: Study:
🟒 Sets: unique collections of items.
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Days 16-20: Libraries for Data Analysis
Days 16-17: Get to know NumPy:
🌟 Perform numerical operations efficiently.
Days 18-19: Dive into Pandas:
πŸ“Š Learn to manipulate and analyze data.
Day 20: Start with basic data visualization using Matplotlib:
πŸ“ˆ Create simple charts and plots.

Days 21-25: Data Cleaning and Analysis
Days 21-22: Practice data cleaning:
🧹 Use Pandas to prepare your data for analysis.
Days 23-25: Explore data analysis techniques:
πŸ” Learn about exploratory data analysis (EDA).

Days 26-30: Advanced Topics
Days 26-27: Learn about data visualization with Seaborn:
🎨 Create more advanced visualizations.
Days 28-29: Get an introduction to machine learning with Scikit-Learn:
πŸ€– Understand basic concepts and algorithms.
Day 30: Work on a small data analysis project:
πŸ’» Apply everything you’ve learned!

Additional Resources
πŸ“š Use Kaggle to find datasets for your projects.
πŸ§‘β€πŸ’» Check out GeeksforGeeks to practice coding problems.


ENJOY LEARNING πŸ‘βœŒοΈ

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Forwarded from Pycode Hubb
Complete Python guide + 99 Projects πŸ”₯βœ…
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