Welcome to my personal data science playground! Here, I’m sharpening my skills by diving into key topics like data processing, cleaning, visualization, stats, and machine learning. The plan? Practice every single day 💪, learn step by step, and turn complex data puzzles into fun challenges.
# Python-Exercises
🌟 pandas-practice🐼
— Pandas magic for cleaning & handling data
- Getting comfy with DataFrames 📄
- Groupby tricks & time series 🕰️
- Complex stuff & performance hacks ⚡
🌟 numpy-practice🧮
— NumPy power for arrays & math
- Array basics & broadcasting 🔊
- Matrix moves & computations ➗
- Custom types & speed boosts 🚀
🌟 matplotlib-practice📈
— Painting pictures with Matplotlib
- Simple line & scatter plots 🎨
- Fancy layouts & multiple plots 🖼️
- Animations & advanced visuals 🎥
🌟 seaborn-practice🌊
— Beautiful stats visuals with Seaborn
- Boxplots, bars & basics 📊
- Mix & match relationship plots 🔄
- Multivariate & custom styles 🎨
🌟 scikit-learn-practice🤖
— Machine learning fun with Scikit-learn
- Basic regression & classification 📉📈
- Model tuning & pipelines 🔧
- Ensembles, reduction & clustering 🧩
🌟 statistics
— Stats fundamentals & hypothesis testing 🎲
🌟 data-cleaning
— Data tidying and prep 🧹
🌟 projects
— Mini-projects that tie it all together 🎯
🛠️Environment Setup 🛠️ Make sure you have Python 3.8 or higher. To get all the goodies, run:
bash
pip install numpy pandas matplotlib seaborn scikit-learn scipy statsmodels jupyter
🎮How to Use This Repository 🎮
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Pick a topic and difficulty Choose what you want to practice (Pandas, NumPy, etc.) and your level (basic, intermediate, advanced).
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Run the exercises Most are Python scripts (.py) or Jupyter notebooks (.ipynb). Run scripts normally or jump into notebooks for hands-on fun.
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Explore and experiment Each exercise has comments to guide you. Don’t be shy — tweak the code, break it, fix it, and learn!
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Try the projects When you’re ready, tackle mini-projects in the projects/ folder that combine your skills in real-world-like challenges.
| Icon | Info |
|---|---|
| 👩💻 | Gina |
| 📧 | [[email protected]] |